Supervised learning with quantum enhanced feature spaces

TL;DR

This paper introduces two quantum-enhanced methods for machine learning that leverage the large dimensionality of quantum states to improve classification, demonstrating their implementation on a superconducting quantum processor.

Contribution

The paper presents two novel quantum algorithms for classification that utilize quantum states to represent feature spaces, advancing quantum machine learning techniques.

Findings

Implemented on a superconducting processor

Demonstrated quantum advantage in feature space representation

Provided new tools for noisy intermediate scale quantum computers

Abstract

Machine learning and quantum computing are two technologies each with the potential for altering how computation is performed to address previously untenable problems. Kernel methods for machine learning are ubiquitous for pattern recognition, with support vector machines (SVMs) being the most well-known method for classification problems. However, there are limitations to the successful solution to such problems when the feature space becomes large, and the kernel functions become computationally expensive to estimate. A core element to computational speed-ups afforded by quantum algorithms is the exploitation of an exponentially large quantum state space through controllable entanglement and interference. Here, we propose and experimentally implement two novel methods on a superconducting processor. Both methods represent the feature space of a classification problem by a quantum state, taking advantage of the large dimensionality of quantum Hilbert space to obtain an enhanced solution. One method, the quantum variational classifier builds on [1,2] and operates through using a variational quantum circuit to classify a training set in direct analogy to conventional SVMs. In the second, a quantum kernel estimator, we estimate the kernel function and optimize the classifier directly. The two methods present a new class of tools for exploring the applications of noisy intermediate scale quantum computers [3] to machine learning.