Quantum computing in neural networks

TL;DR

This paper explores the potential of neural networks to implement quantum computing by encoding qubits as probabilistic bits, demonstrating the construction and performance of neural circuits for universal quantum gates.

Contribution

It introduces a neural network model for quantum computing, constructing and numerically analyzing neural circuits that perform universal quantum gates.

Findings

Neural circuits can implement universal quantum gates.

Probabilistic encoding of qubits in neural networks is feasible.

The proposed scheme shows promising performance in simulations.

Abstract

According to the statistical interpretation of quantum theory, quantum computers form a distinguished class of probabilistic machines (PMs) by encoding n qubits in 2n pbits (random binary variables). This raises the possibility of a large-scale quantum computing using PMs, especially with neural networks which have the innate capability for probabilistic information processing. Restricting ourselves to a particular model, we construct and numerically examine the performance of neural circuits implementing universal quantum gates. A discussion on the physiological plausibility of proposed coding scheme is also provided.