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QI: Fachverband Quanteninformation
QI 9: Quantum Machine Learning and Classical Simulability
QI 9.2: Talk
Tuesday, March 19, 2024, 10:00–10:15, HFT-FT 101
Can a neural network fake a Boson Sampler? — •Martina Jung1, Martin Gärttner1, and Moritz Reh1, 2 — 1Friedrich-Schiller-Universität, Jena, Deutschland — 2Universität Heidelberg, Heidelberg, Deutschland
Originally defined to demonstrate quantum supremacy, Boson Sampling and its simulation have become an own field of research. The simulation of a Boson Sampler is an - per-construction - classically intractable problem due to the computational complexity of its distribution. A statistics-based approach to learn the probability distribution of a sampling process faces issues like sparse data and highly correlated output configurations. These problems are reminiscent of natural language processing (NLP) tasks where a neural network is trained to respond to a query. Indeed, NLP models like a recurrent neural network (RNN) decompose the task by learning to sequentially predict the next word based on the preceding sequence of words. Transferring this concept to the bosonic Fock space, we train a RNN to simulate a Boson Sampler by predicting the conditional probabilities related to input-output configurations. The model's ability to extrapolate is tested on input sequences of lengths beyond the ones seen during the training.
Keywords: Neural Network; Boson Sampling; Machine Learning