Quantum Computation and Quantum Annealing

Charles R. Giardina

Hadi Salloum, Ali Salloum, Manuel Mazzara +1 more

Quantum computing (QC) has become a fascinating and popular topic due to its broad range of applications, particularly in machine learning (ML). The intersection of QC and ML is known as Quantum Machine Learning (QML). QML is a field that investigates how QC can enhance ML, making it one of the most exciting areas of research due to the potential of QC in solving complex problems. In this paper, we demonstrate the Quantum Annealing (QA) approach to improving ML in binary classification tasks. We implemented the QBoost algorithm on a few datasets using D-Wave’s quantum computers, specifically the Advantage 1 and Advantage 2 prototypes, incorporating the new feature Fast Anneal.

Catherine C. McGeoch

Jargalsaikhan Artag, Moe Shimada, Jun‐ichi Shirakashi

Quantum computing offers new paradigms in machine learning, notably through Quantum Annealing (QA), which offers promising approaches to optimization problems. This study introduces an application of Multi-tasking Quantum Annealing (MTQA) to enhance multi-class Support Vector Machines (SVMs). Traditional QA methods require multiple cycles to classify multi-class datasets, significantly increasing computational demands. In contrast, MTQA embeds multiple SVM classifiers in parallel on a quantum annealer, thereby reducing the number of required annealing cycles to a single cycle. Utilizing the D-Wave Advantage 6.4 system, we evaluated our method across three benchmark datasets: a synthetic dataset (Blob), subsets of handwritten digits, and the Iris dataset with different feature focuses. Our findings demonstrate that MTQA achieves accuracy equivalent to that of classical Sequential Minimal Optimization (SMO), Simulated Annealing (SA), and standard QA methods while significantly improving computational efficiency. Despite the current limitations in quantum technology and the empirical selection of model parameters, these results highlight the emergent potential of quantum computing in machine learning. Further advancements in quantum algorithm optimization and parameter tuning are anticipated to enhance the efficacy of QA and MTQA, solidifying their practical application roles.

Wesley O'Quinn, Shiwen Mao

Quantum annealing (QA) is a different technology from gate-model quantum computing. This research proposes a novel technique for solving the Minimum Spanning Tree (MST) (or, the minimum weight spanning tree) problem on a quantum annealer. The problem is of interest due to its applications in clustering, unsupervised learning, network design, and image processing to name a few. The advent of quantum cloud computing has provided access to quantum computing tools, previously unavailable to the general community. D-Wave systems recently released cloud access to their quantum annealer type hardware, which this project leverages to provide a novel solution method to the MST problem.