A Review of Research and Applications of Variational Quantum Algorithms

Junjie Zheng; Junkai Li; Pan Xu; Shaoxuan Yang; Daoshan Zheng; Chaoji Yu

doi:10.54097/5rxtfv88

Authors

Junjie Zheng
Junkai Li
Pan Xu
Shaoxuan Yang
Daoshan Zheng
Chaoji Yu

DOI:

https://doi.org/10.54097/5rxtfv88

Keywords:

Variational Quantum Algorithms; Quantum Machine Learning; Quantum Neural Networks; Network Attack Detection

Abstract

In recent years, as datasets have grown larger, quantum computing has shown potential in addressing the rapidly increasing costs of data processing due to its superposition and entanglement capabilities. Limited by quantum hardware and the number of controllable qubits, variational quantum algorithms (VQAs) are algorithms suitable for NISQ devices, capable of computation on medium-scale noisy devices. VQAs are hybrid classical-quantum algorithms that use parameterized quantum circuits to prepare parameterized quantum states and employ classical optimizers to minimize objective functions, enabling efficient solutions for complex problems. Classical optimizers reduce the stringent requirements on quantum hardware and can achieve quantum advantage on current devices. This paper systematically elaborates on the core foundational theories of quantum computing and neural networks, with a focus on reviewing the research and application status of VQAs in three main scenarios: machine learning (supervised, unsupervised, and reinforcement learning), solving systems of equations (linear and nonlinear), and network attack detection. It analyzes the current technical bottlenecks in this field and provides prospects for future development, offering a reference for further research on variational quantum algorithms.

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