Scalable evaluation of quantum-circuit error loss using Clifford sampling

TL;DR

This paper introduces a scalable method for evaluating quantum-circuit error loss functions using Clifford sampling, aiding optimization in quantum device and algorithm design for intermediate-scale quantum systems.

Contribution

It demonstrates that quadratic and fidelity loss functions can be efficiently estimated via Clifford sampling, enabling scalable error assessment in quantum circuits.

Findings

Error distribution is approximately Gaussian, validating quadratic loss use.

Efficient evaluation of loss functions via Clifford sampling demonstrated on simulated and real quantum circuits.

Results support optimization of quantum devices and algorithms in intermediate-scale regimes.

Abstract

A major challenge in developing quantum computing technologies is to accomplish high precision tasks by utilizing multiplex optimization approaches, on both the physical system and algorithm levels. Loss functions assessing the overall performance of quantum circuits can provide the foundation for many optimization techniques. In this paper, we use the quadratic error loss and the final-state fidelity loss to characterize quantum circuits. We find that the distribution of computation error is approximately Gaussian, which in turn justifies the quadratic error loss. It is shown that these loss functions can be efficiently evaluated in a scalable way by sampling from Clifford-dominated circuits. We demonstrate the results by numerically simulating ten-qubit noisy quantum circuits with various error models as well as executing four-qubit circuits with up to ten layers of two-qubit gates on a superconducting quantum processor. Our results pave the way towards the optimization-based quantum device and algorithm design in the intermediate-scale quantum regime.