Quantum Neuron with Separable-State Encoding

TL;DR

This paper introduces a quantum perceptron model using separable-state encoding, enabling large-scale testing on current quantum hardware with limited fault tolerance, and demonstrates its effectiveness through simulation.

Contribution

It presents a quantum perceptron with reduced multi-qubit gates and separable-state encoding, facilitating large-scale testing on non-fault tolerant quantum computers.

Findings

The quantum perceptron outperforms classical models in certain tasks.

Separable-state encoding allows scalable testing on current quantum hardware.

Hybrid training schemes effectively simulate learning processes.

Abstract

The use of advanced quantum neuron models for pattern recognition applications requires fault tolerance. Therefore, it is not yet possible to test such models on a large scale in currently available quantum processors. As an alternative, we propose a quantum perceptron (QP) model that uses a reduced number of multi-qubit gates and is therefore less susceptible to quantum errors in current actual quantum computers with limited tolerance. The proposed quantum algorithm is superior to its classical counterpart, although since it does not take full advantage of quantum entanglement, it provides a lower encoding power than other quantum algorithms using multiple qubit entanglement. However, the use of separable-sate encoding allows for testing the algorithm and different training schemes at a large scale in currently available non-fault tolerant quantum computers. We demonstrate the performance of the proposed model by implementing a few qubits version of the QP in a simulated quantum computer. The proposed QP uses an N-ary encoding of the binary input data characterizing the patterns. We develop a hybrid (quantum-classical) training procedure for simulating the learning process of the QP and test their efficiency.