Universal quantum gates, artificial neurons and pattern recognition simulated by \textit{LC} resonators

TL;DR

This paper demonstrates how LC resonators can simulate quantum gates and neural networks, enabling pattern recognition tasks through controllable phase shifts, offering a new approach to quantum-inspired computation.

Contribution

It introduces a method to simulate universal quantum gates and neural networks using LC resonators with controllable phase, bridging quantum computing concepts with classical resonator systems.

Findings

Resonator-based quantum gate simulation is feasible.

Pattern recognition can be performed using phase-controlled resonators.

Complex neural networks enable colored pattern recognition.

Abstract

We propose to simulate quantum gates by \textit{LC} resonators, where the amplitude and the phase of the voltage describe the quantum state. By controlling 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 a pattern recognition of numbers and alphabets by evaluating the similarity between an input and the reference pattern. We also study a colored pattern recognition by using a complex neural network.