Provable bounds for noise-free expectation values computed from noisy samples

Abstract

In this paper, we explore the impact of noise on quantum computing, particularly focusing on the challenges when sampling bit strings from noisy quantum computers as well as the implications for optimization and machine learning applications. We formally quantify the sampling overhead to extract good samples from noisy quantum computers and relate it to the layer fidelity, a metric to determine the performance of noisy quantum processors. Further, we show how this allows us to use the Conditional Value at Risk of noisy samples to determine provable bounds on noise-free expectation values. We discuss how to leverage these bounds for different algorithms and demonstrate our findings through experiments on a real quantum computer involving up to 127 qubits. The results show a strong alignment with theoretical predictions.

Pages 17, Figures 6, Tables 3

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• Efficient Online Quantum Circuit Learning with No Upfront Training
• Approximate Quadratization of High-Order Hamiltonians for Combinatorial Quantum Optimization
• Digital Zero-Noise Extrapolation with Quantum Circuit Unoptimization
• Evaluating the Limits of QAOA Parameter Transfer at High-Rounds on Sparse Ising Models With Geometrically Local Cubic Terms
• Sampling (noisy) quantum circuits through randomized rounding