Provably Noise-Resilient Training of Parameterized Quantum Circuits

Lucas Matthew Tecot, Di Luo, Cho-Jui Hsieh

Keywords: robustness, quantum, Parameterized Quantum Circuits, Noise-Resilient

Abstract: Advancements in quantum computing have spurred significant interest in harnessing its potential for speedups over classical systems. However, noise remains a major obstacle to achieving reliable quantum algorithms. In this work, we present a provably noise-resilient training theory and algorithm to enhance the robustness of parameterized quantum circuits. Our method, with a natural connection to Evolutionary Strategies, guarantees resilience to parameter noise with minimal adjustments to commonly used optimization algorithms. Our approach is function-agnostic and adaptable to various quantum circuits, successfully demonstrated in quantum phase classification and quantum state preparation tasks. By developing provably guaranteed learning theory with quantum circuits, our work opens new avenues for practical, robust applications of near-term quantum computers.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Submission Number: 9036