Quantum machine learning with Adaptive Boson Sampling via post-selection

Abstract

The implementation of large-scale universal quantum computation represents a challenging and ambitious task on the road to quantum processing of information. In recent years, an intermediate approach has been pursued to demonstrate quantum computational advantage via non-universal computational models. A relevant example for photonic platforms has been provided by the Boson Sampling paradigm and its variants, which are known to be computationally hard while requiring at the same time only the manipulation of the generated photonic resources via linear optics and detection. Beside quantum computational advantage demonstrations, a promising direction towards possibly useful applications can be found in the field of quantum machine learning, considering the currently almost unexplored intermediate scenario between non-adaptive linear optics and universal photonic quantum computation. Here, we report the experimental implementation of quantum machine learning protocols by adding adaptivity via post-selection to a Boson Sampling platform based on universal programmable photonic circuits fabricated via femtosecond laser writing. Our experimental results demonstrate that Adaptive Boson Sampling is a viable route towards dimension-enhanced quantum machine learning with linear optical devices.

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Cited by in corpus (8)

• Quantum memristor with vacuum--one-photon qubits
• Effective programming of a photonic processor with complex interferometric structure
• Observation of Lie algebraic invariants in Quantum Linear Optics
• Unified boson sampling
• Optical Quantum Computing
• Classical algorithms for measurement-adaptive Gaussian circuits
• Quantum optical shallow networks
• Mitigating quantum operation infidelity through engineering the distribution of photon losses