
Baryons from Mesons: A Machine Learning Perspective
Quantum chromodynamics (QCD) is the theory of the strong interaction. Th...
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Continuousvariable quantum neural networks
We introduce a general method for building neural networks on quantum co...
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Quantum Deformed Neural Networks
We develop a new quantum neural network layer designed to run efficientl...
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Quantum simulation from the bottom up: the case of rebits
Typically, quantum mechanics is thought of as a linear theory with unita...
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Gaussian boson sampling and multiparticle event optimization by machine learning in the quantum phase space
We use neural networks to represent the characteristic function of many...
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Quantum computing and the brain: quantum nets, dessins d'enfants and neural networks
In this paper, we will discuss a formal link between neural networks and...
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Quantum Optical Convolutional Neural Network: A Novel Image Recognition Framework for Quantum Computing
Large machine learning models based on Convolutional Neural Networks (CN...
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The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning
In this work we develop a quantum field theory formalism for deep learning, where input signals are encoded in Gaussian states, a generalization of Gaussian processes which encode the agent's uncertainty about the input signal. We show how to represent linear and nonlinear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles, dubbed “Hintons”. On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing, and provides quantum deformations of neural networks that can be run efficiently on those devices. Finally, we discuss a semiclassical limit of the quantum deformed models which is amenable to classical simulation.
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