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arXiv:2012.06124 · Physical Review Research

Universal quantum gates, artificial neurons, and pattern recognition simulated by LC resonators

Motohiko Ezawa

Published Apr 16, 2021• 10 citations
arXivHTMLPDF

Abstract

We propose to simulate quantum gates by LC resonators, where the amplitude and the phase of the voltage describe the quantum state.By controlling the capacitance or inductance of resonators, it is possible to control the phase of the voltage arbitrarily.A set of resonators acts as the phase-shift, the Hadamard, and the CNOT gates.They constitute a set of universal quantum gates.We also discuss an application to an artificial neuron.As an example, we study pattern recognition of numbers and alphabets by evaluating the similarity between an input and the reference pattern.We also study colored pattern recognition by using a complex neural network.

DOI
10.1103/physrevresearch.3.023051

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We propose to simulate quantum gates by LC resonators, where the amplitude and the phase of the voltage describe the quantum state.By controlling the capacitance or inductance of resonators, it is possible to control the phase of the voltage arbitrarily.A set of resonators acts as the phase-shift, the Hadamard, and the CNOT gates.They constitute a set of universal quantum gates.We also discuss an application to an artificial neuron.As an example, we study pattern recognition of numbers and alphabets by evaluating the similarity between an input and the reference pattern.We also study colored pattern recognition by using a complex neural network.
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Abstract Quantum computation is well known for its particular computational performance. In this paper, a novel quantum neuron model is constructed based on the universal quantum gates unit (i.e. phase-shift gate and controlled-NOT gate), which acts as the basic computational component, with this type of neuron a feed-forward network structure is also built. The simulation results show that the quantum neuron network is superior to classical BP and RBF network for two financial data analysis examples.
A quantum BP neural networks model with learning algorithm is proposed. First, based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate, a quantum neuron model is constructed, which is composed of input, phase rotation, aggregation, reversal rotation and output. In this model, the input is described by qubits, and the output is given by the probability of the state in which |1〉 is observed. The phase rotation and the reversal rotation are performed by the universal quantum gates. Secondly, the quantum BP neural networks model is constructed, in which the output layer and the hide layer are quantum neurons. With the application of the gradient descent algorithm, a learning algorithm of the model is proposed, and the continuity of the model is proved. It is shown that this model and algorithm are superior to the conventional BP networks in three aspects: convergence speed, convergence rate and robustness, by two application examples of pattern recognition and function approximation.
Quantum computing emulation using neuromorphic artificial spiking neurons designed with CMOS electronics is introduced in this paper. By extending the Axon-Hillock neuron model, the proposed circuit can emulate a universal set of quantum gates-Phase Shift, Hadamard, and CNOT-at room temperature, with ultra-low power consumption. Simulations and layout using 180 nm CMOS technology demonstrate successful emulation of quantum logic operations and entanglement, including GHZ states, with small area and significant reduction in power compared to previous oscillator-based approaches. This work paves the way for scalable, energy-efficient quantuminspired computation on standard CMOS hardware, suitable for mobile and edge applications.
The approximation ability of neural networks plays an important role to the result when one uses neural networks to resolve problems.Unfortunately there is no ideal way to construct proper networks with strong approximation ability.A novel construction approach based on the multi-qubits controller-not gates is proposed for neural networks model in this paper.The proposed model consists of three layers where the hidden nodes are the quantum neurons and the output nodes are the common neurons.The quantum neuron consists of the quantum rotation gates and the multi-qubits controllednot gates.The overall memory of input sequences is captured from information feedback of target qubit from output to input in the multi-qubits controlled-not gate.The output of quantum neuron is obtained from the controlled relationship of the multi-qubits controlled-not gate.The L-M learning algorithms are designed in detail based on the basic principles of quantum computation.The features of input sequences can be effectively obtained in two ways of breadth and depth.The experimental results of solitaire forecast show that,when the number of input nodes is close to the length of sequences,the training set recognition rate of the proposed model increases about 8% than the common neural networks,which reveals the effectiveness of the quantum computation for enhancing the approximation capability of the common neural networks.