Applicability of quantum-enhanced machine learning (要旨)

ユウダイ スズキ, Yudai Suzuki, 雄大 鈴木

Sohum Thakkar, Skander Kazdaghli, Natansh Mathur +3 more

Abstract Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications. In this work, we show how quantum machine learning can be used to improve financial forecasting. First, we use classical and quantum Determinantal Point Processes to enhance Random Forest models for churn prediction, improving precision by almost 6%. Second, we design quantum neural network architectures with orthogonal and compound layers for credit risk assessment, which match classical performance with significantly fewer parameters. Our results demonstrate that leveraging quantum ideas can effectively enhance the performance of machine learning, both today as quantum-inspired classical ML solutions, and even more in the future, with the advent of better quantum hardware.

V. K. R. Rajeswari Satuluri, Vijayakumar Ponnusamy

Quantum Computing (QC) has come into view as an emerging technology. Machine Learning(ML) and QC have become ubiquitous in recent years. This study has focused on surveying the enhancement of ML with QC. Applications of QC in different fields are thoroughly discussed. The Biomedical/Healthcare field of research has potential with QC-enhanced ML (QML). Advancements in an amalgamation of ML with QC are further elaborated. QML concepts are introduced with current research advances. The survey ends with challenges in the new technology and future work considerations.

K. Saru Nivedha, M. Vijayalakshmi

Marco Marcozzi, Ernestas Filatovas, Remigijius Paulavičius

Keqin Li, Peng Zhao, Shuying Dai +6 more

This paper delves into the integration of machine learning and quantum computing, highlighting the potential of quantum computing to enhance the performance and computational efficiency of machine learning. Through theoretical analysis and experimental studies, this paper demonstrates how quantum computing can accelerate traditional machine learning algorithms via its unique properties of superposition and entanglement, particularly in handling large datasets and solving high-dimensional problems. Detailed introductions to quantum-enhanced machine learning models such as quantum neural networks and quantum support vector machines are provided, and their efficacy is validated through experimental applications in tasks like handwriting digit recognition. Results indicate that the parallel processing capabilities of quantum computing significantly enhance the speed and precision of model training, while also addressing the challenges and potential solutions for practical applications of quantum computing. Finally, the paper discusses future research directions and the importance of interdisciplinary collaboration in the integration of machine learning and quantum computing.