Optimal quantum control via genetic algorithms for quantum state engineering in driven-resonator mediated networks

TL;DR

This paper demonstrates that genetic algorithms can effectively optimize control protocols for quantum state engineering in superconducting resonator networks, achieving high fidelity and noise resilience.

Contribution

It introduces a machine learning approach using genetic algorithms to optimize quantum control in driven-resonator networks, enabling high-fidelity state preparation.

Findings

Achieved quantum fidelities above 0.96 for complex states.

Demonstrated robustness to noise in quantum control.

Validated effectiveness for large-dimensional quantum systems.

Abstract

We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms. In particular, we focus on superconducting platforms and consider a network of qubits -- encoded in the states of artificial atoms with no direct coupling -- interacting via a common single-mode driven microwave resonator. The qubit-resonator couplings are assumed to be in the resonant regime and tunable in time. A genetic algorithm is used in order to find the functional time-dependence of the couplings that optimise the fidelity between the evolved state and a variety of targets, including three-qubit GHZ and Dicke states and four-qubit graph states. We observe high quantum fidelities (above 0.96 in the worst case setting of a system of effective dimension 96) and resilience to noise, despite the algorithm being trained in the ideal noise-free setting. These results show that the genetic algorithms represent an effective approach to control quantum systems of large dimensions.