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  2. Quantum neural network - Wikipedia

    en.wikipedia.org/wiki/Quantum_neural_network

    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.

  3. Qiskit - Wikipedia

    en.wikipedia.org/wiki/QISKit

    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.

  4. Activation function - Wikipedia

    en.wikipedia.org/wiki/Activation_function

    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.

  5. Quantum machine learning - Wikipedia

    en.wikipedia.org/wiki/Quantum_machine_learning

    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]

  6. Neural network quantum states - Wikipedia

    en.wikipedia.org/wiki/Neural_network_quantum_states

    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.

  7. Reservoir computing - Wikipedia

    en.wikipedia.org/wiki/Reservoir_computing

    Overall, the general concept of reservoir computing stems from the use of recursive connections within neural networks to create a complex dynamical system. [8] It is a generalisation of earlier neural network architectures such as recurrent neural networks, liquid-state machines and echo-state networks.

  8. Q Sharp - Wikipedia

    en.wikipedia.org/wiki/Q_Sharp

    Q# works in conjunction with classical languages such as C#, Python and F#, and is designed to allow the use of traditional programming concepts in quantum computing, including functions with variables and branches as well as a syntax-highlighted development environment with a quantum debugger.

  9. Quantum algorithm - Wikipedia

    en.wikipedia.org/wiki/Quantum_algorithm

    In quantum computing, a quantum algorithm is an algorithm that runs on a realistic model of quantum computation, the most commonly used model being the quantum circuit model of computation. [ 1 ] [ 2 ] A classical (or non-quantum) algorithm is a finite sequence of instructions, or a step-by-step procedure for solving a problem, where each step ...