A hybrid machine-learning algorithm for designing quantum experiments

TL;DR

This paper presents a hybrid machine-learning approach combining genetic algorithms and neural networks to design feasible quantum optics experiments that generate specific quantum states with high fidelity.

Contribution

It introduces a novel hybrid algorithm that efficiently designs realistic quantum experiments using current technology, including a neural network for rapid state classification.

Findings

Successfully designed experiments for 5 target quantum states

Achieved over 96% fidelity in experimental designs

Neural network accurately classifies quantum states from photon data

Abstract

We introduce a hybrid machine-learning algorithm for designing quantum optics experiments that produce specific quantum states. Our algorithm successfully found experimental schemes to produce all 5 states we asked it to, including Schr\"odinger cat states and cubic phase states, all to a fidelity of over $96\%$. Here we specifically focus on designing realistic experiments, and hence all of the algorithm's designs only contain experimental elements that are available with current technology. The core of our algorithm is a genetic algorithm that searches for optimal arrangements of the experimental elements, but to speed up the initial search we incorporate a neural network that classifies quantum states. The latter is of independent interest, as it quickly learned to accurately classify quantum states given their photon-number distributions.