Continuous variable quantum perceptron
Abstract
We present a model of Continuous Variable Quantum Perceptron (CVQP), also referred to as neuron in the following, whose architecture implements a classical perceptron. The necessary nonlinearity is obtained via measuring the output qubit and using the measurement outcome as input to an activation function. The latter is chosen to be the socalled Rectified linear unit (ReLu) activation function by virtue of its practical feasibility and the advantages it provides in learning tasks. The encoding of classical data into realistic finitely squeezed states and the use of superposed (entangled) input states for specific binary problems are discussed.
 Publication:

International Journal of Quantum Information
 Pub Date:
 2019
 DOI:
 10.1142/S0219749919410090
 arXiv:
 arXiv:1911.09399
 Bibcode:
 2019IJQI...1741009B
 Keywords:

 Quantum machine learning;
 quantum perceptron;
 continuous variable models;
 Quantum Physics
 EPrint:
 16 pages, 2 figures