Improving the Reliability of Quantum Circuits by Evolving Heterogeneous Ensembles

TL;DR

This paper introduces QuEEn, an evolutionary algorithm that creates heterogeneous quantum circuit ensembles, significantly improving their reliability in classification tasks, especially under noisy conditions.

Contribution

The paper presents a novel evolutionary approach to generate heterogeneous quantum circuit ensembles, enhancing their robustness over traditional homogeneous ensembles.

Findings

Heterogeneous ensembles outperform homogeneous ones in ideal simulations.

Significant performance gains observed in noisy simulations.

Evolving ensembles is effective for NISQ-era quantum computing.

Abstract

Quantum computers can perform certain operations exponentially faster than classical computers, but designing quantum circuits is challenging. To that end, researchers used evolutionary algorithms to produce probabilistic quantum circuits that give the correct output more often than not for any input. They can be executed multiple times, with the outputs combined using a classical method (such as voting) to produce the final output, effectively creating a homogeneous ensemble of circuits (i.e., all identical). Inspired by n-version programming and ensemble learning, we developed a tool that uses an evolutionary algorithm to generate heterogeneous ensembles of circuits (i.e., all different), named QuEEn. We used it to evolve ensembles to solve the Iris classification problem. When using ideal simulation, we found the performance of heterogeneous ensembles to be greater than that of homogeneous ensembles to a statistically significant degree. When using noisy simulation, we still observed a statistically significant improvement in the majority of cases. Our results indicate that evolving heterogeneous ensembles is an effective strategy for improving the reliability of quantum circuits. This is particularly relevant in the current NISQ era of quantum computing where computers do not yet have good tolerance to quantum noise.