The fundamental allure of quantum technology lies in a law of physics: it is impossible to observe a quantum system without changing it. In the context of communication, this means any attempt at interception is immediately detectable. This “observer effect” has turned quantum R&D into a high-stakes race, drawing intense interest not only from military commands but also from industries where data integrity is the lifeblood—most notably global banking and critical infrastructure.

In 2025, quantum technology decisively transitioned from theoretical physics into engineering standards and national security doctrines. For defense and aerospace firms, this marks the end of venture-backed experimentation and the beginning of rigid deadlines for infrastructure migration.

Historically, defense-funded “moonshots” have been the primary engines for civilian technological revolutions (the internet and GPS being the most prominent). We are seeing this pattern repeat today. As governments pour billions into national security agendas, they are effectively subsidizing the “Silicon Valley” of the 2030s, accelerating the development of dual-use technologies that will soon become standard for the general public.

The global quantum defense market has reached $2.9 billion this year, with a projected 14% CAGR, according to BIS Research. The primary driver is the urgent need to address GPS vulnerabilities and the “harvest now, decrypt later” threat to sensitive data with 20+ year secrecy requirements.

Key Areas of Dual-Use Quantum Technology

To cut through the noise, we evaluate quantum readiness using Technology Readiness Levels (TRL). This distinguishes between marketing “moonshots” and operationally deployable systems.

Secure Communications: PQC & QKD (TRL 4-8: Deployment)

By 2026, the risk of retrospective decryption has become a standard line item in defense risk assessments.

Quantum communication provides what is known as Information-Theoretic Security. Unlike current encryption, which relies on the mathematical difficulty of factoring large numbers, Quantum Key Distribution (QKD) relies on the physical properties of light.

• PQC (Post-Quantum Cryptography): NIST standards are now finalized. As of April 2026, deadlines to submit migration plans for US federal agencies are coming into force.
• QKD (Quantum Key Distribution): While China nears the 2027 launch of its “Dawn” geostationary satellite, the EU’s EuroQCI has activated its first commercial segments to secure cross-border government data.
• Defense & Banking: The ability to detect an eavesdropper instantly is why the European Union and China are building continent-scale QKD “backbones.”

Quantum Sensing & Navigation (TRL 6-7: Field Testing)

In modern electronic warfare, GPS signals are easily jammed or spoofed. Quantum sensors (specifically, atom interferometers) enable “Inertial Navigation” with zero drift. SIPRI notes that many of these technologies are reaching operational readiness. The U.S. Defense Intelligence Agency confirms that sensing and communications are approaching battle-ready maturity.

• The Solution: Quantum accelerometers and atom interferometers allow for navigation based on Earth’s gravitational and magnetic anomalies.
• Business Impact: By eliminating the “drift” inherent in classical Inertial Measurement Units (IMUs), submarines and UAVs can maintain centimeter-level precision for months without an external signal.
• The “No-Bullshit” Reality: SWaP (Size, Weight, and Power) constraints remain the bottleneck. These systems are currently viable for large platforms (frigates, strategic bombers) but are not yet miniaturized for tactical loitering munitions.

Quantum Scenario Simulations & Digital Twins (TRL 5–6: Active Piloting)

Classical Monte Carlo simulations are hitting a “complexity wall” in multi-domain operations. Quantum modeling allows mapping massive probabilistic variables directly onto quantum states to simulate outcomes in real time.

• The Direction: Defense labs, including AFRL, are piloting quantum-enhanced simulation for materials and chemistry R&D — the aim is to shrink the candidate space that classical methods must screen, and, with it, the multi-year discovery cycle. What’s not yet public is an audited, end-to-end time-saving: results so far are speed-ups on specific sub-problems, not program-level metrics.
• Dual-Use Impact:
  • Defense: Modeling “Fog of War” iterations, accounting for simultaneous kinetic, cyber, and electronic warfare disruptions.
  • Banking: Utilizing “Black Swan” market simulations to optimize capital reserves and systemic risk far beyond classical Value-at-Risk (VaR) models.

The Status: Moving from lab-bench theory to active field pilots. 

Quantum Optimization & Logistics (TRL 4-5: Pilot Stage)

Using hybrid quantum-classical algorithms to manage complex theater logistics.

• The Reality: Optimization is where defense interest is highest, and proven advantage is thinnest. Hybrid quantum-classical solvers are being piloted for theatre logistics, but published results remain problem-specific and rarely beat well-tuned classical solvers at scale.
• The Benchmark That Matters: This is precisely what DARPA’s Quantum Benchmarking Initiative exists to settle. QBI runs in three stages (A → B → C); as of late 2025 it is in Stage B, where 11 companies are assessed against a 2033 utility-scale target, with independent verification & validation (Stage C) still ahead. Until that verdict lands, headline optimization percentages are vendor claims, not validated benchmarks.

Who’s Leading — Companies & Collaborations

Quantum Computing Firms

There isn’t a single “finalist” list yet, but the industry is focused on DARPA’s Quantum Benchmarking Initiative (QBI). The following teams have advanced to Stage B—the deepest public cut to date—aiming to produce utility-scale systems by 2033:

• Atom Computing & QuEra Computing – Neutral atoms.
• IBM – Modular superconducting processors.
• IonQ & Quantinuum – Trapped-ion architectures (QCCD).
• Diraq, Quantum Motion, & Silicon Quantum Computing – Silicon-based spin qubits (CMOS/MOS).
• Nord Quantique – Superconducting with bosonic error correction.
• Photonic Inc. – Optically linked silicon spin qubits.
• Xanadu – Photonic quantum computing.

Note: Quantinuum also provides “Quantum Origin” for hardened encryption keys, while D-Wave continues to supply quantum annealers for logistics optimization to defense actors like Lockheed Martin.

Quantum Sensing & Navigation

• Q-CTRL (Australia) — Ironstone Opal, magnetic-anomaly navigation. Field-validated in air, land and maritime trials, with DARPA and DIU contracts for next-generation sensors; the Royal Australian Navy’s MV Sycamore field-tested the system for shipboard navigation in GPS-jammed and GPS-spoofed conditions. In flight trials it outperformed the best competitive alternative by over 100x. Separately, the Australian Army has used Q-CTRL’s Fire Opal on convoy routing — a problem that can take over a month of classical computation for 120 convoys. 
• SandboxAQ (US) — AQNav/MagNav. Holds a US Air Force contract for magnetic navigation; flown for 150 hours across the continental US aboard Airbus subsidiary Acubed’s flight-lab aircraft, producing continuous position fixes inside the FAA’s 2-nautical-mile en-route requirement and reaching ~550 m accuracy 64% of the time. 

Worth stating plainly: both are magnetic-map matching plus AI, not cold-atom interferometry. The two get conflated in coverage and have completely different SWaP profiles and maturity curves.

Quantum Security & QKD Specialists

• ID Quantique (Geneva, Switzerland, acquired by IonQ) provides QKD systems, quantum-safe networking, and hardware-based quantum random number generators—deployments include early QKD for Swiss elections and record-breaking fiber QKD
  
• Quintessence Labs (Australia) develops QKD systems and quantum-secure hardware for generating randomness, backed by Australian Government defense grants.
• LuxQuanta (Barcelona, Spain) — continuous-variable QKD (NOVA LQ), designed to coexist with classical optical traffic on existing fiber infrastructure. Coordinator of the EU-funded QUARTER program and, since July 2026, QUARTERNEXT — a four-year effort to mature CV-QKD into certifiable, industrial-grade systems under the EuroQCI mandate. That is a certification project, not a research demo — which is the harder and more consequential step. 
• Quside (Barcelona, Spain) — high-speed quantum random number generation. Within QUARTERNEXT, it supplies the QRNG entropy for cryptographic key generation across the consortium’s stack. Entropy is the least glamorous layer and the first thing certification bodies audit.

Public & Government-Funded Initiatives

Beyond startups, defense giants and alliances are operationalizing these technologies.

• Thales: Adopting a “pragmatic engineering” approach, Thales is deeply integrated into EuroQCI and is a key player in NIST’s PQC standardization (co-authors of the Falcon algorithm). Their focus is strictly on deployable cold-atom sensors for GPS-denied environments.
• Indra: leading NATO REPMUS exercises to validate QKD interoperability in the maritime domain, and managing the rollout of secure comms in Spain and Portugal.
• NATO: The alliance has formally adopted a “Quantum-ready” strategy. The focus is interoperability—ensuring that a quantum sensor on a British frigate can communicate with a US command center. The DIANA accelerator is now aggressively funding dual-use quantum pilots to bridge the “valley of death” between lab and battlefield.
• BAE Systems (UK): partnered with Infleqtion and QinetiQ on the UK MoD’s quantum navigation flight trials at Boscombe Down, testing an optical atomic clock and cold-atom inertial components aboard QinetiQ’s RJ100 demonstrator. In September 2025, BAE’s FAST Labs signed a three-year agreement with the US Air Force Research Laboratory on quantum sensing and networking, focused on combining multiple quantum RF sensors into arrays. Quantum Navigator
• Safran (France): the useful counterweight. Safran Electronics & Defense’s head of R&T for inertial navigation puts a possible introduction of quantum technologies more than ten years out, and the company has focused development on hemispherical resonator gyroscope (HRG) technology instead, on the grounds that quantum-based technology is not yet mature enough. When a prime with skin in the game says “not yet,” that is stronger evidence on TRL than any vendor deck. Aerospace Testing International
• Leonardo (Italy): active in EU Quantum Flagship sensing consortia and Italy’s national quantum programme, alongside its role in European PNT partnerships.

Global Trajectory & Regulatory Landscape (2026)

Region	Strategic Priority	2026 Regulatory Milestone
United States: From “Benchmarking” to Utility	The U.S. strategy has pivoted from experimental theory to industrial-scale engineering.	DARPA’s QBI: The Quantum Benchmarking Initiative (QBI) is now in a critical phase, aiming for fault-tolerant, utility-scale systems by 2033.National Standards: The NIST Post-Quantum Cryptography (PQC) standards are now the mandatory benchmark for federal agencies, forcing a massive private-sector migration to quantum-resistant encryption.
China: The QKD Superpower	Global QKD Coverage	Record Breakers: In early 2025, Chinese teams achieved a world-record 12,900 km quantum-secured link via satellite.The “Dawn” Satellite: China has announced the 2027 launch of “Dawn,” the first geostationary quantum satellite, which will provide 24/7 uninterrupted quantum coverage, bypassing the 9-minute window of current LEO satellites.
European Union	Strategic AutonomyThe EU is treating quantum as a critical infrastructure project to avoid dependency on U.S. or Chinese tech.	The European Quantum Act: proposal expected in 2026, with the Act itself now targeted for 2027.EuroQCI & Eagle-1: The European Quantum Communication Infrastructure (EuroQCI) is moving from pilot to operational status this year, supported by the launch of the Eagle-1 satellite to secure the bloc’s government communications.
India / Russia	Sovereign Standards	India’s Rapid RiseIn late 2025, India’s DRDO successfully demonstrated long-distance free-space QKD. This highlights a shift where “second-tier” quantum powers are skipping legacy infrastructure and moving straight to quantum-secure satellite communications.Russia: Indigenous Standards & Industrial PilotsRussia’s focus is on technological sovereignty, specifically developing domestic alternatives to Western standards.Indigenous PQC: Russia is developing its own quantum-resistant standards, such as the Shipovnik (Rosehip) algorithm. Russia frames this as protection against what it describes as potentially backdoored Western standardsHardware Roadmap: Rosatom has reported the completion of 50-qubit processors based on ions and neutral atoms, focusing on nuclear logistics and supply chain optimization.
Switzerland	Sovereign niche, regulator-led	Switzerland sits outside the EU and EuroQCI, so it is building its own posture. Its first national quantum strategy (Swiss Quantum Initiative, March 2026) flags the absence of a national QKD network and urges one that could later interconnect with EuroQCI. On the regulatory side, FINMA Guidance 05/2026 (July 2026) asks supervised financial institutions to hold a PQC migration roadmap by mid-2027 — aligned with the pan-European “Securing Tomorrow, Today” statement. On the ground, the Geneva Quantum Network (launched November 2025 by IonQ with CERN, Rolex and the Swiss government) is the country’s first dedicated quantum network.

Reality-check corner – 2026

To make informed investment decisions, it is vital to acknowledge what quantum cannot do today:

1. Breaking RSA-2048: Still impossible. To crack current encryption, we need millions of physical qubits with error correction. We remain in the NISQ (Noisy Intermediate-Scale Quantum) era, with a current ceiling of 1,000+ qubits.
2. Run outside a hybrid workflow: In 2026 the QPU is one accelerator inside a hybrid quantum–classical pipeline, not a standalone machine. Classical HPC still carries most of the load and orchestrates the quantum step; the near-term production model is hybrid, cloud-delivered Quantum-as-a-Service (QaaS), not on-premise quantum replacing your HPC.
3. On-Premise Costs: The overhead of cryogenic cooling and specialized maintenance makes on-premise hardware non-viable for most contractors. The immediate future is Quantum-as-a-Service (QaaS) via secured clouds (Azure, IBM, AWS).

Looking Ahead: Predictions for Industry Trajectory

Timeframe	Expected Quantum Impact in Dual-Use
Now (0–2 years)	Quantum utility through hybrid quantum-classical systems: PQC deployment on the finalized NIST standards, QKD pilot links, early field trials of quantum sensors, and benchmarking schemes.
2–5 years	Operational quantum sensors deployed on platforms, initial quantum-enhanced logistics tools emerge.
10 years	Potential fault-tolerant quantum systems supporting advanced AI, global QKD networks, and material R&D.

Closing Thoughts

Quantum in defense is a cycle of protection and optimization. 

The communication layer (PQC now, QKD where it actually earns its place) is the part with hard deadlines; sensing and optimization are still proving, problem by problem, where they beat classical methods. The organizations that come out ahead won’t be the ones with the best slide decks — they’ll be the ones that knew which algorithms to migrate first, which pilots were worth funding, and which vendors would still be standing in five years.

QCentroid was built to help organizations make decisions with evidence rather than hype. Through our QuantumOps platform, defense and dual-use teams can test quantum and hybrid workflows against their real use cases, benchmark hardware across providers, and do it all in an environment that meets their security and sovereignty requirements.1

If any of this is on your desk right now, let’s talk.

Building a quantum strategy has become urgent for organisations that weren’t ready for how fast quantum investment arrived. Many now have a quantum position before they have a quantum programme.

Which leaves a lot of organisations expected to have a quantum position before they have a quantum programme.

Carlos Kuchkovsky, CEO and Co-Founder of QCentroid, spoke about that gap at the Quantum Global Summit in Basel, in the session From zero to ready: building a strategy that delivers impact, alongside Romi Sumaria, Chief Commercial Officer at QAI Ventures, and Adrian Glatz, Partner at KPMG.

His starting point was the business case. A named problem. A KPI the organisation already reports on. A budget line.

The use cases and technical requirements may look very different across industries. But the path from zero to a quantum programme that can demonstrate business relevance — and survive a budget cycle — is remarkably similar.

That path starts by identifying problems worth solving, understanding how they are solved today, testing different approaches and building evidence around where quantum can add value.

Seven founders from the 2026 accelerator cohort graduated at the summit. Two years ago, QCentroid was in that seat.

Today, QCentroid provides the platform behind the QAI Ventures industry sandboxes, where organisations can explore sector-specific use cases in their own context, test what makes sense for them, and use that evidence to build the roadmap that comes next.

From being part of the accelerator to helping other organisations take their first structured steps into quantum.

A nice reminder that quantum readiness is not about having all the answers. It is about building the capability to find them.

If that is where your organisation is right now, take a look at the QAI Ventures industry sandboxes.

How QCentroid QuantumOps uses expert AI agents and NVIDIA CUDA-Q to start from proven classical baselines, design evidence-driven hybrid experiments, and recommend where —and whether— quantum computing should become part of an enterprise application.

Beyond algorithm selection

For years, much of the conversation around enterprise quantum computing has focused on algorithms: find a relevant business problem, identify a quantum algorithm that could address it, run it on a simulator or quantum processor, and compare the results.

That is an important first step, but it is not how most enterprise applications are actually built. Real applications are heterogeneous systems. They combine data processing, machine learning, optimization, simulation and business logic, already running across CPUs, GPUs and distributed infrastructure. Introducing quantum computing into those systems is therefore rarely a question of replacing an entire classical application with a quantum one.

The more useful question is where, if anywhere, should quantum computing sit inside an application that already works? This shifts the focus from quantum algorithm selection to hybrid application engineering. The objective is no longer simply to demonstrate that a quantum algorithm can solve a problem. It is to determine experimentally whether a quantum component contributes value, where it should be introduced, how much of the application should become quantum, and which computational resource should execute it.

In many cases, particularly in the near term, the resulting architecture will probably be hybrid. In others, continued experimentation may be justified while waiting for more capable quantum hardware. And sometimes the evidence will simply say keep it classical for now. All of these are valid outcomes.

This is the next stage of the QuantumOps approach described in our previous article. Once a classical baseline and a candidate computational problem exist, the challenge becomes engineering the boundary between classical and quantum computing—and accumulating enough evidence to make that boundary defensible.

Start from what already works

Hybrid quantum engineering should begin with a classical baseline, not with a quantum algorithm. The baseline establishes what an alternative architecture must compete with in terms of output quality, computational cost, execution time, scalability and operational requirements. More importantly, it provides a working application that can be progressively modified.

Rather than asking how to build a quantum version of an application, the engineering team can ask which parts of the existing application are actually worth changing. This distinction becomes particularly relevant outside optimization, where there may not be a single obvious solver or computational component to replace.

The QATALIZE project, currently being implemented by QCentroid and Gradiant using CESGA’s HPC and quantum infrastructure, provides a useful example. One of its objectives is to explore a generative AI approach to materials discovery using a conditional Generative Adversarial Network, or cGAN.

Materials discovery involves searching extremely large chemical spaces. In the catalyst use case, candidate materials can ultimately be evaluated using Density Functional Theory (DFT), but those calculations are computationally expensive. The generative model acts as a pre-screening mechanism: it learns from existing data, generates promising new candidates, and reserves expensive downstream evaluation for a much smaller subset. The same architecture can also target materials for CO₂ capture by changing the training data, chemical representation and conditioning targets.

The interesting question for hybrid computing is therefore not simply whether this GAN can be made “quantum”. It is where quantum computation should intervene inside it.

Hybrid engineering creates three levels of decision

What initially looks like a single decision—whether to use quantum computing—quickly separates into several architectural questions. In practice, there are at least three levels that must be navigated systematically.

Level 1 — Which application component?

The first decision is where quantum computing could plausibly contribute to the overall application. An industrial workflow may include data preparation, optimization, simulation, machine learning, inference and post-processing, while only a subset of those components may justify quantum experimentation.

In the QATALIZE materials-discovery example, the candidate component is the generative model used to navigate the chemical search space. Identifying that component, however, does not define the hybrid architecture.

Level 2 — Where inside that component?

Finding a promising component still does not tell us where its classical–quantum boundary should be. A GAN contains a Generator and a Discriminator, each composed of multiple transformations and layers. In the architecture being explored in this project, quantum experimentation is focused on the Generator. The Generator is the initial focus because it directly controls the learned representation used to explore the candidate space, making changes in its latent transformation measurable through downstream candidate-quality metrics.

The engineering question becomes much more granular: at which point inside the Generator should a quantum transformation be introduced? A parametrized quantum circuit (PQC) could operate close to the latent input, after an initial classical transformation, inside a compressed bottleneck, later in the network, or potentially at several points.

The architecture is therefore no longer simply classical versus quantum. It becomes a spectrum of possible hybrid designs.

Level 3 — On which computational resource?

Once a hybrid architecture has been defined, a third decision remains. The quantum component may initially run through GPU-accelerated simulation, then under simulated noise, and eventually—if the evidence justifies it—on a QPU. Different QPUs may themselves become alternative experimental targets.

These three decisions are related, but they are not interchangeable. Together, they define an increasingly large design space:

What to make quantum  →  where to make it quantum  →  where to execute it.

Hybrid application engineering therefore quickly stops being a single quantum experiment and becomes an experiment management and decision problem.

Finding the classical–quantum boundary inside a GAN

The QGAN experiment makes this design problem particularly visible. The classical baseline can be represented as a conventional Generator that transforms noise and conditioning information into a candidate material, while a classical Discriminator evaluates generated samples during training.

The conditioning signals describe what the researcher is looking for—for example, a target adsorbate and an adsorption-energy range—while the Generator learns to propose candidates consistent with those conditions.

Starting from that baseline, several alternative Generator architectures can be explored. A quantum transformation close to the latent space introduces a PQC early in the Generator, allowing the quantum component to operate on an initial representation of the latent variables. A quantum bottleneck first uses classical layers to transform or compress that representation, passes the resulting compact state through a PQC, and then expands it again through classical layers. A later quantum transformation allows classical computation to build a richer intermediate representation before applying the quantum transformation. More complex designs can introduce several quantum layers at different positions.

The architecture has deliberately been designed to support this flexibility: classical and quantum layers can be interleaved, while the number, depth and position of quantum components remain configurable.

Each architectural alternative then introduces further experimental parameters, including the number of qubits, circuit depth, encoding strategy, parametrized ansatz, training configuration and execution backend. The search space cannot be expanded arbitrarily: increasing qubit count or circuit depth may introduce trainability, gradient-estimation and sampling costs that must themselves become part of the experimental evidence. Hybrid application engineering becomes a search problem of its own. The question is no longer simply which quantum algorithm to use, but where quantum computation belongs within the application architecture.

To make these comparisons meaningful, the experimental design must also control for factors that could otherwise confound the results. Classical and hybrid architectures should be evaluated using consistent datasets, conditioning targets, training budgets and evaluation procedures, with repeated runs across controlled random seeds to account for training variability. Where architectures differ in model capacity, factors such as trainable parameter count, circuit evaluations and computational resources should also be tracked. The objective is not simply to find the best-performing configuration, but to determine whether an observed improvement can reasonably be attributed to the architectural change being tested.

Why systematic experimentation matters

Even a deliberately simplified experiment already creates multiple evaluation paths:

Generator architecture	Classical/GPU training	GPU quantum simulation	Noise-aware simulation	QPU experiment
Classical baseline	✓	—	—	—
Early quantum layer	✓	✓	✓	Selected
Quantum bottleneck	✓	✓	✓	Selected
Late quantum layer	✓	✓	✓	Selected
Multiple PQCs	✓	✓	✓	Selected

Legend: ✓ indicates a normal evaluation stage for that architecture; — means that the stage does not apply to the classical baseline; “Selected” means that QPU execution is not performed automatically for every configuration. Only architectures that produce sufficient evidence in earlier simulation and benchmarking stages are shortlisted for hardware validation.

And this matrix is still highly simplified. A single row may contain multiple circuit configurations, training parameters, random seeds, problem sizes and dataset variants. Experiments may need to be repeated to measure training variability, while each execution target introduces additional constraints and measurements.

The challenge is therefore not merely running a quantum circuit. It is maintaining enough structure across all those experiments to determine whether moving the PQC improved the generated candidates, whether that improvement remains under more realistic execution conditions, what additional computational cost it introduces, whether the result is reproducible and whether the architecture remains technically feasible as the problem scales.

Without systematic experimentation, it is relatively easy to produce an interesting quantum result while still lacking enough evidence to support an application decision.

CUDA-Q makes the execution target an experimental variable

This is where NVIDIA CUDA-Q becomes an important enabling technology. CUDA-Q provides a unified programming model for hybrid applications in which CPUs, GPUs and QPUs work together, and its backend model includes CPU and GPU simulators as well as supported quantum hardware targets.

For hybrid neural-network workloads, CUDA-Q also provides an integration pattern with PyTorch, allowing classical neural processing and quantum components to coexist inside the same workflow. The architectural consequence is more important than any individual API: changing the computational target does not necessarily require redesigning the overall application architecture.

Without a common execution layer, moving an experiment from one target to another can easily become an integration exercise involving different SDKs, interfaces and infrastructure. That friction makes systematic comparison harder because part of the engineering effort is spent changing technology rather than testing the hypothesis.

With CUDA-Q, a promising hybrid Generator can instead be evaluated progressively across GPU-accelerated quantum simulation, noise-aware experimentation and selected QPU execution, with evidence from each stage feeding back into architecture and circuit design, while preserving the same underlying application hypothesis. CUDA-Q provides GPU-based noisy simulation capabilities as well as multiple simulation strategies designed for different circuit characteristics and scales.

Different backends do not become equivalent. Hardware constraints, topology, noise, circuit characteristics and execution behavior still matter and need to be measured. The important difference is that these characteristics can become experimental variables rather than architectural barriers.

The less engineering effort a team spends switching execution technologies, the more effort it can spend determining whether the alternative architecture is actually better.

A real heterogeneous environment: HPC and QPU

The QATALIZE infrastructure provides a natural environment in which to apply this methodology. CESGA operates conventional HPC capabilities alongside Qmio, a quantum-computing research environment that includes a 32-qubit superconducting QPU and HPC resources connected to the quantum infrastructure.

This matters because hybrid applications are inherently heterogeneous. Dataset preparation, classical neural-network training and large experimental campaigns remain natural HPC/GPU workloads. Quantum components can be simulated on accelerated classical infrastructure and, where the evidence justifies doing so, selected configurations can advance to quantum hardware.

The objective is not to migrate the workload from HPC to quantum computing. It is to determine how classical, accelerated and quantum resources should cooperate within the same application. That is a fundamentally different adoption model.

From execution to evidence: the role of QuantumOps

CUDA-Q reduces the friction involved in constructing and executing heterogeneous experiments, but easier execution creates another challenge: more architectures and configurations are now feasible to explore.

As circuit parameters, datasets, Generator designs and execution targets multiply, the engineering team needs to preserve the relationship between the hypothesis being tested, the configuration executed and the evidence produced. This is the role of QCentroid QuantumOps.

QuantumOps structures the experimentation cycle around the computational infrastructure, managing alternative configurations, executions, results and benchmarking information so that experiments become comparable rather than isolated runs. Expert AI agents operate over this experimental context to assist particularly across execution, benchmarking and recommendation. Their role is not to make the architectural decision autonomously, but to help teams navigate the design space, identify meaningful comparisons, detect weak experimental branches, interpret accumulated evidence and suggest which configurations deserve further investigation. The final decision remains with the user.

In this sense, CUDA-Q and QuantumOps address different but complementary layers of the problem: NVIDIA CUDA-Q makes heterogeneous computing programmable, QCentroid QuantumOps makes heterogeneous architectures experimentally comparable and evidence-driven.

From experimental evidence to architectural recommendation

For the QGAN experiment, infrastructure metrics such as runtime and resource consumption are necessary but not sufficient. The central question is whether modifying the Generator improves its ability to propose useful material candidates.

Because the exact project evaluation framework will evolve with the implementation, a practical starting point is to focus on three complementary metric families that are widely aligned with evaluation practices in generative chemistry and materials discovery.

The first is candidate validity rate: the percentage of generated outputs that satisfy the chemical and structural constraints required by the representation. Validity is a standard dimension in generative molecular benchmarks, where the objective is to distinguish models that generate syntactically or chemically meaningful candidates from those producing unusable outputs. For QATALIZE, the exact validity rules can be adapted to the catalyst or MOF representation being generated.

The second is novelty and diversity. A generative model that repeatedly reproduces known candidates may achieve high validity while contributing little to materials discovery. Novelty measures whether generated candidates differ from the training set, while diversity or uniqueness captures whether the model is exploring different regions of the design space rather than repeatedly generating near-identical candidates. These dimensions are also commonly included in molecular generation benchmarks.

The third—and ultimately the most application-oriented—is a DFT-validated target hit rate: among the candidates selected for downstream validation, what proportion actually satisfies the target physical criteria, such as falling within the desired adsorption-energy window? Materials-generation research increasingly evaluates generated structures against target properties and validates promising candidates using first-principles calculations or experimental measurements rather than relying exclusively on ML-level metrics. For this use case, improving that hit rate would directly support the original objective: reducing the number of expensive evaluations required to find promising materials.

These three measures should be interpreted together with engineering evidence such as training stability, execution time, computational cost, required qubits, circuit depth and sensitivity to noise. A hybrid Generator that produces a marginal improvement in candidate quality at dramatically higher computational cost may not justify its complexity, while a configuration that is promising under ideal simulation but highly noise-sensitive may be better classified as a future candidate than as an architecture ready for QPU execution today. For an enterprise architecture, experimental performance must ultimately be evaluated alongside operational criteria such as integration complexity, reproducibility, reliability, security, deployment constraints and total computational cost. 

At the end of the cycle, QuantumOps and its expert agents can help consolidate the experimental evidence into an architectural recommendation. The evidence may show that the classical Generator remains the best option; identify a particular quantum bottleneck as promising enough for continued simulation; show that an architecture has potential but should wait for more capable or lower-noise hardware; or justify advancing a specific configuration to QPU validation.

The important point is that none of these outcomes is predetermined. The purpose of the process is not to find quantum advantage. It is to find the right computational architecture. In that context, keep it classical for now is not a failed quantum experiment but an evidence-based engineering decision.

When quantum does become part of the recommended architecture, the team should be able to explain not merely that a quantum experiment worked, but why that component was selected, why the classical–quantum boundary was placed there, what alternatives were evaluated, what evidence supports the decision and which computational resources are appropriate for executing it.

Engineering the boundary

Enterprise quantum adoption is unlikely to happen through a sudden transition from classical applications to quantum applications. It is more likely to emerge progressively inside heterogeneous systems, with CPUs, GPUs and QPUs performing the parts of a workload for which they are best suited.

The real engineering challenge is therefore not choosing between classical and quantum computing. It is finding the right computational boundary between them.

The QGAN materials-discovery project illustrates that challenge particularly well. Even after identifying the Generator as the candidate for quantum experimentation, multiple architectural questions remain: where to introduce the PQC, how much of the network to make quantum, how to configure it, how to execute it and how to determine whether the change produced meaningful value.

NVIDIA CUDA-Q provides the heterogeneous programming and execution foundation that makes this type of exploration practical. QCentroid QuantumOps provides the experimental framework around it: structured execution, benchmarking, evidence accumulation and AI-assisted recommendations.

Together, these capabilities shift the conversation beyond the quantum algorithm: from asking “Can we run this problem on a quantum computer?” to asking “Where should quantum computing participate in this application—and what evidence supports putting it there?”

From use case to benchmark, with NVIDIA CUDA-Q

An operational pattern for enterprise hybrid quantum-classical workflows — from a business problem to a reproducible, comparable benchmark.

Enterprise quantum computing is moving past the isolated demo. For a growing number of teams the question is no longer “can we run a quantum algorithm?” It is a harder, more operational one: how does a team get from a business problem to a reproducible hybrid quantum-classical workflow, compare approaches fairly across classical, GPU-accelerated and quantum backends, and keep improving the experiment as hardware, solvers and understanding of the problem evolve?

That is an operations problem, not only an algorithms problem. It is the problem QCentroid’s QuantumOps practice is built around, and this article is about the operating model. The implementation details come from running QuantumOps at QCentroid with NVIDIA CUDA-Q as the execution substrate, but the pattern is meant to generalize.

This is the second piece in a short series. It builds on two earlier QCentroid articles: the QuantumOps cycle as a framework for quantum-computing adoption, and the integration that made NVIDIA CUDA-Q available inside the QuantumOps platform. The first set out the cycle; the second made the substrate available. This article takes the next step, how QuantumOps acts as the harness for expert quantum agents: the operating layer they run in to drive that cycle end to end, on top of CUDA-Q.

The enterprise bottleneck: from idea to executable, comparable workflow

Most enterprise quantum initiatives begin with a promising use case: portfolio or risk analysis, routing, scheduling, energy-system optimization, materials or molecular simulation, or fraud detection. Between that first business question and the first trustworthy benchmark sit a series of unglamorous steps: the problem has to be translated into a technical formulation; a data schema defined; a classical baseline chosen; quantum or hybrid approaches mapped to executable models; backends configured; experiments tracked; and results compared in a way business, technical and research stakeholders can all read.

Without an operating framework, that work scatters across notebooks, scripts, SDKs, spreadsheets and ad-hoc reports. The scatter produces some recurring failures:

• Reproducibility. Seeds, shot noise and optimizer initialization move results run-to-run; without recorded controls, a number is an anecdote, not a measurement.
• Comparability. Results are hard to line up across solvers, backends and time when each run is configured by hand.
• Legibility. Decision-makers cannot tell whether progress is real, measured and worth the next round of scarce, costly hardware time.

A QuantumOps approach treats quantum experimentation as a managed lifecycle that also governs how agents and people collaborate — who proposed a step, who approved it, and what actually ran — so these failures are designed out rather than fought repeatedly.

Figure 1. Benchmarking jobs: reproducibility and comparability across solvers, datasets and backends.

The QuantumOps cycle

QCentroid structures enterprise adoption as a continuous experiment cycle — Define → Model → Execute → Benchmark → Learn → Iterate — where each step produces artifacts that can be versioned, reused and compared. (The cycle itself is the subject of the first article in this series; here it is the spine the agents and the substrate hang on.)

Figure 2. The QuantumOps cycle: Define → Model → Execute → Benchmark → Learn → Iterate.

A team starts by defining the use case: objective, constraints, datasets, expected outputs, the relevant KPIs and the business metric that will decide success. The problem is then modeled into a formal specification (an optimization model, a QUBO, a Hamiltonian, or a variational/hybrid routine) with an explicit input/output schema so that what is being solved is unambiguous and portable across teams. Candidate solver paths are selected (classical baseline, quantum-inspired, GPU-accelerated simulation, QPU validation), executed as tracked jobs, and benchmarked against a stated baseline. The team then learns from a comparable record and iterates. The value compounds: each experiment joins a growing evidence base that says what changed, why a result moved, and which configuration is worth testing next.

Figure 3. The QuantumOps journey and its per-stage tools across the platform.

QuantumOps: the harness for expert quantum agents

QuantumOps is the harness. It is the operating environment the expert quantum agents run in, it gives them the lifecycle to move through, the registry and traceability to record what they do, governed access to compute and data, and the guardrails that keep an accountable human in control.

QCentroid has developed and integrated a set of expert quantum agents into the platform,  one for each step of the cycle,  so a team can go from a business problem to a benchmarked result without re-inventing the workflow. Because the harness is what provides the lifecycle, governance and execution, it is not tied to those agents alone: the same cycle can drive expert agents created externally,  by a customer’s own team, a partner, or the wider ecosystem,  under the same controls. The harness is agent-agnostic by design; QCentroid’s agents are the ready-made default, not a lock-in. QCentroid’s broader multi-agent approach to quantum use cases is described here.

The agents do not replace quantum experts and they do not “do” the quantum computing; they remove the toil between a business intent and an executable, well-formed experiment, and keep a human at every decision that costs money or feeds a conclusion. A useful way to read the cycle is one expert agent per stage:

• Use-case builder agents. Turn a problem described in natural language into a structured use-case pack: business objective, technical objective, constraints, data schemas, candidate data, expected output, baseline methods, candidate quantum/hybrid approaches, benchmark criteria and success criteria.
• Modeling agent. Proposes initial formulations -decision variables, constraints, objective functions and candidate QUBO mappings for optimization; Hamiltonian construction and ansatz families for simulation- for an expert to review and own.
• Solver-path & development agent. Recommends candidate execution strategies  -classical baseline, quantum-inspired method, GPU-accelerated simulation, a CUDA-Q hybrid workflow, or a QPU-validation step- and when each is worth its cost and generates executable code.
• Experiment-design agent. Fixes the benchmark before it runs: which solver versions to compare, which datasets and instances, which backend configurations, which metrics, dependent and independent variables, and which reproducibility controls.
• Benchmark-interpretation agent. Summarizes results, technical and business oriented, against the KPIs, baseline and proposes the next iteration: refine the model, change the optimizer/solvers, grow the instance, or compare against a stronger baseline.

The agents scaffold; people decide. That division -automate toil, not judgment- is what makes the loop fast without making it reckless, and it is the governance posture enterprises in finance, energy, healthcare and the public sector actually require.

Figure 4. The agents scaffold; people decide

Figure 5. The use-case / feasibility agent turns a problem statement into a structured, feasibility-checked use-case pack for a human to approve.

Figure 6. The modeling agent’s mathematical definition — objective function, constraints and QUBO formulation rendered from LaTeX, for an expert to review.

NVIDIA CUDA-Q as the execution and resource-access substrate

The agents and the cycle need somewhere to run, and this is where NVIDIA CUDA-Q is load-bearing. CUDA-Q is NVIDIA’s open-source platform and programming model for hybrid quantum-classical computing (it is software, not hardware). That abstraction is precisely what lets QuantumOps keep the science separate from the execution target and the solver portable: the backend becomes a parameter, not a rewrite.

For simulation, CUDA-Q draws on cuQuantum -cuStateVec for dense circuits and cuTensorNet for larger structured ones- so the experimentation engine scales on GPUs before any scarce hardware time is spent. In practice a solver keeps one entry point and selects its backend as a parameter, so the same code path runs in GPU-accelerated simulation during iteration and against a QPU for validation:

import cudaq
 
@cudaq.kernel
def ansatz(thetas: list[float]):
    q = cudaq.qvector(4)
    h(q)
    # ... problem-specific cost and mixer / variational layers ...
 
def run(use_case, solver_params):
    #
    # backend is a parameter, not a fork:
    #
    cudaq.set_target(solver_params.get("backend", "nvidia"))
    #   "nvidia" / "tensornet"  -> GPU-accelerated simulation (iterate)
    #   a QPU target            -> hardware validation (promote)
    # build the model, optimize parameters, sample the result
    return result

The point is not the code; it is the property the code has — one solver definition, many targets.

Within QuantumOps, CUDA-Q becomes the managed execution path: a team defines a hybrid workflow, runs it through CUDA-Q on a GPU-accelerated simulator (in QCentroid via the Launchpad, with no local driver or environment setup), captures the experiment’s metadata, compares it with other solver paths, and later validates the same workflow on an available QPU when it is justified. CUDA-Q accelerates and standardizes execution; QuantumOps operates the lifecycle around it. See the companion how-to on accelerating enterprise adoption with NVIDIA CUDA-Q via the Launchpad

Figure 8. Run experiments on multiple devices and accelerated hardware.

How it fits together: from use case to benchmark

Take a domain-neutral example: a combinatorial allocation problem -assign limited resources under business constraints. The work is carried by the set of expert agents acting together across the cycle, not a single one. The builder and modeling agents turn the business description into a use-case pack (objective, technical objective, input/output schema, constraints, baseline methods, candidate approaches, metrics, success criteria) and a candidate formulation (here, a QUBO) for an engineer to review. The experiment-design agent then fixes the benchmark before anything runs:

• A fixed objective and a stated classical baseline (for example, a MILP or heuristic) on the same instances.
• A fixed instance set and dataset version.
• Agreed metrics, typically solution quality against the baseline and time-to-solution.
• Reproducibility controls: seeds, restarts the named simulator (state-vector vs tensor-network) and the GPU.
• The solver paths to compare: classical baseline, a CUDA-Q GPU-accelerated simulation path, and a candidate QPU-validation path.

Iteration happens in GPU-accelerated simulation; promotion to scarce QPU time is a deliberate, approved step, not a default. Every execution records its inputs, model and solver versions, backend, seeds, code version, logs, cost estimation and results, so a run is a versioned artifact rather than a transient notebook state. Because runs are declarative artifacts, the same experiment can be re-dispatched across backends for an apples-to-apples comparison, and a result from one quarter can be set beside the next without manual reconciliation.

A hybrid, heterogeneous problem

The next phase of quantum adoption is hybrid by design. Enterprises will not use QPUs in isolation; they will combine CPUs, GPUs, GPU-accelerated simulators, quantum backends, classical optimization, AI-assisted development and domain expertise. That makes orchestration, benchmarking and lifecycle management central rather than peripheral -the same concerns that already apply to any serious computational practice:

• Heterogeneous resource orchestration. The same experiment may run on a GPU state-vector simulator, a tensor-network simulator, or a QPU — each with different interfaces, capacities and failure modes — scheduled and tracked through one workflow.
• GPU-accelerated simulation as the engine. Most iteration happens in simulation, so simulation must scale and the simulation-to-hardware transition must be seamless.
• Reproducibility and provenance. Versioned, declarative runs with full provenance, so any result can be re-created.
• Cost estimation and energy-aware allocation. Scarce, costly compute means the cost and energy of each path should be estimated and visible before it is spent.

As GPU and quantum hardware move onto shared, low-latency fabric, the boundary between “simulate” and “run on hardware” becomes a configuration choice inside one hybrid program rather than a hand-off between systems. The operating principles above do not weaken in that world; they matter more, because faster and more heterogeneous execution raises the bar for reproducibility and disciplined benchmarking. That is the practical reason to build the practice on a portable and fundamentally hybrid substrate — NVIDIA CUDA-Q — today.

Figure 9. Solver development and deployment cycle 

From isolated demos to institutional memory

Enterprise adoption is a disciplined iteration, not a single proof of concept. Teams need to test formulations, baselines, optimizers, ansätze, backends, data versions and problem sizes — and to understand not just which result is best but why, under which assumptions, and whether it reproduces. When a new backend appears, relevant experiments can be re-run; when a better solver is added, it can be compared against historical baselines; when the business problem changes, the use-case pack and schema evolve without losing the prior history. The output of the method is evidence, not anecdote: a versioned record of what was run, against which baseline, on which resource, at what cost, with what result — an institutional memory that informs which problem is worth the next experiment.

Figure 10. Use cases lifecycle: Agents & Humans –  define, develop, approve.  

The shift is concrete: from “we ran a quantum proof of concept” to “we operate a structured, governed program of hybrid quantum-classical experimentation.” Expert quantum agents lower the cost of entering and moving through the cycle; NVIDIA CUDA-Q provides the hybrid execution and GPU-accelerated simulation that make the cycle fast and portable; QuantumOps is the operating layer — lifecycle, registry, traceability, benchmarking and governance — that turns the two into a repeatable enterprise capability.

Today, expert quantum agents help organizations move faster through the experimentation lifecycle while maintaining governance, reproducibility and comparability. Looking ahead, increasingly coordinated agent systems may automate experiment generation, benchmark execution, model refinement and solver selection, creating the foundations for autonomous quantum laboratories operating at enterprise scale.

The OECD published its report on quantum computing business readiness in March 2026. It’s based on interviews with 16 organizations across 10 countries, recent survey data, and a careful look at what firms are actually doing — not what they say they’re planning to do.

The gap between those two things is the most important finding in the report.

Carlos Kuchkovsky joined the OECD panel at Q-Expo to discuss the findings. This post is our take on what the report gets right, what it points to for companies thinking about where to start, and why the framing matters.

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The readiness paradox

The OECD surveyed executives across multiple sectors and countries. The numbers tell a consistent story:

97% of UK business leaders believe quantum computing will moderately or significantly disrupt their industry. One third have begun any strategic planning. In financial services, 73% of leaders see quantum as an opportunity — but haven’t identified a single application where it delivers a real commercial advantage. In a 2025 global survey of digital trust and IT professionals, 64% expect quantum to become widespread in their industries within a decade. 20% have a formal plan.

This isn’t a technology problem. Companies aren’t waiting because quantum hardware isn’t ready. They’re waiting because they don’t have the internal language, the use cases, or the people to act on what they already believe.

That’s a business transformation problem.

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What’s holding quantum computing readiness back

The OECD identifies four main barriers. They’re worth taking seriously, because they’re not the ones that get talked about most.

1. Technology immaturity — but the harder part is the stack

Current quantum hardware has real limitations: error rates, qubit counts, coherence times. These are engineering problems, not fundamental physics blockers, and they’re being worked on.

The harder issue for companies is that the full quantum computing stack — hardware, control systems, middleware, software — is fragmented and largely incompatible across providers. Quantum software is not hardware-agnostic. Skills built on one platform don’t transfer cleanly to another. There are no standard benchmarks.

For a firm trying to plan infrastructure or allocate R&D budget, this creates genuine uncertainty that’s separate from the hardware maturity question.

2. Limited awareness — and distorted expectations

Two things are happening simultaneously. Most firms haven’t identified concrete use cases relevant to their industry. And a subset of firms have been exposed to vendor-driven narratives that set expectations too high and too short.

When expectations don’t match reality, the result is disillusionment — not deeper engagement. The OECD is direct about this: awareness-raising without realistic framing does more harm than good.

3. Cost — and it’s not just the hardware

Cloud access has changed the economics of experimentation meaningfully. But remote quantum compute time still runs around $70,000 for 12 hours. And that’s before you factor in the internal costs: training staff, running readiness assessments, hiring people who can actually do something with the access.

For SMEs, this isn’t a marginal cost. And the export control restrictions on quantum cloud services create additional access barriers for firms in certain jurisdictions — a point the report flags clearly and that doesn’t get enough attention.

4. Talent — and it’s not the talent most companies are looking for

The assumption is that quantum readiness requires quantum physicists. The OECD’s finding is that this is wrong. The acute shortage is in engineers and developers who can work at the interface of quantum and classical systems, and algorithm specialists with industry-specific knowledge.

Europe has strong basic research in quantum hardware. The gap is on the software and application side. And the talent that does exist is unevenly distributed — concentrated in a few countries and firms, and increasingly being pulled from academic groups into industry, which tightens the pipeline further.

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What’s actually working

The OECD mapped five support mechanisms across the organisations they interviewed. The most effective ones share a common characteristic: they reduce the cost of early experimentation without asking companies to make long-term commitments before the technology justifies them.

Industry consortia that pool pre-competitive R&D are working. The QUTAC model in Germany — 14 major corporations sharing quantum algorithm development and use case documentation — shows what this looks like at scale.

Hybrid quantum-HPC infrastructure is emerging as the realistic near-term entry point. Not quantum alone. Quantum processors alongside classical supercomputing clusters, allowing incremental workflow testing. Technology providers interviewed for the report are explicit: near-term breakthroughs will come from combinations of quantum, AI, and HPC.

Short proof-of-concept formats — months, not years — are enabling firms to generate tangible outputs without PhD-scale investment. A few months of focused work can produce prototype code and a practical roadmap.

And there’s one unexpected entry point: quantum resilience. The transition to post-quantum cryptography is urgent, concrete, and compliance-driven. It’s creating a first conversation about quantum that leads naturally into readiness. Several interviewees noted that cryptography is opening the door for companies that wouldn’t otherwise engage.

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Where QCentroid sits in this

The framing that stays with us from the OECD report is this: readiness is not about being ready for quantum. It’s about building the capacity to adapt as the technology matures.

That’s not a technology project. It’s a strategic and organizational one.

The companies that will be well-positioned when quantum computing reaches commercial viability aren’t necessarily the ones with the biggest R&D budgets today. They’re the ones that started mapping use cases, building internal vocabulary, and identifying the right ecosystem partnerships early.

That process doesn’t require fault-tolerant quantum hardware. It requires clarity on where the technology could matter for your business, and a methodical way to build toward it.

That’s the work.

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Read the full OECD report: https://www.oecd.org/en/publications/building-business-readiness-for-quantum-computing_ee847e5f-en.html

“How do we start?” is still the most common question financial institutions ask about quantum.

Last week at Quantum Industry Day 2026 in Singapore — hosted by the National Quantum Office and Quantinuum — our CEO, Carlos Kuchkovsky, gave two talks focused on answering exactly that. One framework, one practical model, and a fair amount of honesty about what quantum can and can’t do in production today.

Here’s the longer version of what was on stage.

The morning: a framework that doesn’t overcommit

The premise of the morning session was simple. Financial institutions don’t need to wait for fault-tolerant quantum hardware to start preparing — but they also shouldn’t pretend the technology is further along than it is. The interesting work happens in between: building a disciplined adoption roadmap that takes quantum seriously without overcommitting budget, headcount, or credibility.

Carlos walked through five stages of that roadmap:

1. Identify high-value business problems first, technology second. The starting point is “which problems are expensive enough, complex enough, or strategically important enough that a 10–20% improvement would matter.” Optimization, fraud detection, portfolio analytics, risk modeling, and decision-support workflows all fit.

2. Distinguish credible use cases from premature ones. Not every problem that could benefit from quantum will benefit on near-term hardware. The honest question is: can NISQ systems offer a real advantage soon, or have classical methods already solved the problem just as well?

3. Assess data and classical baselines properly. This is where most early quantum projects quietly fail. Without a strong classical baseline, you can’t tell whether a quantum experiment produced a real signal or just a different result. And without clean, structured data, the experiment isn’t really about quantum at all.

4. Structure experiments with the right partners. Internal teams alone rarely have the full stack of expertise — algorithms, hardware access, domain modeling, and software engineering. The most productive setups bring together internal business owners, quantum startups, and research groups around a shared problem statement. This is where the sandbox model comes in.

5. Build long-term strategic positioning. Adoption isn’t a one-off pilot. It’s a multi-year journey that requires internal champions, visibility at the board level, and a portfolio of use cases at various stages of maturity.

The honest part of the talk was about what quantum can’t do in production today and why that matters when you’re allocating budget. Pretending otherwise costs credibility internally, which is the resource quantum programs run out of the fastest.

The afternoon: sandboxes as the practical answer

If the morning was about the framework, the afternoon was about what it looks like when an enterprise actually starts moving through it.

The challenge most financial institutions face isn’t a lack of interest. It’s a lack of a starting point. Internal teams don’t have access to quantum hardware or domain-specific software stacks. Startups have the technology but not the business context. Research groups have the science but not the enterprise integration. Collaborative sandboxes exist to put all three in the same room — around the same problem.

QCentroid’s sandbox platform provides the technical layer: access to quantum hardware, simulators, and development tools, configured as branded, purpose-built environments tailored to specific collaboration models. The interesting part isn’t the infrastructure itself — it’s how the infrastructure changes who can participate and how quickly.

Carlos presented two complementary examples from the Singapore ecosystem.

A venture-building sandbox with QAI Ventures Singapore. Here, corporates and quantum startups co-develop applied solutions around specific, high-value business problems — quantum-enhanced fraud detection being one of the live workstreams. The sandbox compresses what would normally be a 12–18 month procurement-and-pilot cycle into a structured collaboration with clear milestones. The path is commercial: a business problem looking for a startup-built solution.

A research-to-industry sandbox with Singapore’s National Quantum Computing Hub, including Q4F. Q4F (Quantum for Finance) is led by the Centre for Quantum Technologies and supported by NQCH. It’s designed to translate frontier academic quantum research into enterprise-relevant tools. The path is different from the venture sandbox: research-led rather than commercial-led, but the destination — industry application — is the same.

These represent two different entry points into the quantum ecosystem. One is driven by commercial problem-solving. The other by translating frontier research into industry-ready tools. Most enterprises will end up needing a bit of both, depending on the maturity of the use case they’re working on.

Beyond finance

A natural question after the afternoon talk: Does this only work for financial services? The short answer is no. The sandbox framework (a shared environment, multi-party collaboration around a defined business problem, and a structured progression from concept to a testable use case) extends naturally to logistics, energy, healthcare, and other sectors where quantum has credible long-term value. Finance is just the most advanced testbed today because the budgets, the data, and the competitive pressure are all there.

What this means if you’re starting now

If you’re inside a financial institution and trying to figure out the next step, the practical takeaways from both talks come down to three things:

• Start from a business problem, not from the technology. The strongest quantum programs we see are owned by the line of business, not by an isolated innovation team.
• Use a sandbox-style collaboration to accelerate learning. Build vs. buy isn’t the right question yet — co-develop is.
• Be honest about timelines, internally and externally. Credibility matters. So does its absence.

Thanks again to the National Quantum Office and Quantinuum for organizing Quantum Industry Day, and to the panelists, speakers, and participants who turned the conversations into something more useful than another event recap.

If you want to talk through what a sandbox engagement could look like for your organization, get in touch — we’d rather have a 20-minute conversation than send you a brochure.

Hybrid quantum-classical algorithms such as the Variational Quantum Eigensolver (VQE) are fundamentally iterative. A parameterized quantum state is prepared, expectation values are measured, and a classical optimizer updates parameters. This loop continues until convergence. At research scale, this can be executed in a notebook. At enterprise scale, it needs to be a structured experimentation pipeline.

NVIDIA CUDA-Q provides a unified hybrid programming model where quantum kernels and classical optimization routines co-execute within a single program. Through its MLIR → LLVM → QIR compilation stack and backend abstraction layer, CUDA-Q enables developers to write a VQE workflow once and execute it interchangeably on GPU-accelerated simulators or physical QPUs. The hybrid loop is expressed as a coherent computational construct rather than stitched together across disparate SDKs.

In enterprise environments, execution is only one dimension. Access to scalable infrastructure during development is equally critical. Large parameter sweeps, ansatz exploration, and convergence studies require substantial compute resources. Provisioning and configuring GPU infrastructure often becomes a bottleneck before the algorithm itself is optimized.

This is where a QuantumOps framework complements the NVIDIA stack.

To make this concrete, consider electrolyte design for industrial batteries. From a computational perspective, the task reduces to estimating electronic structure properties of candidate molecules. A Hamiltonian is constructed and mapped to qubits. An ansatz is defined. A classical optimizer iteratively updates parameters to minimize the expected energy.

In practice, this workflow requires repeated experimentation. Ansatz depth must be varied. Optimizers must be compared. Noise models must be evaluated. Simulation results must eventually be validated on real hardware. Each variation expands the experimental search space.

Enterprise VQE workflows require structure.

In our example, when screening multiple electrolyte candidates, hundreds or thousands of experiments may be executed. Hamiltonians evolve as molecular models are refined. Optimizer configurations change. Backend selections shift between GPU simulators and QPUs. Without operational discipline, results become fragmented and difficult to reproduce.

A QuantumOps framework layers experiment lifecycle management on top of CUDA-Q’s hybrid runtime. Experiments are defined declaratively. Hamiltonians, ansätze, and optimizer configurations are versioned as artifacts. Backend selection, shot configuration, and execution metadata are captured automatically. Convergence trajectories are stored and indexed for comparison.

CUDA-Q accelerates hybrid execution. QuantumOps systematizes hybrid experimentation.

The combined impact can be summarized in the following end-to-end comparison:

End-to-End VQE Workflow: Fragmented vs Accelerated & Operationalized

Workflow Step	Fragmented Stack	NVIDIA CUDA-Q + QuantumOps Framework	Primary Benefits (Impact)
Hybrid algorithm implementation	Quantum circuits and classical optimizers integrated manually across separate SDKs.	Unified hybrid kernel model in CUDA-Q with structured experiment definitions.	Reduced integration complexity.Development time ↘
Simulation performance & scaling	CPU-based simulation limits qubit count and slows parameter sweeps.	GPU-accelerated simulation on NVIDIA GPUs with managed sweep orchestration.	Higher throughput.Time-to-insight ↘, scalability ↗
Infrastructure availability during development	Local setup or custom cloud provisioning required.	Instant access to CPU and NVIDIA GPU resources via QCentroid Launchpad.	Faster onboarding.Infrastructure friction ↘, deployment time ↘
Hardware portability (Sim ↔ QPU)	Backend-specific APIs; hardware changes require refactoring.	QPU-agnostic execution compatible with leading quantum hardware providers.	Reduced vendor lock-in.Vendor lock-in ↘, validation cycles ↘
Experiment lifecycle management	Script-driven runs with manual tracking of configurations.	Declarative, versioned experiment definitions with captured metadata.	Deterministic reproducibility.Reproducibility ↗, technical risk ↘
Parameter sweeps & benchmarking	Manual aggregation and comparison of results.	Indexed sweeps with normalized cross-backend benchmarking.	Structured performance comparison.Optimization cycles ↘, decision confidence ↗
Resource & cost visibility	Fragmented tracking across vendors.	Per-experiment visibility across GPU and QPU execution.	Better hardware allocation.Cost control ↗
Collaboration & knowledge retention	Results dispersed across repositories and individuals.	Persistent experiment registry with searchable history.	Institutional continuity ↗

Although electrolyte design provides a concrete example, the architecture is domain-agnostic. Any enterprise VQE workload—whether in materials science, energy systems, or optimization—requires scalable simulation, backend portability, reproducibility, and cost-aware infrastructure allocation.

The following figures illustrate the automated execution of a benchmarking job for the given use case using all the solvers and the datasets configured.

Additionally, using QCentroid Launchpad, our cloud-hosted Jupyter development environment, teams can start hybrid quantum development with immediate access to CPU, RAM, and NVIDIA GPU resources. There is no local infrastructure setup, no driver configuration, and no manual environment management. Developers can begin experimenting with CUDA-Q on GPU-backed instances in minutes.

CUDA-Q accelerates this loop through unified hybrid execution and GPU-backed simulation. QCentroid Launchpad ensures that the necessary NVIDIA GPU infrastructure is immediately available during development. But execution speed and infrastructure access still do not fully solve the enterprise challenge.

More importantly, workloads can be deployed on different NVIDIA GPU configurations with minimal friction. Teams can test how simulation performance scales across GPU models, evaluate memory requirements for larger qubit counts, and determine which hardware configuration is sufficient for their use case. This flexibility directly impacts cost efficiency: instead of overprovisioning infrastructure, enterprises can calibrate resources based on measured performance.

The combination of NVIDIA GPUs and NVIDIA CUDA-Q creates a versatile development substrate. GPU-accelerated state vector and tensor network simulators allow simulation of larger qubit systems than CPU-bound approaches. At the same time, resource allocation can be adapted dynamically to match problem scale and budget constraints. As quantum hardware evolves, the same CUDA-Q code remains compatible with leading QPU providers thanks to its qubit-agnostic backend abstraction. Simulation and hardware validation are not separate development tracks, they are part of a continuous workflow.

Going back to our example, in a simplified hybrid quantum–classical workflow implemented with NVIDIA CUDA-Q, in practice, enterprise teams design parameterized quantum circuits, execute them on NVIDIA GPUs for large-scale simulation, and iteratively optimize them using classical routines. The same code structure can later be executed against real QPUs without redesigning the algorithm.

The figure below illustrates how a parameterized circuit is defined, simulated on NVIDIA GPUs, and embedded in a classical optimization loop — the foundational pattern behind most near-term quantum applications such as optimization, chemistry simulation, and machine learning.

When layered with a QuantumOps framework, this stack transforms VQE from an experimental algorithm into an operational research system. In industrial electrolyte discovery—and across enterprise quantum computing—the true acceleration vector lies not only in faster computation, but in the convergence of scalable GPU infrastructure, portable hybrid execution, and structured experimentation.

Beyond Computation, Towards Transformation

Quantum computing has transcended the realm of scientific curiosity to emerge as a definitive strategic enabler, already reshaping how organizations innovate, optimize, and compete. For CEOs, Boards and senior leaders, the essential discussion is not about qubits or algorithms; it is squarely focused on value creation, risk management, and long-term competitive positioning.

The business case is compelling. Benchmarks from D-Wave’s 2025 survey and McKinsey analyses indicate early-stage efficiency improvements in the range of 8–15% for selected operational optimization pilots alongside accelerated R&D cycles in specific simulation-heavy domains, particularly materials science and drug discovery. This is not an IT upgrade; it is a strategic reset that will impact logistics, finance, healthcare, and energy alike. These pioneering examples confirm that quantum conversation is not a future consideration; it is a present-day boardroom imperative.

As with every technological revolution, the window of opportunity is closing rapidly. Early adopters are positioned to capture significant efficiencies, accelerate their strategic goals, and actively shape the ecosystems of tomorrow. Those who hesitate will inevitably face higher costs, steeper learning curves, and diminished market influence. The strategic implications are clear: quantum computing will redefine competitive advantage through unprecedented efficiency, accelerated innovation, and enhanced resilience. Organizations must act now to harness its potential or risk being left behind.

The choice before boards is both simple and stark: shape the future, or be reshaped by it.

The Strategic Inflection Point: A New Class of Problem-Solving 

Quantum computing represents a fundamental paradigm shift in processing information. Classical computers evaluate possibilities sequentially; quantum systems leverage the principles of superposition and entanglement to explore millions of possibilities simultaneously. This is not about incrementally faster calculations but about achieving a step-change in capability, allowing organizations to solve problems of complexity and scale that are currently intractable.

For the strategic leader, this translates into unlocking capabilities previously beyond reach. Logistics enterprises will optimize global routes and inventories in real time, realizing double-digit savings in fuel and warehousing costs while significantly strengthening resilience, as seen in Volkswagen’s pilots and echoed in 2025 manufacturing case studies. Financial institutions will run millions of parallel scenarios, revolutionizing portfolio optimization, derivative pricing, and enterprise risk management. In healthcare, pharmaceutical companies will compress drug discovery timelines by digitally simulating molecular interactions, securing decisive advantages in patents and market positioning. Energy firms are already designing next-generation batteries and modeling climate risk with greater accuracy, per McKinsey.

The essence for the Board is this: quantum computing turns overwhelming complexity into unparalleled opportunity, compressing time-to-value from years to minutes.

Implications for Corporate Strategy and the Imperative of Timing

The adoption of quantum computing should be understood not as an incremental IT upgrade, but as a strategic reset that cuts across every dimension of Corporate Strategy. Its most immediate impact will be unlocking efficiencies at a scale classical computing cannot match, particularly in logistics, manufacturing, and energy, where cost reductions in the double digits will become feasible, as seen in Volkswagen’s pilots and echoed in 2025 manufacturing case studies.

Furthermore, quantum capabilities will dramatically accelerate strategic initiatives. Transformation programs encompassing digital evolution, sustainability transitions, or global supply chain redesign will advance at an exponential, rather than linear, pace. Quantum also fundamentally enhances organizational resilience. By evaluating millions of scenarios in parallel, companies can anticipate disruptions and model responses proactively rather than reactively. Financial institutions will stress-test portfolios against countless simulated futures, and energy firms will design systems robust enough to withstand extreme volatility. By 2025, quantum is aiding climate modeling with unprecedented accuracy, per McKinsey, helping firms like E.ON anticipate disruptions.

Perhaps most significantly, quantum readiness will redefine industry ecosystems. Markets will reorganize around those who are prepared, with early movers dictating standards, forming new alliances, and reshaping value chains. Partners, providers, and competitors will be reassessed through this new strategic lens.

On the critical question of timing, the horizon is clear. In the immediate term of one to two years, pilots will proliferate and deliver measurable value in logistics, finance, and R&D, as McKinsey reports that quantum firms generated $650-750M in revenue in 2024 alone. In the medium term of three to seven years, quantum-enhanced applications will achieve unambiguous returns on investment, forcing entire industries to reorganize around early adopters. Looking seven to fifteen years ahead, quantum will be deeply embedded in mission-critical operations, and business models themselves will be transformed. Boards must recognize that waiting is not a neutral act; it is a conscious decision to cede ground and incur a future cost of catch-up.

Risks and Mitigation: Navigating the Challenges of Quantum Adoption

While the opportunities are substantial, quantum adoption comes with notable risks that boards must address proactively. Key challenges include high initial costs, talent shortages, and security vulnerabilities. For instance, entry costs for pilots can range from $ 100K to $1 M, per industry benchmarks. Talent gaps are acute: Deloitte’s August 2025 report shows only a 4.4% growth in quantum job postings, with projections indicating that only 50% of quantum computing jobs were filled in 2025 and a global workforce of approximately 30,000 professionals. Additionally, quantum computers pose a threat to current encryption, potentially breaking standard protocols, as highlighted in McKinsey’s June 2025 insights.

To mitigate these, organizations can start with cloud-based quantum access (e.g., via IBM Quantum or Amazon Braket) to minimize hardware investments and upfront costs. For talent, partner with universities or firms like IBM for training programs, and focus on upskilling existing staff. On security, transition to post-quantum cryptography standards from NIST to safeguard data. Phased investments reduce overall risk, turning potential hurdles into manageable steps.

The Proof in Practice: Board-Ready Examples

The theoretical potential of quantum is best understood through its practical, board-ready applications. JPMorgan Chase offers a compelling case. Since 2018, the firm has pursued quantum-generated cryptography and applied advanced algorithms to complex financial modeling, exploring use cases in portfolio optimization, option pricing, and fraud detection . JPMorgan Chase’s ongoing work since 2018 now includes a 2025 open-source quantum software library for error-correction (reducing qubits needed by 10-100x) and a leadership overhaul to accelerate applications like portfolio optimization. For the financial sector, the opportunity encompasses stronger security, faster and more accurate decision-making, and superior capital efficiency. The risk of inaction is exposure to breaches, regulatory challenges, and competitive inefficiency

In the automotive sector, Volkswagen’s demonstration of real-time traffic optimization in Lisbon using a quantum annealer provided a clear window into the future. By dynamically rerouting buses during a major event, the company achieved significant efficiency gains. Building on Volkswagen’s 2019 Lisbon pilot (which reduced delays via real-time routing), recent expansions, such as DHL’s 2025 IBM collaboration, show supply chain optimizations cutting costs by double digits. For boards, the opportunity translates into direct cost savings and elevated service excellence. The risk is remaining locked into outdated, inefficient operational models while competitors learn to optimize in real time.

Within the energy sector, E.ON has applied quantum computing to climate risk modeling, enabling it to anticipate systemic volatility and design more resilient energy networks. E.ON’s quantum algorithm for weather risk modeling, developed with IBM (2024 updates show grid optimization for renewables), enables resilient networks amid climate volatility. The strategic implication is leadership in sustainability and operational robustness. The risk of delay is weaker positioning during the historic energy transition. These examples, alongside Deutsche Bahn’s work on railway scheduling and Nippon Steel’s simulation of advanced materials, illustrate a consistent theme: quantum computing is already driving efficiency and innovation in critical infrastructure.

While specific ROI remains proprietary, early pilot benchmarks across these industries indicate a target range of 8-15% efficiency gains in optimized processes (e.g., logistics routing, asset utilization) and potential reductions in R&D timelines by 25-40% for complex material and drug discovery projects (D-Wave 2025 benchmarks for logistics/asset utilization; McKinsey Quantum Monitor 2025 for drug discovery). These figures provide a concrete framework for calibrating potential investment returns . Leaders expect $1-5M ROI in the first year of adoption, per D-Wave’s July 2025 study.

Cultivating the Quantum Mindset: A Framework for Governance and Action

Adopting quantum computing demands a fundamental shift in leadership mindset. Boards and executives must grow comfortable making strategic decisions under uncertainty, moving from a deterministic, linear worldview to a probabilistic one. This requires seeing interdependencies across the ecosystem and recognizing that quantum’s greatest value is unlocked by solving interconnected problems rather than operating in isolated silos. Finally, a culture of iterative learning is essential, as quantum adoption is less a single bold bet and more a disciplined process of experimentation, piloting, and rapid adaptation.

This mindset must be operationalized through a structured, four-phase roadmap . In the roadmap, add cost estimates per phase, e.g., “Awareness: Low-cost workshops ($10K-50K); Pilots: $100K-500K via platforms like QuantumOps (per industry averages, Deloitte 2025)”.

The journey commences with building Awareness ($30K-50K), where the board and executive team align on the strategic significance of quantum and develop a common language. This initial phase is crucial for defining the strategic imperatives: What is the precise opportunity for our business, and what is the cost of inaction?

The subsequent phase involves Identifying Quick Wins, selecting one or two high-impact use cases directly tied to key performance indicators. This forces the critical questions: Which application offers the highest financial return, and what are the realistic benchmarks for success? These are then rigorously tested in controlled Pilots ($100K-500K via platforms like QuantumOps, per industry averages, Deloitte 2025), where organizations validate value and build internal credibility without excessive investment. Here, leadership must address critical questions about resource allocation: What is the required investment for exploration, and how do we bridge the capability gap with our existing talent ? The final phase is Scaling and Integration, embedding quantum insights into core operations. This demands a plan for operationalization: How do we integrate these new capabilities into our current systems and processes to secure a lasting advantage?

Through this lens, quantum adoption is revealed not as a gamble but as a structured journey in which each step systematically reduces risk, delivers tangible learning, and compounds competitive advantage.

Partnering Options: Bridging Boardroom Strategy to Operational Reality

At QCentroid, we bridge the gap between visionary strategy and operational execution. We operate not as a technology vendor, but as your strategic partner, guiding boards and executives through the complexities of the quantum transition. Our purpose is to translate quantum potential into tangible value, connecting capabilities directly to corporate strategy and profitability.

Our neutral, unbiased approach ensures we evaluate the entire technology ecosystem to find the right solution for your specific challenges. Through structured frameworks and proven methodologies, we provide the clarity needed to start, prioritize, and integrate quantum into your organization’s strategic DNA

Our partnership is built to deliver concrete outcomes:

We begin by Framing the Strategic Conversation, facilitating executive workshops to align leadership on quantum implications and actions to answer the fundamental question: ‘What is the precise opportunity for our business and the cost of inaction?‘“. We then Identify and Prioritize Value, working with your teams to pinpoint the use cases with the greatest impact on your business model and KPIs. Our QuantumOps Platform empowers you to De-Risk Exploration, allowing you to test and validate these use cases without heavy upfront investment or scarce internal expertise. Finally, we help you Develop a Long-Term Roadmap to build quantum readiness into your organization’s core operations.

This end-to-end partnership—led by Carlos Kuchlovsky and a team with a proven track record across corporate and entrepreneurial environments—accelerates learning, builds internal capability, and transforms quantum computing from a theoretical concept into a profitable, strategic, and actionable advantage.

Final Reflection

Quantum computing is not an IT issue. It is a Board-level agenda item that will define which companies lead industries and which are forced to follow.

The organizations that act now will:

• Accelerate their strategic goals—achieving in years what others take decades.
• Redefine their industries—by setting standards and shaping ecosystems.
• Capture talent and partnerships that strengthen their competitive edge.
• Build resilience—turning uncertainty into foresight and complexity into opportunity.

Those who wait will face higher costs, weaker positioning, and the risk of irrelevance as competitors reshape the market with quantum advantage. Boards should evaluate quantum readiness now to align with accelerating adoption trends (the quantum market is projected at $1.6B in 2025, rising to $7.3B by 2030, BCC Research).

Sources/References

1. McKinsey Quantum Technology Monitor 2025: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-year-of-quantum-from-concept-to-reality-in-2025
2. D-Wave 2025 Survey on ROI: https://www.dwavequantum.com/company/newsroom/press-release/new-study-more-than-one-quarter-of-surveyed-business-leaders-expect-quantum-optimization-to-deliver-5m-or-higher-roi-within-first-year-of-adoption/
3. Deloitte Quantum Computing Report 2025: https://www.deloitte.com/us/en/insights/topics/emerging-technologies/quantum-computing-futures.html
4. BCC Research Quantum Market 2025: https://www.bccresearch.com/pressroom/ift/global-quantum-computing-market-to-grow-346
5. Quantum Risks 2025: https://www.pkfod.com/insights/the-leap-to-quantum-unleashing-the-power-to-unlock-the-opportunities/