Quantum machine learning with linear optics and coherent states

Artificial optical neural networks based on coherent light propagation can potentially provide a higher information processing speed and lower power consumption than conventional electronic architectures [1]. Yet, their performance is limited by the difficulty of realizing nonlinear activation functions. In contrast, quantum machine learning approaches circumvent the need for nonlinearities by embedding data into high dimensional Hilbert spaces using quantum feature maps [2,3]. Here we show how the quantum feature map approach allows one to carry out photonic machine learning using coherent states, linear optical interferometers, and single photon or photon number-resolving detectors [4]. Our work sheds some light on the possibility of performing non-trivial quantum machine learning tasks using bosonic modes.

[1] Y. Shen, N. C. Harris, S. Skirlo, et al, Nat. Photonics 11, 441–446 (2017).

[2] M. Schuld and N. Killoran, Phys. Rev. Lett. 122, 040504 (2019).

[3] V. Havlicek, A. D. Corcoles, K. Temme, et al, Nature 567, 209-212 (2019).

[4] B. Y. Gan, D. Leykam, D. G. Angelakis, arXiv:2107.05224 (2021).