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Information Sciences

Comments on “quantum artificial neural networks with applications”

Adenilton José da Silva, Wilson Rosa de Oliveira

Published Jul 28, 2016• 13 citations
Publisher
DOI
10.1016/j.ins.2016.07.062

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A recent article [Science 388, 199-204 (2025)] investigates the applicability of numerical methods and a quantum processor unit in simulating a quantum annealing protocol. One of the findings indicates that Neural Quantum States - a versatile variational ansatz for the many-body wave function based on artificial neural networks - fail to reach the same accuracy as the quantum processor. In this comment we revisit these concerns, demonstrating that NQS can provide competitive results in some of the cases when accounting for the Monte-Carlo noise and large autocorrelation times between samples obtained from the final state.
Quantum artificial neural network (QANN) is one of the new paradigms built upon the combination of classical neural and quantum computations. It has great values for theoretic study and potentials to applications. In this paper, an intrinsic similarity of quantum theory and artificial neural network (ANN) theory has been highlighted and analyzed. The dynamic features of QANN are also discussed in details. As an application using dynamic features, a novel model of quantum neuron (QN) is described hereby with a focus on an operation function. Numerical results are illustrated to show that this the proposed QN model can perform a XOR function unrealizable with a classical neuron (CN). The model allows obtain a nonlinear mapping performed by two layers of classical ANN (CANN)