Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise
This work addresses noise resilience for quantum circuit classifiers, which is crucial for practical applications of near-term quantum computers.
The authors tackled the problem of noise in quantum circuit classifiers by developing a provably noise-resilient training theory and algorithm, which guarantees resilience to parameter noise with minimal adjustments to existing optimization methods and was successfully demonstrated in quantum phase classification tasks.
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 circuit classifiers. 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 tasks. By developing provably guaranteed optimization theory with quantum circuits, our work opens new avenues for practical, robust applications of near-term quantum computers.