We introduce a high-dimensional quantum encoding based on coherent mode-dependent single-photon subtraction from multimode squeezed states. This encoding can be seen as a generalization to the case of nonzero squeezing of the standard single-photon multirail encoding. The advantage is that the presence of squeezing enables the use of common tools in continuous-variable quantum processing, which in turn allows us to show that arbitrary d-level quantum states can be generated and detected by simply tuning the classical fields that gate the photon-subtraction scheme. Therefore, the scheme is suitable for mapping arbitrary classical data in quantum mechanical form. Regardless of the dimension of the data-set alphabet, the mapping is conditioned on the subtraction of a single photon only, making it nearly unconditional. We prove that this encoding can be used to calculate vector distances, a pivotal primitive in various quantum machine-learning algorithms.

High-dimensional quantum encoding via photon-subtracted squeezed states / F. Arzani, A. Ferraro, V. Parigi. - In: PHYSICAL REVIEW A. - ISSN 2469-9926. - 99:2(2019 Feb 28), pp. 022342.022342-1-022342.022342-11. [10.1103/PhysRevA.99.022342]

High-dimensional quantum encoding via photon-subtracted squeezed states

A. Ferraro
Penultimo
;
2019-02-28

Abstract

We introduce a high-dimensional quantum encoding based on coherent mode-dependent single-photon subtraction from multimode squeezed states. This encoding can be seen as a generalization to the case of nonzero squeezing of the standard single-photon multirail encoding. The advantage is that the presence of squeezing enables the use of common tools in continuous-variable quantum processing, which in turn allows us to show that arbitrary d-level quantum states can be generated and detected by simply tuning the classical fields that gate the photon-subtraction scheme. Therefore, the scheme is suitable for mapping arbitrary classical data in quantum mechanical form. Regardless of the dimension of the data-set alphabet, the mapping is conditioned on the subtraction of a single photon only, making it nearly unconditional. We prove that this encoding can be used to calculate vector distances, a pivotal primitive in various quantum machine-learning algorithms.
Settore FIS/03 - Fisica della Materia
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/907766
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