In this survey, we aim to explore the fundamental question of whether the next generation of artificial intelligence requires quantum computing. Artificial intelligence is increasingly playing a crucial role in many aspects of our daily lives and is central to the fourth industrial revolution. It is therefore imperative that artificial intelligence is reliable and trustworthy. However, there are still many issues with reliability of artificial intelligence, such as privacy, responsibility, safety, and security, in areas such as autonomous driving, healthcare, robotics, and others. These problems can have various causes, including insufficient data, biases, and robustness problems, as well as fundamental issues such as computability problems on digital hardware. The cause of these computability problems is rooted in the fact that digital hardware is based on the computing model of the Turing machine, which is inherently discrete. Notably, our findings demonstrate that digital hardware is inherently constrained in solving problems about optimization, deep learning, or differential equations. Therefore, these limitations carry substantial implications for the field of artificial intelligence, in particular for machine learning. Furthermore, although it is well known that the quantum computer shows a quantum advantage for certain classes of problems, our findings establish that some of these limitations persist when employing quantum computing models based on the quantum circuit or the quantum Turing machine paradigm. In contrast, analog computing models, such as the Blum-Shub-Smale machine, exhibit the potential to surmount these limitations.

翻译：本综述旨在探讨一个根本性问题：下一代人工智能是否需要量子计算。人工智能正日益在我们日常生活的诸多方面扮演关键角色，并处于第四次工业革命的核心地位。因此，确保人工智能的可靠性和可信赖性至关重要。然而，人工智能在自动驾驶、医疗健康、机器人等领域仍存在诸多可靠性问题，如隐私、责任、安全与安保等。这些问题可能源于多种原因，包括数据不足、偏差和鲁棒性问题，以及数字硬件上的可计算性等根本性挑战。这些可计算性问题的根源在于，数字硬件基于图灵机的计算模型，而该模型本质上是离散的。值得注意的是，我们的研究结果表明，数字硬件在解决优化、深度学习或微分方程等问题时存在固有局限。因此，这些局限性对人工智能领域，尤其是机器学习领域，具有重要影响。此外，尽管众所周知量子计算机在特定类别问题上展现出量子优势，但我们的研究证实，基于量子电路或量子图灵机范式的量子计算模型在解决某些局限性时仍存在不足。相比之下，模拟计算模型（如Blum-Shub-Smale机）则展现出克服这些局限的潜力。