A Review of Quantum Machine Learning Algorithms Based on Variational Quantum Circuit

Yu Ruiqi; Zhang Xinyun; Ren Shuang

doi:10.7544/issn1000-1239.202330979

Journal of Computer Research and Development > 2025 > Corrected proof > DOI: 10.7544/issn1000-1239.202330979 CSTR: 32373.14.issn1000-1239.202330979

Yu Ruiqi, Zhang Xinyun, Ren Shuang. A Review of Quantum Machine Learning Algorithms Based on Variational Quantum Circuit[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202330979

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A Review of Quantum Machine Learning Algorithms Based on Variational Quantum Circuit

School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044

Funds: This work was supported by the National Natural Science Foundation of China (62072025).

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Author Bio:
Yu Ruiqi: born in 2002. Master candidate. His main research interests include quantum computing and machine learning

Zhang Xinyun: born in 1997. PhD. Member of CCF. His main research interests include machine learning, 3D computer vision, and quantum computing

Ren Shuang: born in 1981. PhD, associate professor, PhD supervisor. Member of CCF. His main research interests include machine learning, quantum computing, and 3D computer vision
Received Date: December 04, 2023
Revised Date: November 18, 2024
Accepted Date: January 08, 2025
Available Online: January 08, 2025

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Abstract

Abstract

As the scale of available data increases, the importance and impact of machine learning grows. It has been found that quantum computing can be realized with the help of the principles of quantum mechanics, and the quantum machine learning algorithm formed by combining quantum computing and machine learning can theoretically produce exponential acceleration advantages over classical machine learning algorithms. Quantum versions of many classical algorithms have been proposed and they may solve problems that are difficult to classical computers. At present, limited by the quantum computing hardware, the number of controllable qubits, noise, and other factors restrict the development of quantum computers. Quantum computing hardware is unlikely to reach the level needed for universal quantum computers in the short term, and current research focuses on the algorithms that can run on noisy intermediate-scale quantum (NISQ) computers. Variational quantum algorithms (VQAs) are hybrid quantum classical algorithms which are suitable for current quantum computing devices. Related research is one of the research hotspots in the field of quantum machine learning. Variational quantum circuits (VQCs) are parameterized quantum circuits (PQCs) used in variational quantum algorithms to solve quantum machine learning tasks. It is also be called Ansatz and quantum neural networks (QNNs). The framework of variational quantum algorithm mainly contains five steps: 1) Designing the loss function according to the task. Designing parameterized quantum circuits as model and initializing parameters. 2) Embedding classical data. The classical data is pre-processed and encoded to the quantum state. If quantum data is used as input, it only needs to be pre-processed without encoding. 3) Calculating the loss function through parameterized quantum circuit. This step is where quantum advantage comes in. 4) Measuring and post-processing. Through quantum measurement operation, the quantum superposition state wave packet collapses into classical state. The classical data can be obtained after post-processing. 5) Optimizing the parameters. Updating parameters and optimizing the model with classical optimization algorithms and then returning to step 3 until the loss function converges after several iterations. We can obtain a set of optimal parameters. The final result is the output of the optimal model. This paper reviews the basic theory of quantum computing and the basic framework of variational quantum algorithm, and further introduces the application and progress of variational quantum algorithm in the field of quantum machine learning, then reviews supervised quantum machine learning including quantum classifiers, unsupervised quantum machine learning including quantum circuit born machine, variational quantum Boltzmann machine and quantum autoencoder, semi-supervised quantum learning including quantum generative adversarial network, quantum reinforcement learning, and quantum circuit architecture search in detail. Next, this paper compares the models and analyses their advantages and disadvantages, and briefly discusses and summarizes the related datasets and simulation platforms that can reproduce the introduced models. Finally, this paper puts forward the challenges and future research trends of quantum machine learning algorithms based on variational quantum circuit.

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References (167)

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