Machine learning of noise-resilient quantum circuits

TL;DR

This paper introduces a machine learning framework called NACL that optimizes quantum circuits to be resilient against hardware noise, improving the reliability of near-term quantum computations.

Contribution

The paper presents a novel noise-aware circuit learning method that adapts quantum circuits to specific device noise models, enhancing noise mitigation in quantum computing.

Findings

NACL successfully optimizes circuits for noise resilience on superconducting qubits.

Demonstrates improved performance in quantum state overlap, Fourier transform, and W-state preparation.

Applicable to various quantum tasks with device-specific noise models.

Abstract

Noise mitigation and reduction will be crucial for obtaining useful answers from near-term quantum computers. In this work, we present a general framework based on machine learning for reducing the impact of quantum hardware noise on quantum circuits. Our method, called noise-aware circuit learning (NACL), applies to circuits designed to compute a unitary transformation, prepare a set of quantum states, or estimate an observable of a many-qubit state. Given a task and a device model that captures information about the noise and connectivity of qubits in a device, NACL outputs an optimized circuit to accomplish this task in the presence of noise. It does so by minimizing a task-specific cost function over circuit depths and circuit structures. To demonstrate NACL, we construct circuits resilient to a fine-grained noise model derived from gate set tomography on a superconducting-circuit quantum device, for applications including quantum state overlap, quantum Fourier transform, and W-state preparation.