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A key difference lies in communication between the layers of a neural networks. For classical neural networks, at the end of a given operation, the current perceptron copies its output to the next layer of perceptron(s) in the network. However, in a quantum neural network, where each perceptron is a qubit, this would violate the no-cloning theorem.
Qiskit is made of elements that work together to enable quantum computing. The central goal of Qiskit is to build a software stack that makes it easier for anyone to use quantum computers, regardless of their skill level or area of interest; Qiskit allows users to design experiments and applications and run them on real quantum computers and/or classical simulators.
The term is claimed by a wide range of approaches, including the implementation and extension of neural networks using photons, layered variational circuits or quantum Ising-type models. Quantum neural networks are often defined as an expansion on Deutsch's model of a quantum computational network. [71]
In quantum neural networks programmed on gate-model quantum computers, based on quantum perceptrons instead of variational quantum circuits, the non-linearity of the activation function can be implemented with no need of measuring the output of each perceptron at each layer.
Quil is being developed for the superconducting quantum processors developed by Rigetti Computing through the Forest quantum programming API. [5] [6] A Python library called pyQuil was introduced to develop Quil programs with higher level constructs. A Quil backend is also supported by other quantum programming environments. [7] [8]
Neural Network Quantum States (NQS or NNQS) is a general class of variational quantum states parameterized in terms of an artificial neural network. It was first introduced in 2017 by the physicists Giuseppe Carleo and Matthias Troyer [ 1 ] to approximate wave functions of many-body quantum systems.