
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 learni...
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A quantum algorithm for training wide and deep classical neural networks
Given the success of deep learning in classical machine learning, quantu...
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NeuralNetwork Heuristics for Adaptive Bayesian Quantum Estimation
Quantum metrology promises unprecedented measurement precision but suffe...
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A neural network oracle for quantum nonlocality problems in networks
Characterizing quantum nonlocality in networks is a challenging problem....
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QDNN: DNN with Quantum Neural Network Layers
The deep neural network (DNN) became the most important and powerful mac...
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Defining Quantum Neural Networks via Quantum Time Evolution
This work presents a novel fundamental algorithm for for defining and tr...
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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 Deformed Neural Networks
We develop a new quantum neural network layer designed to run efficiently on a quantum computer but that can be simulated on a classical computer when restricted in the way it entangles input states. We first ask how a classical neural network architecture, both fully connected or convolutional, can be executed on a quantum computer using quantum phase estimation. We then deform the classical layer into a quantum design which entangles activations and weights into quantum superpositions. While the full model would need the exponential speedups delivered by a quantum computer, a restricted class of designs represent interesting new classical network layers that still use quantum features. We show that these quantum deformed neural networks can be trained and executed on normal data such as images, and even classically deliver modest improvements over standard architectures.
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