Batched quantum state exponentiation and quantum Hebbian learning

TL;DR

This paper introduces a quantum Hebbian learning method using batched quantum state exponentiation, enabling efficient data interaction and Hamiltonian simulation for quantum machine learning applications.

Contribution

It presents a novel batched quantum state exponentiation technique for quantum Hebbian learning, including gate decomposition and efficiency analysis, advancing near-term quantum machine learning.

Findings

Efficient implementation of quantum Hebbian learning via batched state exponentiation

Gate decomposition into Clifford+T set for practical quantum circuits

Potential to extract data eigenvalues through phase estimation

Abstract

Machine learning is a crucial aspect of artificial intelligence. This paper details an approach for quantum Hebbian learning through a batched version of quantum state exponentiation. Here, batches of quantum data are interacted with learning and processing quantum bits (qubits) by a series of elementary controlled partial swap operations, resulting in a Hamiltonian simulation of the statistical ensemble of the data. We decompose this elementary operation into one and two qubit quantum gates from the Clifford+T set and use the decomposition to perform an efficiency analysis. Our construction of quantum Hebbian learning is motivated by extension from the established classical approach, and it can be used to find details about the data such as eigenvalues through phase estimation. This work contributes to the near-term development and implementation of quantum machine learning techniques.