Designing a Machine Learning-Driven, Cross-Hardware Emulator for Noisy Quantum Computers with Gate-Based Protocols

TL;DR

This paper presents a machine learning-based method to create accurate, device-specific quantum computer emulators that model noise without requiring pulse-level control, aiding optimization and debugging.

Contribution

The authors develop a supervised ML protocol that constructs noise-aware emulators from gate set tomography data, applicable across different hardware platforms.

Findings

Achieves 0.128% relative error in energy estimation compared to real hardware.

Successfully models device noise without pulse-level control.

Emulator effectively predicts quantum program outcomes on various hardware.

Abstract

Quantum computer emulators model the behavior and error rates of specific quantum processors. Without accurate noise models in these emulators, it is challenging for users to optimize and debug executable quantum programs prior to running them on the quantum computer, as device-specific noise is not properly accounted for. To overcome this challenge, we design a machine learning(ML)-driven approach to construct approximate device-specific emulators that applies to different hardware platforms. We apply supervised ML on a pre-generated library containing simulated gate set tomography training data. The ML model then analyses gate set tomography data from a target quantum computer to predict its noise model, which is in turn used to construct the device-specific emulator. We demonstrate the effectiveness of our protocol's emulator in estimating the unitary coupled cluster energy of the H$_2$ molecule and compare the results with those from actual quantum hardware. Remarkably, our noise model captures device noise with high accuracy, achieving a percentage relative error of just 0.128\% in expectation value relative to the actual quantum hardware. Importantly, we show that even without access to pulse-level control, noise from the quantum computer can nonetheless be characterized and independently validated by our protocol.