A Triple-Hybrid Quantum Support Vector Machine Using Classical, Quantum Gate-based and Quantum Annealing-based Computing

TL;DR

This paper introduces a triple-hybrid quantum support vector machine combining classical, gate-based quantum, and quantum annealing methods, demonstrating improved accuracy and convergence on complex datasets.

Contribution

The work presents a novel hybrid approach integrating three computational paradigms for quantum machine learning, enhancing performance on complex data classification tasks.

Findings

Achieves higher precision on complex quantum datasets.

Converges faster with fewer circuit evaluations.

Shows variable performance on simple classical data.

Abstract

Quantum machine learning is one of the fields where quantum computers are expected to bring advantages over classical methods. However, the limited size of current computers restricts the exploitation of the full potential of quantum machine learning methods. Additionally, different computing paradigms, both quantum and classical, each have their own strengths and weaknesses. Obtaining optimal results with algorithms thus requires algorithms to be tweaked to the underlying computational paradigm, and the tasks to be optimally distributed over the available computational resources. In this work, we explore the potential gains from combining different computing paradigms to solve the complex task of data classification for three different datasets. We use a gate-based quantum model to implement a quantum kernel and implement a complex feature map. Next, we formulate a quadratic unconstrained optimisation problem to be solved on quantum annealing hardware. We then evaluate the losses on classical hardware and reconfigure the model parameters accordingly. We tested this so-called triple-hybrid quantum support vector machine on various data sets, and find that it achieves higher precision than other support vector machines (both quantum and classical) on complex quantum data, whereas it achieves varying performance on simple classical data using limited training. For the complex data sets, the triple-hybrid version converges faster, requiring fewer circuit evaluations.