Tag: Innovation

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.

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.