Automated Design Tool Improves Quantum Circuit Performance for Machine Learning Tasks
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Scientists are increasingly exploring variational quantum circuits as promising machine learning models, yet achieving optimal performance requires careful circuit design which is often a difficult and time-consuming process. Grier M. Jones and Viki Kumar Prasad, both from The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Canada and Department of Chemical and Physical Sciences, University of Toronto Mississauga, Canada, alongside Aviraj Newatia from The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Canada, Department of Computer Science, University of Toronto, Canada, and the Vector Institute for Artificial Intelligence, Canada, and colleagues present a novel evolution-inspired algorithm for optimising these circuits through local gate modifications.
This research, conducted in collaboration with researchers at the Department of Chemistry, University of Calgary, Canada, introduces a method to automatically discover competitive circuit architectures, demonstrated through successful application to synthetic regression tasks and complex datasets including bond separation energies and water conformer data. The ability to efficiently design high-performing quantum circuits represents a significant step towards realising the potential of quantum machine learning and deploying these models on current hardware. Parametrized quantum circuits, while flexible, often require painstaking manual design to achieve optimal performance for specific tasks. By applying a fixed set of gate-level actions to existing circuits, the algorithm efficiently explores promising configurations. This localized search strategy is motivated by the observation that many effective quantum circuits can be derived from relatively small perturbations of already functional designs. This performance metric, calculated through state-vector simulation, indicates the frequency of incorrect predictions made by the model during each computational step. Analysis of the discovered circuits reveals that the algorithm prioritizes structural preservation during refinement, maintaining functional integrity while enabling targeted improvements. The best-performing model was successfully deployed on state-of-the-art quantum hardware, validating its practical applicability beyond simulation. This deployment confirms the feasibility of translating algorithmically-designed circuits into tangible quantum computations. This approach circumvents the limitations of previous quantum architecture search methods, which often struggle with the computational cost of searching vast configuration spaces. Additionally, a dataset of water conformers, generated using the data-driven coupled-cluster approach, provided a challenging benchmark for assessing the algorithm’s capabilities in modelling molecular properties. This choice of datasets reflects the potential of quantum machine learning to accelerate computationally intensive tasks within chemistry and materials science. However, realising this potential demands more than just algorithms; it requires the efficient design of quantum circuits tailored to specific tasks. This work presents a significant step towards automating that process, demonstrating a method for evolving quantum circuit architectures through a local, probabilistic search. Furthermore, the fixed set of gate-level actions may limit the exploration of truly novel circuit topologies. Looking ahead, we can anticipate a convergence of these architecture search algorithms with techniques for optimising circuit compilation and error correction. The next generation of tools won’t just find good circuits, they will build them, adapting to the specific constraints of the available hardware and pushing the boundaries of what’s computationally possible. 👉 More information 🗞 Probabilistic Design of Parametrized Quantum Circuits through Local Gate Modifications 🧠 ArXiv: https://arxiv.org/abs/2602.12465 Tags: Rohail T. As a quantum scientist exploring the frontiers of physics and technology. My work focuses on uncovering how quantum mechanics, computing, and emerging technologies are transforming our understanding of reality. I share research-driven insights that make complex ideas in quantum science clear, engaging, and relevant to the modern world. Latest Posts by Rohail T.: Statistical Modelling Now Maps Complex Variations with Greater Precision February 17, 2026 Climate Models Assessed to Improve River Flow Projections for Water Management February 16, 2026 Automation of All Work Is Theoretically Possible, New Research Suggests February 16, 2026