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Artificial intelligence (AI) for quantum and quantum for AI

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Abstract

The technological fields of AI and quantum technology have evolved in parallel, and have demonstrated considerable potential to complement each other. Amalgamation of them refers to the use of AI techniques to develop algorithms for quantum computing (QC) and quantum physics, as well as the use of QC to enhance AI applications. QC has the potential to revolutionize various fields. Controlling quantum systems is notoriously difficult, which is one of the major obstacles standing in the way of widespread use of QC. AI has opened up new avenues for automated control of quantum systems. In particular, the application of AI can provide invaluable insight into the complex and multifaceted domain of quantum physics to accelerate the discovery of quantum physics laws, and can potentially alleviate challenges that have been historically associated with QC and quantum communication. On the other hand, QC can also be used to enhance AI applications. For instance, QC can be used to haste the training of neural networks, which are used in machine learning. Concurrently, a series of advancements in quantum technology can serve to drive innovation in the realm of machine learning by enabling the development of novel algorithms, frameworks, and hardware. This article presents a comprehensive overview on the reciprocal relationship between AI and quantum technology, emphasizing the utility of AI in the field of quantum technology, and the potential of quantum technology to catalyze the evolution of AI.

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The datasets used and/or analyzed during the current study areavailable from the corresponding author on reasonable request.

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Authors and Affiliations

  1. China Telecom Research Institute, Guangzhou, China

    Yingzhao Zhu & Kefeng Yu

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  1. Yingzhao Zhu
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  2. Kefeng Yu
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YZ wrote the main manuscript text, KY prepared Figs. 1–8. All authors reviewed the manuscript.

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Correspondence to Yingzhao Zhu.

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Zhu, Y., Yu, K. Artificial intelligence (AI) for quantum and quantum for AI. Opt Quant Electron 55, 697 (2023). https://doi.org/10.1007/s11082-023-04914-6

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