Genetic Algorithms for Digital Quantum Simulations

TL;DR

This paper introduces genetic algorithms to improve digital quantum simulations by enhancing fidelity, reducing errors, and optimizing resource use, while adapting to experimental constraints and increasing robustness against gate errors.

Contribution

It presents a novel application of genetic algorithms to optimize digital quantum simulation protocols and design resilient modular gates with higher fidelity.

Findings

Genetic algorithms improve quantum simulation fidelity.

Modular gates outperform individual imperfect gates.

Enhanced robustness against gate errors.

Abstract

We propose genetic algorithms, which are robust optimization techniques inspired by natural selection, to enhance the versatility of digital quantum simulations. In this sense, we show that genetic algorithms can be employed to increase the fidelity and optimize the resource requirements of digital quantum simulation protocols, while adapting naturally to the experimental constraints. Furthermore, this method allows us to reduce not only digital errors, but also experimental errors in quantum gates. Indeed, by adding ancillary qubits, we design a modular gate made out of imperfect gates, whose fidelity is larger than the fidelity of any of the constituent gates. Finally, we prove that the proposed modular gates are resilient against different gate errors.