Acta Physica Sinica

Experimental progress of quantum machine learning based on spin systems

Yu Peng Tian, Zidong Lin, Xiangyu Wang, Liangyu Che, Dawei Lu

Published Jan 1, 2021• 1 citation

Abstract

Machine learning is widely applied in various areas due to its advantages in pattern recognition, but it is severely restricted by the computing power of classic computers. In recent years, with the rapid development of quantum technology, quantum machine learning has been verified experimentally verified in many quantum systems, and exhibited great advantages over classical algorithms for certain specific problems. In the present review, we mainly introduce two typical spin systems, nuclear magnetic resonance and nitrogen-vacancy centers in diamond, and review some representative experiments in the field of quantum machine learning, which were carried out in recent years.

DOI: 10.7498/aps.70.20210684

Related papers

Experimental Quantum‐Enhanced Machine Learning in Spin‐Based Systems

Xiangyu Wang, Zidong Lin, Liangyu Che +2 more

Abstract With the advancement of computing power and algorithms, machine learning has been a powerful tool in numerous applications nowadays. However, the hardware limitation of classical computers and the increasing size of datasets urge the community to explore new techniques for machine learning. Quantum‐enhanced machine learning is such a rapidly growing field. It refers to quantum algorithms that are implemented in quantum computers, which can improve the computational speed of classical machine learning tasks and often promises an exponential speedup. In the past few years, the development of experimental quantum technologies leads to many experimental demonstrations of quantum‐enhanced machine learning in diverse physical systems. Here, the recent experimental progress in this field in two typical spin‐based quantum systems—nuclear magnetic resonance and nitrogen‐vacancy centers in diamond—is reviewed, and the ongoing challenges are discussed.

Advanced Quantum TechnologiesJun 11, 20229 citations

Machine Learning: Quantum vs Classical

Tariq Mahmood Khan, Antonio Robles‐Kelly

Encouraged by growing computing power and algorithmic development, machine learning technologies have become powerful tools for a wide variety of application areas, spanning from agriculture to chemistry and natural language processing. The use of quantum systems to process classical data using machine learning algorithms has given rise to an emerging research area, i.e. quantum machine learning. Despite its origins in the processing of classical data, quantum machine learning also explores the use of quantum phenomena for learning systems, the use of quantum computers for learning on quantum data and how machine learning algorithms and software can be formulated and implemented on quantum computers. Quantum machine learning can have a transformational effect on computer science. It may speed up the processing of information well beyond the existing classical speeds. Recent work has seen the development of quantum algorithms that could serve as foundations for machine learning applications. Despite its great promise, there are still significant hardware and software challenges that need to be resolved before quantum machine learning becomes practical. In this paper, we present an overview of quantum machine learning in the light of classical approaches. Departing from foundational concepts of machine learning and quantum computing, we discuss various technical contributions, strengths and similarities of the research work in this domain. We also elaborate upon the recent progress of different quantum machine learning approaches, their complexity, and applications in various fields such as physics, chemistry and natural language processing.

IEEE AccessJan 1, 2020213 citations

Harnessing the power of quantum systems based on spin magnetic resonance: from ensembles to single spins

Xing Rong, Dawei Lu, Xi Kong +5 more

Quantum computation provides great speedup over its classical counterpart for certain tasks. Spin system is one of the most important candidates to realize quantum computations. The initialization, readout and quantum gate operations of spin-qubits can be accomplished by advanced spin resonance techniques, which include nuclear magnetic resonance, electron paramagnetic resonance and optically detected magnetic resonance. This review aims to summarize recent experimental progresses in spin-based quantum computing. Preserving quantum states, precise quantum operations, quantum algorithms and quantum simulations on spin systems are reviewed. The fast improvement of quantum technology motivated by quantum computations results in recent progress in spin magnetic resonance in nanoscale. This review also introduces the achievements in nanoscale magnetic resonance with Nitrogen vacancy centers in diamonds.

Advances in Physics XDec 27, 201611 citations

Quantum Machine Learning: Scope for real-world problems

Abhishek Jadhav, Akhtar Rasool, Manasi Gyanchandani

Quantum computing with its inherent parallelism provides a quantum advantage over classical computing. Its potential to offer breakthrough advances in various areas of science and engineering is foreseen. Machine learning is one of the key areas where the power of quantum computing can be utilized. Though many machine learning algorithms have been successfully developed to solve a variety of problems in the past decades, these algorithms take a long time to train. Also working on today's colossal datasets makes these algorithms computationally intensive. Quantum machine learning by utilizing the concepts of superposition and entanglement promises a solution to this problem. Quantum machine learning algorithms are in surface for the past few years and majority of the current research has dealt with the two machine learning problems namely classification and clustering. In this paper, a brief review of the recent techniques and algorithms of quantum machine learning and its scope in solving real world problems is studied.

Procedia Computer ScienceJan 1, 202367 citations

Recent advances in quantum machine learning: a survey with a comparative analysis

Vassili Privalihhin

Quantum mechanics, a fundamental theory in physics that describes the behavior of nature at atomic and subatomic scales, offers significant advantages over classical physics in various contexts. These advances have laid the foundation for the field of quantum computing, which leverages the unique properties of quantum mechanics to solve complex computational problems more efficiently than classical computers. At the core of quantum computing are qubits, the quantum analogs of classical bits. Unlike classical bits, which are restricted to binary states (0 or 1), qubits utilize superposition and entanglement, enabling them to exist in multiple states simultaneously and interact in ways that amplify computational capabilities. This study presents an overview of quantum algorithms and tools for quantum machine learning, with a focus on recent advancements. Through a comparative analysis and empirical evidence, the research highlights the potential advantages of quantum algorithms in various applications, such as data processing, pattern recognition, and algorithmic complexity. The findings suggest that quantum methods may surpass their classical counterparts in certain domains. However, a key challenge remains: the current limitations of quantum hardware. Despite the theoretical benefits, practical implementation is constrained by the noisy and error-prone nature of quantum devices. Consequently, the experiments conducted in this study were performed in simulation environments, which demonstrated potential improvements when applying quantum paradigms.

Dec 19, 20240 citations

Experimental Evaluation of Quantum Machine Learning Algorithms

Ricardo Daniel Monteiro Simoes, Patrick Huber, Nicola Meier +3 more

Machine learning and quantum computing are both areas with considerable progress in recent years. The combination of these disciplines holds great promise for both research and practical applications. Recently there have also been many theoretical contributions of quantum machine learning algorithms with experiments performed on quantum simulators. However, most questions concerning the potential of machine learning on quantum computers are still unanswered such as How well do current quantum machine learning algorithms work in practice? How do they compare with classical approaches? Moreover, most experiments use different datasets and hence it is currently not possible to systematically compare different approaches. In this paper we analyze how quantum machine learning can be used for solving small, yet practical problems. In particular, we perform an experimental analysis of kernel-based quantum support vector machines and quantum neural networks. We evaluate these algorithm on 5 different datasets using different combinations of quantum feature maps. Our experimental results show that quantum support vector machines outperform their classical counterparts on average by 3 to 4% in accuracy both on a quantum simulator as well as on a real quantum computer. Moreover, quantum neural networks executed on a quantum computer further outperform quantum support vector machines on average by up to 5% and classical neural networks by 7%.

IEEE AccessJan 1, 202366 citations