The rapid accumulation of big data in the Internet era has gradually decelerated the progress of Artificial Intelligence (AI). As Moore’s Law approaches its limit, it is imperative to break the constraints that are holding back artificial intelligence. Quantum computing and artificial intelligence have been advancing along the highway of human civilization for many years, emerging as new engines driving economic and social development. This article delves into the integration of quantum computing and artificial intelligence in both research and application. It introduces the capabilities of both universal quantum computers and special-purpose quantum computers that leverage quantum effects. The discussion further explores how quantum computing enhances classical artificial intelligence from four perspectives: quantum supervised learning, quantum unsupervised learning, quantum reinforcement learning, and quantum deep learning. In an effort to address the limitations of smart cities, this article explores the formidable potential of quantum artificial intelligence in the realm of smart cities. It does so by examining aspects such as intelligent transportation, urban operation assurance, urban planning, and information communication, showcasing a plethora of practical achievements in the process. In the foreseeable future, Quantum Artificial Intelligence (QAI) is poised to bring about revolutionary development to smart cities. The urgency lies in developing quantum artificial intelligence algorithms that are compatible with quantum computers, constructing an efficient, stable, and adaptive hybrid computing architecture that integrates quantum and classical computing, preparing quantum data as needed, and advancing controllable qubit hardware equipment to meet actual demands. The ultimate goal is to shape the next generation of artificial intelligence that possesses common sense cognitive abilities, robustness, excellent generalization capabilities, and interpretability.

In order to solve the problems of road traffic congestion and the increasing parking time caused by the imbalance of parking lot supply and demand, this paper proposes an asymptotically optimal public parking lot location algorithm based on intuitive reasoning to optimize the parking lot location problem. Guided by the idea of intuitive reasoning, we use walking distance as indicator to measure the variability among location data and build a combinatorial optimization model aimed at guiding search decisions in the solution space of complex problems to find optimal solutions. First, Selective Attention Mechanism (SAM) is introduced to reduce the search space by adaptively focusing on the important information in the features. Then, Quantum Annealing (QA) algorithm with quantum tunneling effect is used to jump out of the local extremum in the search space with high probability and further approach the global optimal solution. Experiments on the parking lot location dataset in Luohu District, Shenzhen, show that the proposed method has improved the accuracy and running speed of the solution, and the asymptotic optimality of the algorithm and its effectiveness in solving the public parking lot location problem are verified.