Testing quantum computers with the protocol of quantum state matching

TL;DR

This paper demonstrates how the quantum state matching protocol can be used to test and benchmark noisy intermediate-scale quantum computers, revealing insights into device noise and performance differences.

Contribution

It introduces a method to test quantum computers using the quantum state matching protocol and analyzes device noise and performance through experimental implementation.

Findings

Smaller quantum volume device performs better in specific tests.

The protocol distinguishes statistical errors from device-specific errors.

Performance varies with input type and device characteristics.

Abstract

The presence of noise in quantum computers hinders their effective operation. Even though quantum error correction can theoretically remedy this problem, its practical realization is still a challenge. Testing and benchmarking noisy, intermediate-scale quantum (NISC) computers is therefore of high importance. Here, we suggest the application of the so-called quantum state matching protocol for testing purposes. This protocol was originally proposed to determine if an unknown quantum state falls in a prescribed neighborhood of a reference state. We decompose the unitary specific to the protocol and construct the quantum circuit implementing one step of the dynamics for different characteristic parameters of the scheme and present test results for two different IBM quantum computers. By comparing the experimentally obtained relative frequencies of success to the ideal success probability with a maximum statistical tolerance, we discriminate statistical errors from device specific ones. For the characterization of noise, we also use the fact that while the output of the ideal protocol is insensitive to the internal phase of the input state, the actual implementation may lead to deviations. For systematically varied inputs we find that the device with the smaller quantum volume performs better on our tests than the one with larger quantum volume, while for random inputs they show a more similar performance.