Quantum machine learning with adaptive linear optics

Abstract

We study supervised learning algorithms in which a quantum device is used to perform a computational subroutine - either for prediction via probability estimation, or to compute a kernel via estimation of quantum states overlap. We design implementations of these quantum subroutines using Boson Sampling architectures in linear optics, supplemented by adaptive measurements. We then challenge these quantum algorithms by deriving classical simulation algorithms for the tasks of output probability estimation and overlap estimation. We obtain different classical simulability regimes for these two computational tasks in terms of the number of adaptive measurements and input photons. In both cases, our results set explicit limits to the range of parameters for which a quantum advantage can be envisaged with adaptive linear optics compared to classical machine learning algorithms: we show that the number of input photons and the number of adaptive measurements cannot be simultaneously small compared to the number of modes. Interestingly, our analysis leaves open the possibility of a near-term quantum advantage with a single adaptive measurement.

arXiv comment16 + 5 pages, presented at AQIS2020, accepted in Quantum

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• Fock State-enhanced Expressivity of Quantum Machine Learning Models
• Phase-space negativity as a computational resource for quantum kernel methods
• Quantum Kernel Evaluation via Hong-Ou-Mandel Interference
• Experimental benchmarking of quantum state overlap estimation strategies with photonic systems
• Quantum memristor with vacuum--one-photon qubits
• Observation of Lie algebraic invariants in Quantum Linear Optics
• Mitigating photon loss in linear optical quantum circuits
• Classical algorithms for measurement-adaptive Gaussian circuits
• Complexity of Gaussian quantum optics with a limited number of non-linearities
• Optical Quantum Computing