"Automation is the key to unlocking the true potential of quantum computing"
Qruise is driving automation and scalability in quantum computing with its innovative software solution. By combining AI-driven control, machine learning, and digital twin technology, QruiseOS enables the efficient optimisation and management of quantum hardware—regardless of the underlying technology.
In this interview, Nicu Becherescu, Business Development Manager at Qruise, discusses the key challenges in quantum research, the crucial role of automation, and the company’s vision for transitioning quantum computing from research labs to real-world industrial applications.
QruiseOS enables a fast and comprehensive setup of quantum computers. What makes your solution so unique?
QruiseOS is designed to bridge the gap between theoretical quantum computing models and real-world hardware, enabling faster and more precise system optimisation. What sets our solution apart is its combination of AI-driven control, physics-informed machine learning, and real-time adaptive optimisation.
Our three core pillars are:
End-to-End Automation: QruiseOS automates the calibration, characterisation, and optimisation of qubits, significantly reducing the time needed to get a system up and running.
Differentiable Digital Twin Technology: Our platform features a digital twin that continuously learns from experimental data, refining quantum hardware models to predict and correct errors before they impact performance.
Seamless Integration: QruiseOS is hardware-agnostic, meaning it can be integrated across various quantum computing architectures—from superconducting qubits to trapped ions and beyond.
Your system includes an extensive library of over 40 experiments, a model management system, and a graph-based dependency structure. How does this make it easier for researchers and developers to work with quantum computers?
At Qruise, we understand that quantum computing is highly complex, requiring continuous characterisation, calibration, and optimisation of numerous interdependent parameters. That’s why we’ve built a system that offers: A comprehensive library of predefined quantum experiments (covering every textbook example you can think of). A robust model management system that keeps track of experimental conditions and hardware states. A graph-based dependency structure that streamlines the execution of interconnected experiments. These features eliminate the need for researchers to design every experiment from scratch, enhancing reproducibility and enabling faster benchmarking across different quantum hardware platforms.
Automation and reproducibility are critical in quantum computing. How does QruiseOS improve these aspects?
Automation and reproducibility are essential for scaling quantum computing from academic research to industrial applications. Quantum systems evolve over time due to drift and environmental factors. Our model management system ensures researchers always work with the latest, high-fidelity models of their hardware. This enables seamless switching between different models, easier comparison of experimental results, and continuous real-time performance refinement.
Additionally, quantum experiments are inherently interconnected. Our graph-based dependency structure ensures that any parameter change is correctly propagated throughout the system. This avoids the need to manually re-run entire sequences when adjusting a single variable, reducing computational overhead and experiment time. Ultimately, QruiseOS automates the low-level hardware configuration, allowing researchers to focus on discovery and innovation rather than system troubleshooting.
Your system enables the storage, sharing, and reproducibility of experiments. What challenges did you face in developing this approach, and how does it change the way scientists work with quantum hardware?
This is a very good and critical question. Quantum hardware varies significantly between different platforms, making standardisation a major challenge. To address this, we developed a flexible abstraction layer that allows QruiseOS to adapt across different qubit technologies. Reproducibility goes beyond simply storing experiments—it requires version control for hardware states, calibration parameters, and noise profiles. Our model management system ensures experiments are tied to the exact conditions in which they were conducted, improving accuracy and comparability.
Your approach combines machine learning with physical models—a promising strategy. What advantages does QruiseML offer for quantum computing?
QruiseOS and QruiseML are two sides of the same coin. We believe that quantum computing requires a fundamentally new approach to control and optimisation—one that blends machine learning (ML) with physics-based models.
Unlike traditional ML models, QruiseML incorporates: Physical laws, Hamiltonian models, and system dynamics to ensure predictions remain grounded in real-world quantum behaviour. Differentiable digital twins that allow researchers to simulate, predict, and optimise system performance without excessive trial-and-error on real hardware. Adaptive quantum control that dynamically adjusts pulse sequences in real time to counteract noise and decoherence effects. By reducing the need for expensive and time-consuming physical measurements, QruiseML significantly accelerates calibration, noise characterisation, and algorithm development.
Your integration with Qiskit and Private Quantum Cloud makes your technology particularly accessible. How important are these features for scalability?
We are committed to making quantum computing more accessible, scalable, and seamlessly integrable into existing workflows. Our Private Quantum Cloud and direct Qiskit integration are key enablers of this vision, providing companies and research organizations with secure, flexible, and powerful access to quantum experiments.
Unlike public cloud quantum services, our Private Quantum Cloud provides dedicated access to quantum computing infrastructure. This is essential for companies and research organizations that require data privacy, compliance with industry regulations, and full control over their experiments. Users can run high-priority experiments with guaranteed resource allocation, avoiding queuing delays common in shared cloud services. The cloud environment is fully customizable, allowing organizations to fine-tune quantum experiments and machine learning models without external constraints. Multiple teams within a company or across research institutions can collaborate in a secure environment. The combination of Qiskit integration and a private cloud removes infrastructure barriers, making it easier for companies without in-house quantum hardware to conduct high-fidelity experiments.
How has winning the Quantum Effect Award 2024 impacted your company?
Winning the Quantum Effects Award 2024 has been an important moment for Qruise, validating our vision, technology, and impact in the quantum ecosystem. This recognition has not only strengthened our credibility but has also opened new doors for collaborations, investments, and business opportunities. The award has significantly boosted our visibility among leading quantum hardware developers, research institutions, and industry partners. As a result, we have received new collaboration requests from quantum labs, universities, and companies seeking to integrate our solution.
Also, the award has attracted increased interest from investors looking to support scalable quantum software solutions. We are actively exploring strategic funding opportunities to accelerate our R&D efforts, expand our team, and strengthen our global presence.
For our team, this award is a testament to years of effort in building AI-driven quantum software solutions. It validates our approach in combining machine learning, digital twins, and physics-based models to improve quantum computing efficiency. On behalf of Qruise, I want to thank you again for hosting this competition, which, in some ways, makes quantum computing more 'tangible' for everyone.
A look into the future: Where is Qruise heading in the next few years?
Qruise is leading the way in quantum automation, making quantum computing more scalable, accessible, and efficient. Our focus on AI-driven optimization, expanding hardware compatibility, and real-world applications will help transition quantum computing from research labs to commercial and industrial adoption. The next few years will be transformative, and we’re excited to shape the future of quantum computing.
Also, academically speaking, Qruise is cooking something exciting for the near future. Without spilling the beans too much, we are going to take our hardware-agnostic approach to new heights. We plan to extend its compatibility to a wider range of quantum platforms and even go beyond quantum. Stay tuned for updates from Qruise.
Interested in the Quantum Effects Award? Then apply now: Quantum Effects - Quantum Effects Award | Messe Stuttgart
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