Quantum machine learning with Adaptive Boson Sampling via post-selection

TL;DR

This paper demonstrates that adaptive Boson Sampling with post-selection can be used for quantum machine learning, showing potential for dimension-enhanced applications on linear optical devices.

Contribution

It introduces an experimental implementation of adaptive Boson Sampling with post-selection for quantum machine learning on programmable photonic circuits.

Findings

Adaptive Boson Sampling is feasible with current photonic technology.

The approach enhances the dimension of quantum machine learning models.

Experimental results validate the viability of the method.

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.