Currently, the challenge lies in the traditional intelligent algorithm’s ability to effectively address the e-hailing repositioning issue. Accurately identifying the underlying characteristics in extensive traffic data within a limited timeframe is difficult, ultimately preventing the achievement of the most optimal solution. This paper suggests a hybrid computing architecture involving reinforcement learning and quantum annealing based on intuitive reasoning. Intuitive reasoning aims to enhance performance in scenarios with poor system robustness, complex tasks, and diverse goals. A deep learning model is constructed, trained to extract scene features, and combined with expert knowledge, then transformed into a quantum annealable form. The final strategy is obtained using a D-wave quantum computer with quantum tunneling effect, which helps in finding optimal solutions by jumping out of local suboptimal solutions. Based on 400000 real data, four algorithms are compared: minimum-cost flow, sequential markov decision process, hot-dot strategy, and driver-prefer strategy. The average total revenue increases by about 10% and vehicle utilization by about 15% in various scenarios. In summary, the proposed architecture effectively solves the e-hailing reposition problem, offering new directions for robust artificial intelligence in big data decision problems.

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.

This work is the first to determine that a real quantum computer (including generalized and specialized) can decipher million-scale RSA relying solely on quantum algorithms, showing the real attack potential of D-Wave machines. The influence of different column widths on RSA factorization results is studied on the basis of a multiplication table, and the optimal column method is determined by traversal experiments. The traversal experiment of integer factorization within 10 000 shows that the local field and coupling coefficients are 75%–93% lower than the research of Shanghai University in 2020 and more than 85% lower than that of Purdue University in 2018. Extremely low Ising model parameters are crucial to reducing the hardware requirements, prompting factoring 1245407 on the D-Wave 2000Q real machine. D-Wave advantage already has more than 5000 qubits and will be expanded to 7000 qubits during 2023–2024, with remarkable improvements in decoherence and topology. This machine is expected to promote the solution of large-scale combinatorial optimization problems. One of the contributions of this paper is the discussion of the long-term impact of D-Wave on the development of post-quantum cryptography standards.

The intelligent transportation system (ITS) integrates a variety of advanced science and technology to support and monitor road traffic systems and accelerate the urbanization process of various countries. This paper analyzes the shortcomings of ITS, introduces the principle of quantum computing and the performance of universal quantum computer and special-purpose quantum computer, and shows how to use quantum advantages to improve the existing ITS. The application of quantum computer in transportation field is reviewed from three application directions: path planning, transportation operation management, and transportation facility layout. Due to the slow development of the current universal quantum computer, the D-Wave quantum machine is used as a breakthrough in the practical application. This paper makes it clear that quantum computing is a powerful tool to promote the development of ITS, emphasizes the importance and necessity of introducing quantum computing into intelligent transportation, and discusses the possible development direction in the future.