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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.
Quantum neural networks apply the principals quantum information and quantum computation to classical neurocomputing. [72] Current research shows that QNN can exponentially increase the amount of computing power and the degrees of freedom for a computer, which is limited for a classical computer to its size. [72]
Cirq was developed by the Google AI Quantum Team, and the public alpha was announced at the International Workshop on Quantum Software and Quantum Machine Learning on July 18, 2018. [2] A demo by QC Ware showed an implementation of QAOA solving an example of the maximum cut problem being solved on a Cirq simulator.
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]
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.
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.