A hybrid machine-learning algorithm for designing quantum experiments
This work addresses the challenge of designing realistic quantum experiments for researchers in quantum optics, though it is incremental as it combines existing methods.
The authors tackled the problem of designing quantum optics experiments to produce specific quantum states, and their hybrid machine-learning algorithm successfully generated experimental schemes for all 5 target states with fidelities over 96%.
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 Schrödinger 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.