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The quest for a Quantum Neural Network
This article presents a systematic approach to QNN research, concentrating on Hopfield-type networks and the task of associative memory, and outlines the challenge of combining the nonlinear, dissipative dynamics of neural computing and the linear, unitary dynamics of quantum computing. Expand
An introduction to quantum machine learning
This contribution gives a systematic overview of the emerging field of quantum machine learning and presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning. Expand
Quantum Machine Learning in Feature Hilbert Spaces.
This Letter interprets the process of encoding inputs in a quantum state as a nonlinear feature map that maps data to quantum Hilbert space and shows how it opens up a new avenue for the design of quantum machine learning algorithms. Expand
Circuit-centric quantum classifiers
A machine learning design is developed to train a quantum circuit specialized in solving a classification problem. In addition to discussing the training method and effect of noise, it is shown thatExpand
PennyLane: Automatic differentiation of hybrid quantum-classical computations
PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation, and it extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. Expand
Evaluating analytic gradients on quantum hardware
An important application for near-term quantum computing lies in optimization tasks, with applications ranging from quantum chemistry and drug discovery to machine learning. In many settings --- mostExpand
Continuous-variable quantum neural networks
A general method for building neural networks on quantum computers and how a classical network can be embedded into the quantum formalism and propose quantum versions of various specialized model such as convolutional, recurrent, and residual networks are introduced. Expand
Quantum embeddings for machine learning
This work proposes to train the first part of the circuit with the objective of maximally separating data classes in Hilbert space, a strategy it calls quantum metric learning, which provides a powerful analytic framework for quantum machine learning. Expand
Machine learning and the physical sciences
Machine learning (ML) encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. ThisExpand