Quantum Circuit Cutting with Maximum Likelihood Tomography

TL;DR

This paper presents maximum likelihood fragment tomography (MLFT), a novel quantum circuit cutting method that improves output estimation accuracy and reduces classical overhead, enabling efficient execution of clustered quantum circuits on limited qubit devices.

Contribution

Introduction of MLFT, a new circuit cutting technique that optimizes output probability estimation using maximum likelihood, outperforming traditional methods.

Findings

MLFT accurately estimates circuit outputs in numerical experiments.

Circuit cutting with MLFT achieves higher fidelity than full circuit execution.

MLFT reduces classical computational overhead in quantum circuit analysis.

Abstract

We introduce maximum likelihood fragment tomography (MLFT) as an improved circuit cutting technique for running clustered quantum circuits on quantum devices with a limited number of qubits. In addition to minimizing the classical computing overhead of circuit cutting methods, MLFT finds the most likely probability distribution for the output of a quantum circuit, given the measurement data obtained from the circuit's fragments. We demonstrate the benefits of MLFT for accurately estimating the output of a fragmented quantum circuit with numerical experiments on random unitary circuits. Finally, we show that circuit cutting can estimate the output of a clustered circuit with higher fidelity than full circuit execution, thereby motivating the use of circuit cutting as a standard tool for running clustered circuits on quantum hardware.