A Review of Machine Learning Classification Using Quantum Annealing for Real-World Applications

Abstract

Optimizing the training of a machine learning pipeline helps in reducing training costs and improving model performance. One such optimizing strategy is quantum annealing, which is an emerging computing paradigm that has shown potential in optimizing the training of a machine learning model. The implementation of a physical quantum annealer has been realized by D-wave systems and is available to the research community for experiments. Recent experimental results on a variety of machine learning applications using quantum annealing have shown interesting results where the performance of classical machine learning techniques is limited by limited training data and high dimensional features. This article explores the application of D-wave's quantum annealer for optimizing machine learning pipelines for real-world classification problems. We review the application domains on which a physical quantum annealer has been used to train machine learning classifiers. We discuss and analyze the experiments performed on the D-Wave quantum annealer for applications such as image recognition, remote sensing imagery, computational biology, and particle physics. We discuss the possible advantages and the problems for which quantum annealing is likely to be advantageous over classical computation.

Implementation of Quantum Annealing: A Systematic Review

“…Machine learning has been widely applied in different sectors such as health, finance, autonomous driving, security, and others [38]. However, it was observed in Table XII to be facing different challenges stemming from several factors such as the scale of the data generated, hardware limitations, computational complexity, and cost.…”

From Quantum Graph Computing to Quantum Graph Learning: A Survey

“…To overcome these problems, the authors of [Bass et al, 2018] present a heterogeneous algorithm that uses classical co-processing to preprocess the primitive problem and randomly selects a large number of possible solutions, after which the reduced form of the max clique problem corresponding to the largest possible solutions can be encoded into the quantum annealer for accelerating the searching process. In [Pelofske et al, 2019], a decomposition alternative divides a big input graph into multiple smaller subgraphs that fit the quantum annealer to further improve the adaptability. Other applications also show the benefits of using Hamiltonian to encode and solve graph related problems including graph isomorphism problem [Calude et al, 2017;Ushijima-Mwesigwa et al, 2017] and vertex cover problem [Pelofske et al, 2019].…”

“…In [Pelofske et al, 2019], a decomposition alternative divides a big input graph into multiple smaller subgraphs that fit the quantum annealer to further improve the adaptability. Other applications also show the benefits of using Hamiltonian to encode and solve graph related problems including graph isomorphism problem [Calude et al, 2017;Ushijima-Mwesigwa et al, 2017] and vertex cover problem [Pelofske et al, 2019]. Likewise, recent works demonstrate quantum annealing has potential advantages over classical approaches ranging from optimization [Brady et al, 2021] to machine learning [Nath et al, 2021].…”

“…Other applications also show the benefits of using Hamiltonian to encode and solve graph related problems including graph isomorphism problem [Calude et al, 2017;Ushijima-Mwesigwa et al, 2017] and vertex cover problem [Pelofske et al, 2019]. Likewise, recent works demonstrate quantum annealing has potential advantages over classical approaches ranging from optimization [Brady et al, 2021] to machine learning [Nath et al, 2021]. However, it is diffi-cult to ensure the final quantum state to be the ground state, meaning that the final solution is probably not optimal.…”

Quantum Annealing and Computation

“…[14], while for discussions on the efficiency of quantum versus classical annealing in the context of learning problems, the readers may consult ref. [15] (see [16] for a recent review on machine learning using quantum annealing). For a discussion on fault-tolerant quantum heuristics in the context of combinatorial optimizations, one may consult ref.…”