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A hybrid machine learning algorithm for designing quantum experiments
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DOI：10.1007/s42484-019-00003-8.png)
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Abstract
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We introduce a hybrid machine learning algorithm for designing quantum optics experiments to produce specific quantum states. Our algorithm successfully found experimental schemes to produce all 5 states we asked it to, including Schrodinger 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.
Keywords:
Machine learning
Genetic algorithm
Artificial intelligence
Quantum state engineering
Quantum optics
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