Category: Blog

The Quantum Imperative: What Every Leader Needs to Know

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 8–15% efficiency gains in operations and 25–40% faster R&D cycles with real-world examples showing 2-2500x speedups in simulations. Don’t think about it as an IT upgrade; think of it as 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. Recent Quantum Examples: https://datafloq.com/read/5-real-world-applications-of-quantum-computing-in-2025/
  6. Quantum Risks 2025: https://www.pkfod.com/insights/the-leap-to-quantum-unleashing-the-power-to-unlock-the-opportunities/

Building an AI Multi-Agent Tool for Quantum  Computing Use Case Discovery

15 min read

Oscar Bastidas Jossa, Artificial Intelligence Engineer
Alberto Calvo, CTO and co-founder

Summary

Quantum computing holds transformative potential, yet its practical adoption remains hindered by complexity and uncertainty about real-world applications. To address this gap, we present the Qcentroid multi-agent AI tool that systematically translates business needs into scientifically grounded quantum computing use cases through a three-stage architecture: Interview, Generator, and Deep Research. In the Interview stage, an interactive AI agent collects organizational objectives and computational bottlenecks, producing a structured problem summary. The Generator stage validates problem suitability and synthesizes candidate quantum applications by aligning business needs with established quantum computing algorithms. The Deep Research stage employs a parallelized, multi-agent framework to conduct rigorous feasibility analyses. Drawing on semantic search over curated scientific databases, online literature retrieval, and iterative evaluation, the system produces comprehensive feasibility reports detailing scientific and mathematical basis, suitability of employing Quantum/Quantum-Inspired computing methods, scientific evidence, limitations and risks, for the proposed use cases. By combining conversational problem scoping, algorithmic mapping, and autonomous scientific investigation, our platform enables organizations to make evidence-based, strategic decisions about quantum adoption.

The problem, and what does our new tool address? 

Despite the fact that developing end-to-end, production-ready quantum solutions for business problems is not yet ready, recent research advances, such as progress on error-correction techniques and improved qubit stability, show clear movement toward practical deployment [1]. Because potential applications span numerous industries, the space for application software is far from saturated [2], offering significant opportunity but also considerable uncertainty. As a result, organizations often struggle to assess whether their computational challenges are well-suited to quantum approaches and how best to evaluate and validate such opportunities. Survey studies, such as [3], highlight that the complexity of quantum systems and software is among the primary barriers to broader adoption. This uncertainty slows progress and leaves decision-makers without the evidence needed to confidently justify investment.

Our tool, the QCentroid Multi-Agent AI Use Case Generator tool, was designed precisely to try  bridging this gap. It transforms informal business conversations into structured, scientifically grounded quantum computing use cases. By combining conversational scoping, algorithmic mapping, and autonomous deep research, the system helps organizations cut through complexity and make data-driven decisions about quantum feasibility.

Technical Concepts

Before proceeding to explain each stage of the architecture, let´s briefly explain some technical concepts that are used in this architecture and will be mentioned along the article.

Prompting techniques: We applied several prompting techniques across different sections of the system. These included:

  • Persona Prompting: Crafting prompts that assign the model a specific role or perspective.
  • Chain-of-Thought (CoT) Prompting: This technique enables complex reasoning by guiding the model through intermediate reasoning steps [4].
  • Few-Shot Prompting: In addition to task descriptions, the model is shown a few illustrative examples. This helps generalize to new tasks by following the demonstrated patterns [5].

Graphs: The architecture was implemented using LangGraph. At its core, LangGraph models workflows as graphs, where the behavior of agents is defined by three main components:

  • States: Shared data structures that represent the current state of the application.
  • Nodes: Functions that encode the logic of the agents.
  • Edges: Functions that determine the next step to execute based on the current state.

Subgraphs: A subgraph is a graph encapsulated as a single node within another graph. In our architecture, the three main stages—Interview, Generator, and Deep Research—were encapsulated as subgraphs within the multi-agent system. This design offers several benefits:

  • Modularity: Each subgraph can be developed and tested independently, having their own states. For example, we could test different types of users and business contexts in the interview subgraph, without needing to run subsequent subgraphs. The same applied to the other stages.
  • Specialization: Agents can focus on specific domains. This opens opportunities to expand beyond quantum computing—for instance, by adding expert agents specialized in Machine Learning (ML).
  • Control: Flow management is more straightforward. For example, in the Deep Research subgraph, we were able to execute parallel procedures efficiently thanks to the subgraph-based design.
Multi-agent AI architecture for quantum computing use case generation diagram

Figure 1. Example of shared and private states in two subgraphs.

One example of the benefits of modularity is illustrated in Figure 1. The diagram shows how two subgraphs share the common “messages” state, while still maintaining their own private states: “generate_use_cases” for the Generator subgraph and “pinecone_search_query” for the Deep Research subgraph.

Context engineering: is about designing dynamic systems that provide the right information and tools in the right format, enabling Language Large models (LLM) to accomplish tasks effectively. Complex agents such as the architecture shown in this article, gather context from multiple sources— users, past interactions, tool calls, or external data—and must integrate these dynamically. Since LLMs cannot infer missing details, success depends on supplying accurate context and relevant tools. Equally important is format: clear, structured inputs enable the model to reason and act far more reliably than poorly organized or ambiguous data [6]. Context engineering was widely used in the research agent process, which we will discuss later.

Structured output: Refers to guiding the model to produce responses in a predefined format, it is important for context engineering to provide clear and structured information.  To achieve this, a schema must first be defined—commonly using a JSON-like structure or libraries such as Pydantic. LangChain simplifies this process with the with_structured_output() method, which automatically binds the schema to the model and ensures the output is parsed correctly into the desired structure.

Map-Reduce: Advanced design pattern that allows parallel execution in <LangGraph, using the Send object.

Retrieval Augmented Generation (RAG): is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response [7].

Note:  In this article, the words subgraphs and agents can be used interchangeably, since in our architecture each subgraph acts as an agent that can decide the control flow of the application.

The Architecture

Figure 2. AI Multi-Agent Quantum computing Use Case Generator architecture

This section provides a comprehensive overview of our Multi-Agent Quantum Computing Use Case Generator tool.  The architecture is divided into three-stages: Interview, Generator, and Deep Research, as seen in Figure 2. 

In this architecture, each stage corresponds to a subgraph that acts as an independent agent and has its own states. The generator and the Deep Research agents, receive the conversation summary and quantum computing use cases from previous stages, respectively. However it is worth noting that these subgraphs also share some other states, as for example the states of the conversation messages between the user and the interview agent.

Stage 1: Interview – Scope

The foundation of any relevant use case is a deep understanding of the problem. Our initial stage is an interactive interview agent designed to map the user’s specific context.

Figure 3. Example of user interaction.

The agent in this stage contained three main nodes: get_human_feedback, conduct_interview, evaluate_interview. These nodes were connected through edges in a loop, to perform the following tasks:

  • User Data Collection: In this node the agent engages the user to collect critical data points: strategic objectives, operational challenges, existing computational bottlenecks, and unexplored opportunities. We used the CoT Prompting technique, to guide the agent in the collection of the user information. As mentioned before, this process is an iterative loop, continuing until the information collected is established for analysis. Figure 3 illustrates an example conversation in which can be appreciated how the agent scopes the user’s business. 
  • Messages evaluation: The purpose of the agent in this node is to actively guide the conversation, ensuring alignment with topics amenable to quantum computing or quantum-inspired solutions. It employs conditional logic to steer the dialogue towards quantifiable problems, such as optimization, simulation, or machine learning challenges, and is programmed to conclude interviews that diverge from these domains.
  • Brief Generation: The stage culminates in a structured “Conversation Summary,” a formalized document that serves as the input for the subsequent stage.

Stage 2: The Generator – Generation of Quantum Computing Use Cases

With a clearly defined scope, the Generator agent synthesizes this information to propose initial solutions.

  • Interview Evaluation: In this node the agent analyses the Conversation Summary input to confirm the feasibility of applying quantum computational methods. This acts as a first validation gate before generating the quantum use cases. 
  • Quantum Computing Use Cases Generation: A “Quantum Expert” LLM then synthesizes the user’s business information with its knowledge base of quantum computing algorithms. It generates a preliminary list of use cases, mapping business problems like logistics optimization to algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) or Variational Quantum Eigensolver (VQE), or material science simulations to quantum chemistry algorithms.

Stage 3: Deep Research – Autonomous, Multi-Agent Feasibility Analysis

This final stage is the centerpiece of our system: a parallelized AI multi-agent architecture, inspired by the open Deep Research architecture [8], which conducts a rigorous academic and technical investigation for each proposed use case. Its purpose is to assess the feasibility of applying quantum computing and quantum-inspired algorithms to the user’s business. This serves as a second validation gate before producing scientifically grounded quantum computing use cases.

Parallel deep research workflow using LangGraph and multi-agent AI

Figure 4. Parallel Deep Research

The parallel procedure is illustrated in Figure 4. We apply the map-reduce design pattern to branch the workflow, enabling parallel execution for each use case. The Generator agent forwards the list of use cases to the next subgraph using LangGraph’s Send object. For each use case, a specialized agent is instantiated, allowing research tasks to be carried out independently, and more efficiently, at the same time.

We apply context engineering [9] in different procedures to extract key information, keep relevant content and sources, and compress research results. We followed established guidelines for model families such as GPT-4.1[10], ensuring more effective use of the model’s capabilities. During this procedure we also applied structured outputs to maintain clear and structured data in the flow execution of the nodes. 

As seen in the third block of Figure 1, the process begins with the Deep Research Loop:

  1. The Interviewer: This new LLM interviewer acts as a Socratic inquisitor. Based on the proposed use case (e.g., “Portfolio Optimization using Quantum Annealing”) and the user’s context (e.g., a financial services firm concerned with volatility), it formulates precise research questions. For example: “What is the latest research on the performance of D-Wave’s annealers for QUBO problems with constraints similar to our user’s portfolio?”
  2. The Quantum Computing Expert: This agent takes the questions and queries both internal and external knowledge sources. To ensure the LLMs correctly understood their roles during the interview, we applied persona prompting. Specifically, we defined two personas: an Interviewer persona, as described in the previous section, and a Quantum Expert persona, which responds to all questions using the information retrieved by the tools.
  3. Database and Tools: The query is forwarded to a specialized vector database populated with embeddings derived from hundreds of recent arXiv preprints, peer-reviewed journal articles, and quantum algorithm literature. To enable this, we employ a RAG pipeline, leveraging dense vector indexes in Pinecone that support high-dimensional semantic similarity search. In parallel, the agent integrates external tools such as Tavily to perform live web searches, ensuring access to the most up-to-date scientific studies and complementing the static knowledge base with real-time information.
  4. Source Evaluation: An evaluation layer filters the retrieved sources by the quantum computing expert, ranking them based on relevance to the Interviewer query, and applicability to the user’s specific industry. Irrelevant or sources are discarded.

This loop iterates, refining the search and building a rich corpus of relevant knowledge.

Sample feasibility report for quantum computing business application

Figure 5. Example of generated use cases and feasibility reports.

  • Deep Research Evaluation. At the end of the loop, the AI multi-agent system conducts a final review, examining the collected evidence and determining the feasibility of applying the quantum use cases to the user’s business. The review applies constraint checks (e.g., data availability, problem size, hardware/runtime requirements, regulatory limits), compares against baselines, and outputs a confidence-weighted verdict. Few-shot prompting is employed to guide the evaluator by providing contrasting examples of feasible and non-feasible cases, ensuring more consistent and accurate judgments.
  • Report Writing: The process culminates in a comprehensive feasibility report (see an example in Figure 5). This is not a simple summary but a structured scientific document containing:
    • Scientific and Mathematical Basis: A brief mathematical description of the quantum computing algorithms used for the use case proposed.
    • Limitations and Risks: A clear-eyed view of current hardware limitations, algorithmic noise, and implementation challenges.
    • Conclusion: A final verdict on the feasibility and potential benefits for the user’s business.
    • Scientific Evidence and Literature Review: A curated list of supporting studies with citations.

Our tool is designed to be more than a simple tool generator; it is an automated quantum computing research assistant that empowers organizations to make informed, data-driven decisions about their entry into the quantum computing landscape.

What´s next?

We are still cooking …

At QCentroid, we have the goal of accelerating the large-scale adoption of quantum computing. We’ve got several planned and in-progress features to achieve this end-to-end workflow: from defining a use case to developing it into test-ready code. You’ll start with a simple conversation and end with a working experimental prototype, supported by tools such as a mathematical problem-definition generator, a data generator, and pseudocode-to-code generation—though that’s just the beginning; the rest you’ll have to wait and see.

(HIDDEN)

We invite you—whether you’re a scientist, a business leader, a developer,  or simply curious—to explore our tool’s capabilities and join us in refining the bridge between the quantum and classical worlds. Discover its potential now

Discover now

References

[1] “quantum-monitor-2025.pdf.” Accessed: Sep. 25, 2025. [Online]. Available: https://www.mckinsey.com/~/media/mckinsey/business%20functions/mckinsey%20digital/our%20insights/the%20year%20of%20quantum%20from%20concept%20to%20reality%20in%202025/quantum-monitor-2025.pdf 

[2] “quantum-technology-monitor-april-2023.pdf.” Accessed: Sep. 25, 2025. [Online]. Available: https://www.mckinsey.com/~/media/mckinsey/business%20functions/mckinsey%20digital/our%20insights/quantum%20technology%20sees%20record%20investments%20progress%20on%20talent%20gap/quantum-technology-monitor-april-2023.pdf?utm_source=chatgpt.com 

[3] N. Mousa and F. Shirazi, “A survey analysis of quantum computing adoption and the paradigm of privacy engineering,” SECURITY AND PRIVACY, vol. 7, no. 6, p. e419, 2024, doi: 10.1002/spy2.419. 

[4] J. Wei et al., “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models,” Jan. 10, 2023, arXiv: arXiv:2201.11903. doi: 10.48550/arXiv.2201.11903. 

[5] J. Kaplan et al., “Scaling Laws for Neural Language Models,” Jan. 23, 2020, arXiv: arXiv:2001.08361. doi: 10.48550/arXiv.2001.08361. 

[6] “The rise of ‘context engineering,’” LangChain Blog. Accessed: Aug. 28, 2025. [Online]. Available: https://blog.langchain.com/the-rise-of-context-engineering/ 

[7] P. Lewis et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” in Advances in Neural Information Processing Systems, Curran Associates, Inc., 2020, pp. 9459–9474. Accessed: Sep. 04, 2025. [Online]. Available: https://proceedings.neurips.cc/paper/2020/hash/6b493230205f780e1bc26945df7481e5-Abstract.html 

[8] “Open Deep Research,” LangChain Blog. Accessed: Aug. 21, 2025. [Online]. Available: https://blog.langchain.com/open-deep-research/ 

[9] “Context Engineering,” LangChain Blog. Accessed: Aug. 21, 2025. [Online]. Available: https://blog.langchain.com/context-engineering-for-agents/ 

[10] “GPT-4.1 Prompting Guide | OpenAI Cookbook.” Accessed: Aug. 21, 2025. [Online]. Available: https://cookbook.openai.com/examples/gpt4-1_prompting_guide 

Accelerating Quantum Readiness: QCentroid & QAI Ventures

Singapore Quantum Hackathon QAI Ventures

From Idea to Proof of Concept (PoC) in 48 Hours at

Over the past few months, QCentroid has proudly partnered with QAI Ventures to power the GenQ Global Hackathon Series. Across three high-energy events in Calgary (Energy), Geneva (Life Sciences), and Singapore (Finance), we witnessed a glimpse of the future of industry-specific AI and Quantum solutions.

These global sprints brought together hundreds of the world’s brightest students to tackle real-world complexities – from Counterparty Credit Risk and Fraud Detection to advanced Life Sciences case studies – all compressed into intense 48-hour development windows.

Why QCentroid? The Readiness Accelerator

In a 48-hour sprint, tools are either critical enablers or fatal distractions. While many companies talk about “Quantum Operations,” we know that for today’s market, the real challenge is Readiness. The success of the GenQ series hinged on one core element: eliminating the technical friction that usually stalls innovation.

Zero-Friction Onboarding: Ending Quantum Computing “Dependency Hell”

In a time-compressed environment, losing hours to environment setup is a project-killer. Our platform provided a genuine “login-and-code” experience. By pre-configuring complex quantum SDKs and environments, we ensured that participants spent zero minutes on troubleshooting and 100% of their time on solving the problem.

Enterprise-Grade Stability at Scale

Supporting three distinct, high-intensity sprints globally served as more than just a collaboration—it was an Enterprise Hardening exercise. By maintaining a seamless infrastructure across North America, Europe, and Asia, we proved that the QCentroid environment is ready for the most demanding corporate “sandboxes,” ensuring identical conditions for every team, regardless of location.

The Universal Adapter for Hybrid Workflows

QuantumOps served as a neutral gateway. Teams accessed five different hardware providers throughout the tour—IonQ, Microsoft, Quantinuum, QuEra, and IQM—allowing them to rapidly iterate and benchmark solutions and quantum algorythms across the world’s leading quantum backends within a single interface.

Key Capabilities: Empowering the Business-First Journey

The students’ success was directly linked to the features that bridge the gap between business logic and technical execution:

  • The Power of the Launchpad: Participants overwhelmingly chose our built-in coding environment over external IDEs. This confirms that an integrated, low-friction workspace is the fastest route to a working prototype.
  • The Readiness Catalog: To accelerate prototyping, students leveraged a pre-loaded catalog of solvers and templates. This “Quantum Program Builder” approach allowed teams to start with a foundation rather than a blank page.

The Numbers: Impact at Scale

The data confirms the velocity achievable when you remove technical barriers:

  • Global Reach: 3 Continents (North America, Europe, Asia).
  • 100% PoC Velocity: Every single winning team successfully deployed a working Proof of Concept (PoC) on the platform within the 48-hour window.

Looking Ahead: Your Roadmap to Quantum Readiness

While supporting emerging talent is rewarding, these hackathons served as a crucial stress test for our platform. The insights gained from these high-pressure environments have directly informed our roadmap, hardening our connectors (including our latest IQM integrations) and refining our “Business-First” agentic flows.

The platform that can reliably support global, high-intensity hackathons is ready for your enterprise challenges.

Is your organization ready to move from “Quantum Hype” to “Quantum Ready”? Contact us today to see how QCentroid can accelerate your transition from a business idea to an industry solution.

Real-World Quantum Wins of 2025 (So Far): What Business Leaders Should Pay Attention To

At QCentroid, we translate these breakthroughs into strategic insight for decision-makers. Here’s what you should know if you’re exploring how quantum can impact your business.

TL;DR

  • 2025 has delivered the first field-tested, future revenue-relevant pilots. Every win you’ll read about is already running inside a hybrid classical/quantum workflow that organisations can trial today.
  • Early benefits come from software layers and domain-tuned algorithms, not raw qubit numbers. Vendors that pair hardware with robust error-mitigation or “quantum-inspired” solvers are securing the clearest ROI. 
  • Headlines quoting “50×–2 500× speed-ups” usually compare against generic reference codes. Benchmark each solution on your own data and against your best classical heuristics before green-lighting full projects. 
  • A pragmatic roadmap is to pinpoint one painful computational hotspot, pilot a hybrid solver there, and upskill staff while hardware matures. That way you capture near-term upside without over-committing capital.

Logistics & Supply Chain

Q-CTRL (London Rail Scheduling) 

In a June 2025 case study, Q-CTRL’s Fire Opal solver optimized train schedules at London Bridge station. Running on classical hardware but leveraging quantum principles, Q-CTRL used real train data; their hybrid quantum-classical solver handled 26 trains over 18 minutes with a 6× larger problem size than bare quantum hardware, and achieved ~2,500× lower compute cost than standard quantum methods. 

Q-CTRL reports its approach delivered approximate solutions faster and with higher quality than alternatives, and expects this quantum solver to outperform classical schedulers by ~2028

Takeaway: Even before full-scale quantum hardware is in use, quantum-derived algorithms are showing measurable performance boosts in real-world transport systems.

Industry nuance: the 2 500× figure is a comparison with earlier quantum baselines, not with tuned OR-Tools deployments currently in production. The study demonstrated a record size of the problem that can be solved on a quantum hardware – albeit it is still orders of magnitude smaller than a full-day timetable. Wait for hardware to catch up, software has proven its worth. 

Q-CTRL (Airbus/BMW Supply Chain Optimization) 

In a late-2024 industry challenge, Q-CTRL’s solver addressed a real aircraft supply-chain problem (Airbus/BMW quantum mobility quest). The quantum computing-based solution managed multi-site manufacturing, logistics, and carbon constraints for aircraft parts. The challenge’s exact solution would take classical methods “tens of thousands of years” to find, but the quantum solver produced high-quality schedules in practical time. 

This case demonstrates hybrid quantum computing enabling solutions for complex logistical optimisation, with performance already comparable to top classical heuristics and a clear path to further gains as hardware improves.

What to remember: Supply chains are low-margin, high-complexity systems. Quantum-ready AI is already achieving competitive results—today—making this a space to watch for early adoption.

Industry nuance: A Fraunhofer follow-up showed that a GPU-based simulated-annealing code reached similar schedule quality in roughly the same wall-time. Enterprises should therefore run side-by-side trials before assuming an intrinsic quantum edge.

Finance & Routing

IBM+Kipu (Finance/Routing Optimization)

Kipu Quantum and IBM demonstrated that gate-model quantum computing outperforms classical computing in high-order binary optimization. Running on a 156-qubit IBM Q processor with Kipu’s BF-DCQO (Bias‑Field Digitized Counterdiabatic Quantum Optimization) algorithm, they solved finance/routing (HUBO) problems in ~0.5 seconds, whereas the best classical solver (IBM CPLEX) took ~30–50 seconds on the same tasks. 

This corresponds to up to 80× faster performance for quantum on those instances, marking a concrete speedup in portfolio and logistics problems without full error correction. 

Business relevance: These are the same classes of problems faced in delivery route planning, fund rebalancing, and large-scale resource allocation. Quantum is beginning to tackle them at functional scales.

Industry nuance: The published HUBO instances were engineered to map neatly onto IBM’s heavy-hex lattice. When queueing and post-processing are included, the end-to-end operation takes 70s, and the gap narrows to ‘only’ 4 times (still good). Firms should benchmark with their own data and a fully tuned CPLEX or GPU solver.

Chemistry, Pharma & Materials

IonQ + AstraZeneca (Quantum-Accelerated Chemistry) 

In June 2025, IonQ announced a collaboration with AstraZeneca, AWS, and NVIDIA to speed up drug-chemistry simulations, accelerating a critical reaction mechanism.

They simulated a Suzuki–Miyaura drug synthesis reaction using IonQ’s QPU in a hybrid workflow with NVIDIA GPUs. The result was a 20× reduction in time-to-solution (months to days) compared to previous classical simulations, while maintaining high accuracy. 

This large-scale end-to-end demonstration shows that quantum acceleration can dramatically reduce the runtime of high-precision molecular modeling (e.g., catalysis, materials) that were previously slow on classical HPC.

Impact: This points to a near-term business advantage in pharmaceuticals – reducing time-to-discovery in multi-billion-dollar drug pipelines.

Industry nuance: The 20× shrink is measured against previous implementations using coupled-cluster workflows; modern DFTB on GPUs can reclaim much of that gap. Hybrid quantum runs still require classical re-validation before clinical spend.

IonQ + Kipu (Protein Folding/Optimization) 

Also in mid-2025, IonQ and Kipu solved the most complex protein-folding and optimization problems yet on a quantum computer. Their joint work folded a 12-amino-acid protein (a 3D folding instance) and solved dense QUBO/HUBO problems up to 36 qubits, achieving optimal solutions in all test cases. While classical benchmarks weren’t reported, this industry record shows quantum computers tackling hard bio/chemistry structures. It exemplifies QC’s progress toward drug design and complex optimization, where classical simulation or exact solution is intractable.

How can we use it: Drug discovery edge: Solving protein folding—essential for understanding peptide dynamics and drug-target interactions—on quantum hardware signals real commercial progress in accelerating molecular design. Optimization crossover: The same core algorithm solved dense MAX-SAT and spin-glass instances—problems analogous to real-world challenges in logistics planning, finances, optimization, and AI.

Industry nuance: The lattice-model peptide (≤ 192 bits) is a simplified toy system; tools such as Rosetta or AlphaFold solve it in milliseconds. Treat this as a scientific milestone that foreshadows future drug-scale work.

D-Wave (Magnetic Materials Simulation) 

In the March 2025 demonstration, D-Wave reported first “quantum supremacy” on a useful problem – their annealing computer simulated complex magnetic material dynamics in minutes, a task that the U.S. DOE’s Frontier supercomputer would take ~1 million years and years of electricity to do classically. 

This result (published in Science) shows quantum annealers can now outperform classical supercomputers by orders of magnitude on certain physics simulations, validating QC speedups on a real-world science problem.

Why it matters: Advanced materials R&D—for batteries, semiconductors, or magnets—could benefit from these kinds of large-scale simulations much sooner than previously expected.

Industry nuance: Subsequent GPU studies claim to reproduce much of the result for smaller lattices and shorter evolution times; the “million-year” figure applies to the hardest biclique instances only. If you model magnetic glasses, start a pilot; otherwise view this as proof that narrow quantum advantage is emerging.

University of Sydney (Chemical Reaction Dynamics) 

For the first time (May 2025), Sydney chemists simulated real molecular dynamics on a trapped-ion. They modeled ultrafast light-driven reactions of three real molecules with a resource efficiency ~10<sup>6</sup>× higher than conventional quantum methods. Classical supercomputers can only compute static molecular properties in these cases; this new QC method captured the full time-dependent chemistry. 

Industry relevance: This breakthrough aids in solar energy, photonics, and materials design. It’s also a sign that hybrid quantum AI will be a go-to method in complex molecular simulations.

Industry nuance: Scaling beyond six atoms is an open research question; commercial impact is likely 3-5 years away.

Data Science & Machine Learning

CSIRO (Quantum Machine Learning for Big Data) 

Australia’s national science agency CSIRO, demonstrated quantum-enhanced data processing for a real sensor dataset. Using a “quantum kernel PCA (Principal Component Analysis)” algorithm, they compressed and analyzed environmental sensor data (groundwater chemistry) with improved compactness and accuracy compared to classical methods. 

This case suggests potential for QML to handle other high-dimensional, information-rich data streams like real-time traffic, healthcare, or energy data analysis, compressing massive datasets without losing key information.

Translation: As industrial systems digitize, quantum-enhanced data analysis could soon offer a competitive edge in preventive maintenance, cybersecurity, and more.

Industry nuance: The study is not about speed gains but the applicability of QML to certain problems; it was carried out in a simulator of a quantum computer and may require real quantum hardware to further develop to replicate the result.

USC/D-Wave (Optimization of Spin-Glass Problems) 

USC researchers (Phys. Rev. Lett.) used a D-Wave quantum annealer with 1,300 error‑protected qubits to solve hard spin-glass optimization tasks. This quantum annealing setup outperformed the best classical algorithm (parallel tempering) on time-to-solution for near-optimal results, demonstrating a “quantum advantage” in approximate optimization. 

In tests, the quantum device achieved solutions in seconds that the classical solver required much longer to reach, highlighting tangible speed/accuracy gains from quantum computing.

Published in Science, this achievement is being called the first-ever demonstration of quantum supremacy for a useful, application-relevant problem. 

However, the announcement sparked a scientific exchange: classical research teams countered that similar tasks could be solved with modern algorithms on GPUs or HPC clusters. D‑Wave’s CEO responded that these critiques overlooked critical variables, including larger lattice structures, longer evolution times, and multiple observables, which their proprietary methods explicitly covered.

Takeaway for business: This milestone reinforces that quantum annealers are now solving real-world scientific problems, not just toy benchmarks. If your industry involves advanced materials or high-dimensional modeling, it’s time to explore quantum‑accelerated tools, but also to benchmark them carefully against classical alternatives.

Industry nuance: Later GPU-based “population-annealing” narrowed the gap on some—but not all—instances. The practical advantage is therefore instance-dependent, and direct pilots are recommended.

The cases above aren’t theory, they address practical problems, many using quantum hardware or emulators available in 2025. While general-purpose quantum computing remains on the horizon, these milestones show that a focused, hybrid quantum edge can already be harnessed – provided you validate it rigorously on your workloads. Thus bringing us to the state of narrow quantum advantage. 

Still wondering how to turn the headlines above into a concrete advantage for your organisation? If you’re ready to move from reading about quantum to running it, let’s talk. Book a 30-minute discovery session with one of our solution architects and receive a tailored readiness roadmap within five business days. Early movers are already mapping their first pilots—make sure you’re on that list.

→ Secure your slot here ›

An AI Use Case: Using QCentroid Platform to Benchmark AI-Generated Optimization Algorithms

In today’s fast-paced world of software development, AI code generation tools like OpenAI’s ChatGPT, DeepSeek, Grok, and others have become powerful companions for developers. These tools can quickly generate entire algorithms from natural language prompts, saving time and sparking innovation. But not all AI-generated code is created equal. Performance, accuracy, and reliability can vary significantly between providers and even between different prompts to the same model.

At QCentroid, we believe in harnessing the power of advanced computing—from quantum to AI—to accelerate real-world problem solving. One of the increasingly valuable use cases we’re seeing is using the QCentroid Platform to evaluate and benchmark AI-generated optimization algorithms.

In this post, we’ll walk through how developers can use our platform to compare the quality and performance of code generated by different AI providers, helping them make better, data-driven decisions.

The Challenge: Comparing AI-Generated Algorithms

Let’s say you prompt three different AI models—OpenAI, Grok (xAI), and DeepSeek—to generate a Python implementation of a classical optimization problem such as the Knapsack problem, portfolio optimization, or route scheduling.

You’ll probably get three different implementations:

  • Different coding styles and algorithmic approaches
  • Varying levels of optimization
  • Some may even have runtime bugs or miss-key constraints

Now, how do you determine which is better? This is where QCentroid comes in.

Using the QCentroid Platform for Code Benchmarking

The QCentroid Platform provides a cloud-based environment specifically designed for benchmarking optimization algorithms, whether they’re classical, quantum-inspired, or generated by AI. Here’s how a developer can use it to compare AI-generated solutions.

1. Upload and Register the Algorithms

You just have to push the algorithms generated by the various AI tools to Git repositories, connect these repositories to the QCentroid platform. Each of these algorithms is what we call a solver and it may include metadata like:

  • AI provider (e.g., ChatGPT, Grok, etc.)
  • Prompt used (for reproducibility)
  • Programming language and dependencies
  • Expected input/output behavior

2. Define Benchmarking Parameters

Next, you can configure the benchmarking criteria:

  • Execution time (average runtime across test cases)
  • Accuracy (based on problem-specific metrics, like optimality gap or constraint violations)
  • Stability (whether the algorithm completes without errors)
  • Resource usage (CPU time, memory consumption, etc.)

You can also define test datasets or input configurations for consistent evaluation.

3. Run Benchmarking Jobs

Then, you can run benchmarking jobs on the platform, and it will automatically:

  • Set up isolated containers for each algorithm version
  • Run multiple test iterations to account for randomness or edge cases
  • Collect performance and execution metrics
  • Detect crashes or errors

This ensures fair and reproducible evaluation across different implementations.

4. Compare Results Visually

The QCentroid Dashboard displays comparative analytics for all AI-generated algorithms. You’ll get:

  • Heatmaps, plots and radar charts to visualize trade-offs (e.g., faster vs. more accurate)
  • Logs and tracebacks for debugging runtime errors
  • Ranking based on customizable scoring functions (e.g., 50% weight on speed, 30% on accuracy, 20% on code robustness)

These insights make it easy to decide which AI-generated version is most suitable for production, experimentation, or further refinement.

What Makes This Unique?

Unlike typical Jupyter or IDE-based testing, QCentroid centralizes and automates the entire benchmarking workflow. This allows:

  • Team collaboration: Results are sharable across your team, with notes and reviews.
  • Repeatability: You can re-run the same benchmarking job months later with updated models or test data.
  • Transparency: You’ll know exactly where each AI model excels—or fails.

Extending to Hybrid & Quantum Benchmarks

The real magic comes when you combine classical benchmarking with quantum or hybrid methods. For example, a developer could:

  • Use AI-generated classical baselines
  • Compare them against quantum-inspired optimization algorithms available through QCentroid
  • Understand where quantum methods may offer speedups or better scaling

This opens the door to multi-paradigm performance testing, which is becoming increasingly relevant in finance, logistics, and R&D sectors.

Final Thoughts: From Prompt to Production

As AI becomes a routine coding partner, the ability to evaluate and trust what it generates is crucial. The QCentroid platform allows teams to move from “prompt engineering” to “production engineering”—by giving them the tools to benchmark, compare, and select the best AI-generated algorithms.

If you’re exploring ways to integrate AI and quantum into your development workflow, or want to validate the code your AI assistant just handed you, QCentroid has your back.

Unlocking new opportunities: Quantum Computing-as-a-Service for Industry Hubs and Associations

Industry hubs and associations are constantly looking for innovative ways to provide value to members, drive engagement, and ensure the financial sustainability of the organization. Quantum Computing represents a transformative opportunity that could position your association or hub as a leader in this cutting-edge technology, while creating significant new revenue streams.

Let’s explore how integrating a Quantum Computing as a Service (QCaaS) platform, like QCentroid’s, can enable your association to stay ahead of the curve, enhance your value proposition, and attract a wider base of members.

The Case for Quantum Computing in Industry Hubs

Quantum computing is no longer the stuff of science fiction. Its potential to revolutionize industries is already being explored in sectors like finance, healthcare, energy, and logistics. From solving complex optimization problems to accelerating drug discovery, quantum technologies promise breakthroughs that were previously unimaginable.

For your members, this means access to tools that can:

  • Optimize supply chains and logistics, saving costs and improving efficiency.
  • Revolutionize materials science for manufacturing and R&D.
  • Enhance risk analysis and fraud detection in financial services.

By offering QCaaS as part of your association’s membership benefits, you’re not just providing access to a powerful tool; you’re positioning your organization as an enabler of cutting-edge innovation.

Real-World Success: How Associations Leverage SaaS Platforms

Many associations have successfully integrated SaaS (Software as a Service) platforms to enhance member value. Consider examples like:

  • Technology & Services Industry Association (TSIA): Provides members with exclusive data analytics tools, business frameworks, and advisory services tailored to their industries.
  • Cloud Software Association (CSA): Offers members resources and networks to develop and scale SaaS partnerships.

These examples demonstrate that associations can thrive by offering their members tailored technological solutions. By following their lead and adopting QCaaS, you can replicate this success in the quantum computing domain.

Unlocking Value for Your Members

Introducing QCaaS can make your association indispensable to your members. Here’s how:

  1. Exclusive Access to Quantum Resources: Partner with a QCaaS provider like QCentroid to offer your members privileged access to quantum computing platforms. This could include discounted usage rates, tailored industry solutions, or access to a shared quantum environment for experimentation and innovation.
  2. Educational Opportunities: Offer training sessions, webinars, and certifications to upskill your members on quantum computing. This not only empowers them but also positions your association as a thought leader.
  3. Collaborative Innovation: Foster collaboration among your members by creating quantum-focused working groups or R&D initiatives. This creates networking opportunities while driving meaningful advancements.

Creating a New Revenue Stream

Incorporating QCaaS isn’t just about providing value; it’s also a smart business decision. Here’s how it can contribute to your bottom line:

  • Subscription Models: Charge members a premium for access to QCaaS tools and services.
  • Educational Programs: Monetize training and certification programs tailored to quantum computing applications in specific industries.
  • Consulting Services: Leverage your association’s expertise to provide members with bespoke consulting on how to integrate quantum solutions into their businesses.

Why Start Now?

The quantum computing revolution is already underway. Early adopters will have a significant competitive advantage, and your association has the chance to lead the charge. By offering QCaaS to your members, you position your organization as a pioneer in enabling innovation and as an indispensable resource for businesses preparing for the quantum era.

Next Steps

Ready to bring quantum computing to your association? Here’s how to get started:

  1. Assess Member Needs: Survey your members to identify where quantum computing could have the most impact.
  2. Partner with a QCaaS Provider: Collaborate with a trusted platform like QCentroid to tailor solutions for your members.
  3. Develop a Rollout Plan: Create a strategy for launching QCaaS as a member benefit, including training, access, and marketing initiatives.

By taking these steps, you’ll ensure that your association stays relevant, innovative, and indispensable to your members. Quantum computing isn’t just the future—it’s an opportunity you can seize today.

QCentroid at ETH Quantum Hackathon 2025

Quantum Portfolio Optimization Challenge Recap

Last weekend, QCentroid participated in the ETH Quantum Hackathon 2025, an exciting gathering of bright young minds from around the world, all coming together to push the boundaries of what’s possible with quantum technology. As proud sponsors of one of the official hackathon challenges — the Quantum Portfolio Optimization challenge — we witnessed firsthand the creativity, energy, and technical talent of the next generation of quantum developers.

Hosted at ETH Zurich and organized by the Quantum Engineering Commission, with the support of AMIV and VSETH, the ETH Quantum Hackathon is one of the leading events in the global quantum community. The 2025 edition brought together students, researchers, and early-career professionals for three days of hands-on building, experimentation, and innovation, with participants tackling real-world quantum problems across various industries.

The QCentroid Challenge: Quantum Portfolio Optimization

Key Insights from the Quantum Portfolio Optimization Challenge

Our challenge track, we asked participants to design and implement a quantum routine inside a portfolio optimization algorithm based on real-world datasets. The goal was to allocate investments across a set of assets, using the unique computational power of quantum devices to explore the solution space more efficiently than classical methods can. They could select where to put the quantum routine (asset selection, expected return guess, asset allocation, etc) and, using classical parts of well known algorithms, improve the behaviour of one or more steps of those algorithms using classical parts of well-known algorithms.

Portfolio optimization is a natural use case for quantum computing, as it involves solving complex optimization problems that quickly become intractable as the number of variables grows. We challenged participants to go beyond basic implementations and think creatively about how quantum mechanics could be leveraged to find optimal investment strategies.

To support the teams, QCentroid provided guidance, mentorship, and access to documentation and resources. Participants used their development environments along with different simulators, ensuring accessibility and flexibility across various teams.

Five teams took on the QCentroid challenge, each bringing their unique approach. The diversity of ideas and strategies showcased the richness of quantum algorithm design, reminding us of how early we still are in this field and how much potential lies ahead.

The winning team stood out with a particularly elegant approach to the problem. Using creativity, they transformed the problem into a well-known problem and solved it with a modified fundamental algorithm. They added some real-world constraints not to increase difficulty but to ease the solution. 

Other teams solved the problem with approaches from graph theory to variational solutions with impressive theoretical results, aiming to explore how they could perform in the real world. 

This kind of insight and originality is exactly what we hoped to see when designing the challenge. It confirmed that quantum-native thinking — building algorithms that align with how quantum systems behave — can offer new avenues for solving longstanding problems in finance and beyond.

Why Quantum Hackathons Matter for the Future of Quantum Computing

Beyond the technical solutions, what impressed us most was the participants’ passion, energy, and collaborative spirit. Teams navigated quantum concepts, implemented algorithms, tested code, and presented thoughtful final solutions. It was a reminder that the future of quantum computing is not just about hardware or software — it’s about people. And the future is bright.

Being part of this event reaffirmed QCentroid’s mission to make quantum and advanced computing accessible and useful for enterprises of all sizes. Through our QuantumOps platform, we aim to reduce the barriers to entry, helping organizations explore, integrate, and scale quantum solutions in a way that aligns with their business goals.

Events like the ETH Quantum Hackathon show we’re not alone in this mission. The momentum is building, and a new wave of quantum developers is emerging — one that is diverse, interdisciplinary, and deeply motivated to use this powerful technology for good.

Looking Ahead

We sincerely thank ETH Zurich, the organizers — Quantum Engineering Commission, AMIV, and VSETH —, professors, other sponsors, and, of course, the amazing participants who joined our challenge track. We’re excited to keep supporting initiatives like this and building bridges between academia, industry, and the quantum community.

If you’re curious to learn more about the Quantum Portfolio Optimization challenge or want to dive into some of the winning solutions, you can visit the official hackathon page here:
ETH Quantum Hackathon 2025

Let’s keep pushing boundaries, exploring new ideas, and making the quantum future a shared reality.

QCentroid: Powering Quantum Hub Innovation

From Quantum Access to Quantum Acceleration: Empowering Your Hub with QCentroid

Quantum Hubs like yours are the crucibles for the next wave of technological advancement. Your mission is critical: to cultivate innovation, nurture talent, and drive real-world quantum applications. But in today’s rapidly evolving quantum landscape, simply providing access to resources isn’t enough. To truly thrive and lead, your Hub must become an accelerator—a place where quantum potential is rapidly transformed into tangible solutions. QCentroid’s Quantum Hub acceleration platform is engineered with this precise goal in mind: to transform your Hub into a premier, high-velocity destination for quantum breakthroughs.

Is Your Quantum Hub Hitting These Roadblocks to Real Impact?

Managing a Quantum Hub effectively means confronting the dynamic, real-world scenarios that arise as you build a cutting-edge ecosystem. Do any of these challenges sound painfully familiar?

  • Beyond Reselling: Is Your Hub Offering Real Value or Just Access?
    Partner agreements are a start, but are members still bogged down by complex interfaces and PoC setups? If setup outpaces breakthroughs, your Hub’s unique accelerator value diminishes.
  • Resource Blind Spots: Lacking Clear Cost and Usage Visibility?
    Lacking a unified view of multi-partner resource use? This hinders cost advice, budget clarity, and efficient management of your Hub’s overall computational draw.
  • Event Execution Headaches: Hackathons Becoming Logistical Nightmares?
    Are your showcase hackathons bogged down by backend integrations, complex benchmarking, and inconsistent tooling, turning them into support headaches for your team?
  • Demonstrating Value: Struggling to Attract Users & Showcase Impact?
    Is operational friction slowing member results, hindering user attraction, and corporate engagement? Without tangible outcomes, proving your Hub’s value to sponsors becomes a major hurdle.

These aren’t isolated incidents; they are the daily realities for ambitious Quantum Hubs striving to deliver exceptional value. They scream for a platform that doesn’t just grant access, but actively dismantles complexity, ignites agility, and empowers both your members and your management team.

QCentroid: Your Hub’s Quantum Acceleration Engine

QCentroid delivers a comprehensive, vendor-agnostic QuantumOps platform specifically engineered for Quantum Hubs. We provide a managed environment that equips your members with universal access (to your pre-contracted partner resources) and advanced tools, enabling them to transform months of complex work into days of focused innovation. This approach ensures you maintain effortless control, achieve your strategic objectives, and amplify your Hub’s impact.

With QCentroid, your Hub can offer:

  • Unified, Vendor-Agnostic Access: Empower members with the freedom to utilize diverse QPUs and simulators from your Hub’s partners through a single, intuitive interface. They always have the right tool for their specific challenge, without the learning curve of multiple platforms.
  • Accelerated Development & Benchmarking: Leverage our integrated Launchpad and powerful analytical tools to shorten development cycles dramatically. Move from concept to validated result significantly faster.
  • Simplified User Management: Streamline onboarding, permission setting, and project organization, drastically reducing administrative overhead for your team.
  • Granular Cost & Usage Control: Implement transparent, effortless budget management and precise resource allocation across projects and users—all within one platform. Gain clarity on spending and optimize resource utilization.
  • A Premier Platform for High-Impact Events: Seamlessly host hackathons, workshops, and quantum challenges. Our platform handles the backend complexity, so you can focus on boosting engagement, fostering collaboration, and enhancing your Hub’s visibility.
  • A Low Technical Barrier to Entry: Benefit from minimal setup requirements on your end, complemented by low-code features and straightforward integration, making quantum exploration accessible to a broader range of your members.
  • (Optional) White-Label Branding: Present the QCentroid platform as your Hub’s own distinct, cutting-edge offering, reinforcing your brand and leadership.

Launch Your Hub’s Accelerated Quantum Offering – Quickly and Effectively

Integrating QCentroid is designed to be a smooth, efficient process:

  1. Efficient Deployment: Choose the model that works for you—SaaS or on-premise. Minimal technical preparation is required from your team. We handle the heavy lifting.
  2. Seamless Member Onboarding: Grant access, define permissions, and allocate budgets with straightforward administrative tools for all resources integrated into the platform. Get your members up and running in no time.
  3. Drive Accelerated Innovation: Members can immediately begin to build, test, benchmark, and achieve results significantly faster. You gain a centralized overview for monitoring progress, managing resources effectively, and showcasing impact.

The QCentroid Advantage: A Clear Path to Enhanced Hub Performance

Adopting QCentroid isn’t just an operational upgrade; it’s a strategic move to elevate your Hub’s entire value proposition:

  • Attract & Retain Top Talent and Innovators: Offer a best-in-class, dynamic, and fast-paced quantum environment that becomes a magnet for leading researchers, developers, and quantum-curious corporates.
  • Boost Your Hub’s Reputation & Secure Engagement: Establish your Hub as a leader in applied quantum computing by showcasing rapid, verifiable results and successfully hosting prominent quantum events. Utilize our proven success stories and your members’ accelerated progress to build unwavering confidence with stakeholders and attract more users.
  • Maximize Operational Efficiency & ROI: Substantially reduce the infrastructure burden, technical overhead, and day-to-day complexity associated with managing access to diverse, integrated quantum resources. Free up your team to focus on strategic growth, not troubleshooting.
  • Future-Proof Your Hub’s Offering: Stay at the forefront of the quantum revolution with a flexible, vendor-agnostic platform that adapts to the evolving quantum landscape and allows you to easily incorporate new partners and technologies as they emerge.

QCentroid provides the essential tools and framework for your Hub to become a true catalyst for quantum breakthroughs, offering clear, measurable, and rapid upsides.

Proven in Leading Quantum Ecosystems

QCentroid is the trusted platform for organizations committed to making a significant, measurable quantum impact:

  • GenQ for QuantumCity: Powered a high-engagement quantum hackathon with 18 teams, significantly elevating QuantumCity’s visibility and serving as a foundational point for their ecosystem development. The result? Increased community engagement and a clear demonstration of quantum capabilities.
  • BIQAIN (Basque Quantum Hub): Selected QCentroid to deploy and customize its platform, providing a solution catalog and streamlined access to hardware from 12 hub partners. This accelerated quantum adoption and attracted new corporate members to their burgeoning ecosystem.
  • Moody’s Analytics: Utilized QCentroid (QuantumOps) to accelerate internal algorithm development and prototyping. Crucially, they also leveraged it to offer a customer-facing distribution platform for their innovative quantum-powered financial solutions. This translated to faster R&D and new commercial opportunities.

Ready to Transition from Fragmented Access to True Quantum Acceleration?

Move beyond merely providing siloed access to quantum resources. The time to accelerate innovation is now. Equip your members with the unified, powerful platform they need to achieve groundbreaking results faster. Solidify your Quantum Hub’s position as an indispensable leader in applied quantum development.

Discover how QCentroid can empower your Hub to revolutionize your quantum strategy.

  • Schedule Your Personalized Demo: See the platform in action and discuss our flexible partnership models tailored for Quantum Hubs.
  • Download Our Hub Solutions Brief: Get comprehensive details on how we transform Hub operations and outcomes.
  • Explore Partner Success Stories: See the tangible results QCentroid delivers for ecosystems like yours.

Schedule a demo with our team and check our Hub Solutions Page.

Partner with QCentroid and unlock the full, accelerated potential of your quantum ecosystem.

QCentroid at Seoul’s Quantum Finance Forum | Quantum computing in finance

An impactful week for QCentroid in Seoul! 🇰🇷

Our Co-founder, Antonio Peris, presented at the Quantum for Finance Forum, sharing how QCentroid’s orchestration platform unlocks quantum and advanced computing for practical enterprise use.

Antonio Peres, Quantum computing in finance, Seoul's Quantum Finance Forum

Key Insights from the Forum:

  • Quantum is Moving to Practice: Leading financial institutions like JPMorgan Chase and HSBC are not just exploring theory. They’re actively piloting quantum solutions for complex risk modeling and portfolio optimization. This signals a clear shift: quantum is becoming a tool for today, not just tomorrow.
  • Addressing Real-World Financial Challenges: Discussions highlighted quantum’s potential to tackle previously intractable problems in finance. Think more accurate fraud detection, hyper-personalized financial products, and significantly faster trade settlements. The impact? Enhanced efficiency, reduced risk, and new revenue streams.
  • Cross-Sector Collaboration is Key: The strong presence of finance leaders, IT experts, researchers, and policymakers underscored a vital point: building a robust quantum ecosystem requires diverse expertise and shared learning. This collaborative spirit is crucial for accelerating adoption and navigating the complexities of this new frontier.

Korea’s Quantum Leap – The Opportunity:

  • Strategic Advantage: Korea’s focused approach to quantum, backed by government initiatives and dynamic domestic champions like SDT Inc., offers a unique opportunity. By starting with a strategic vision, Korea can avoid early adopter pitfalls and build a highly competitive, quantum-ready financial sector.
  • Building Future-Proof Infrastructure: The discussions emphasized creating a foundational quantum infrastructure now. This means Korean industries can leapfrog older technologies, embedding quantum capabilities directly into their core operations for a lasting competitive edge.

QCentroid’s Role – Enabling Practical Quantum Adoption:

  • Learning from AI/ML’s Journey: The path to successful AI adoption taught us valuable lessons about the need for standardization, clear ROI demonstration, and user-friendly platforms. QCentroid applies these learnings, simplifying quantum access and integration, making it less of a research project and more of a business solution.
  • Orchestration is Non-Negotiable: As quantum hardware and algorithms diversify, an effective orchestration platform becomes essential. It allows businesses to benchmark different solutions, integrate with existing IT, and manage complex workflows. QCentroid provides this critical layer, ensuring enterprises can harness the best of quantum and keep track of breakneck technology progress.
  • Fostering Collaborative Ecosystems: Our platform is designed to support both private enterprise initiatives and broader collaborative projects, like those seen with @Moody’s or BIQAIN (Basque Quantum Hub). This flexibility helps accelerate innovation across the entire Korean quantum landscape.

A sincere thank you to the organizers – the Ministry of Science and ICT, SDT Inc., Future Quantum Convergence Forum (FQCF), KIST, and the Telecommunications Technology Association – for orchestrating such a pivotal event. Special appreciation to Natasha Kovacs for her outstanding efforts. It was also a pleasure to share the stage with experts and practitioners from JPMorgan Chase, HSBC, Deloitte, Opetek, ETRI, and @Yonsei University, and to engage with all the participants.

QCentroid is enthusiastic about contributing to Korea’s vibrant quantum future and quantum computing in finance. The potential for innovation and growth is immense, and we are ready to collaborate.

QuIC’s Recommendations for Europe’s Quantum Strategy: What It Means for Business (And Why You Should Care)

The European Quantum Industry Consortium (QuIC) has just published its recommendations for the EU’s Quantum Strategy, a document packed with insights, warnings, and, most importantly, opportunities. Carlos Kuchkovsky, CEO of QCentroid, has been one of the key contributors to this work, leveraging his experience as the former Head of Innovation at BBVA, as well as leading the work of the European Quantum Flagship on benchmarking and standardization. As he puts it in the recommendations, addressing organizations across Europe:

The challenge isn’t whether Quantum Computing will be relevant, but how fast companies can adapt.

But before you assume this is just another policy paper, slow down and take a look at our breakdown because details matter at this time. If you’re leading a company in finance, healthcare, logistics, or any data-heavy industry, what happens in quantum doesn’t stay in quantum. It’s about to change the way you do business.

Key Takeaways from the QuIC Report: What Matters for Business?

1. QuIC Recommends Europe Build Its Own Full-Stack Quantum Computer (Because Who Likes Dependencies?)

QuIC advises Europe to push for a “Made in Europe” quantum stack—hardware, software, and everything in between. The goal? Less reliance on non-European tech, more strategic independence.

Why you should care: Right now, quantum computing breakthroughs often come from the US, China, or Australia, for example. A strong European ecosystem means local businesses get better access, potentially with government-backed incentives. If you’re in an industry that needs high-powered computation (finance, logistics, pharmaceuticals), this is good news.

2. A Stronger Quantum Supply Chain (So We Don’t Have Another Chip Crisis, But With Qubits)

QuIC stresses the need for Europe to strengthen its quantum supply chain—funding research, securing raw materials, and creating a resilient network.

Why you should care: A stable supply chain means fewer bottlenecks when your business is ready to experiment with quantum solutions. It also means better pricing and competitive access to quantum hardware, reducing reliance on non-EU providers.

3. Making Europe a Quantum Investment Hotspot (Translation: Money Talks, Let’s Keep It Here)

QuIC’s recommendation? More incentives, better funding models, and smoother regulations to make Europe a more attractive place for quantum startups and investors.

Why you should care: If you’re considering dipping your toes into quantum, either through partnerships or direct investment, Europe is working on making it easier and more lucrative. Expect more accelerators, funding pools, and potential tax benefits.

4. Quantum Standards & Benchmarks (Because Right Now, It’s a Bit of a Wild West)

Quantum computing today is like the early days of AI—everyone’s hyped, but there’s little standardization. Europe wants to lead the way in setting benchmarks for performance, security, and interoperability.

Why you should care: If your company is considering quantum tech, clear standards will help you compare solutions apples-to-apples rather than trying to decode marketing jargon from different vendors. It also makes integration with existing IT infrastructure smoother. An insider tip: you can already compare many of the quantum solutions from various providers head-to-head with the QCentroid platform. 

5. Turning Intellectual Property into Revenue (Instead of Just Cool Science)

Europe has world-class quantum research but has historically lagged in commercialization. The strategy aims to change that by improving how quantum intellectual property (IP) is protected and monetized. QuIC recommends that Europe focus on better protecting and commercializing quantum intellectual property.

Why you should care: More structured IP policies mean that investing in quantum won’t just be for big tech players. Mid-sized enterprises and even non-tech companies can find commercial angles, whether through licensing, collaborations, or applied research projects.

6. Building a Quantum Workforce (Because Someone Has to Understand This Stuff)

QuIC calls for major investment in education, training, and reskilling initiatives to ensure Europe has enough quantum-literate professionals—not just scientists, but also business leaders who know how to implement the tech.

Why you should care: Finding skilled quantum professionals today is like trying to hire blockchain developers in 2015—difficult and expensive. A structured workforce plan means better talent pipelines for businesses wanting to explore quantum applications.

7. Keeping European Quantum Independent (So We Don’t Have to Beg for Access Later)

Security is a major concern. QuIC emphasizes the need for European control over critical quantum technologies, especially cloud-based quantum services, whether in banking, defense, or healthcare.

Why you should care: If your business handles sensitive data, future EU regulations might favor quantum solutions developed and hosted within Europe. Getting ahead of compliance now could save you a lot of headaches later.

8. Quantum for Sustainability & Social Good (Not Just for Science Fiction Plots)

QuIC recommends that quantum support broader sustainability goals—both environmental and social. This includes promoting responsible innovation, supporting green quantum tech, and aligning with the UN Sustainable Development Goals.

Why you should care:  As ESG reporting gets more serious, quantum applications in climate modeling, sustainable supply chains, and green energy could unlock real efficiencies and regulatory advantages. Plus, inclusive frameworks mean a more diverse future talent pool—and better optics for your brand.

Final Thoughts: What Should Businesses Do Now?

  1. Start preparing for quantum computing. You don’t need a quantum team yet, but understanding potential applications in your industry will prepare you for the next step.
  2. Keep an eye on EU funding and incentives. There is money available to support early adoption.
  3. Consider partnerships. Why take all the risks and a budget hit going it alone? Join forces with one or more quantum teams at startups and vendors and share the burden. You help them advance, they help you get answers you need. 

If you’re curious about how quantum computing could impact your business, now is the time to start paying attention.

Want to dive deeper? Read the full QuIC recommendations here and book a demo with our team.