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In recent years, electric vehicles (EVs) have emerged as a sustainable alternative to conventional automobiles. Distinguished by their environmental friendliness, superior performance, reduced noise, and low maintenance requirements, EVs offer numerous advantages over traditional vehicles. The integration of electric vehicles with cloud computing has heralded a transformative shift in the automotive industry. However, as EVs become increasingly interconnected with the internet, various devices, and infrastructure, they become susceptible to cyberattacks. These attacks pose a significant risk to the safety, privacy, and functionality of both the vehicles and the broader transportation infrastructure. In this paper, we delve into the topic of electric vehicles and their connectivity to the cloud. We scrutinize the potential attack vectors that EVs are vulnerable to and the consequential impact on vehicle operations. Moreover, we outline both general and specific strategies aimed at thwarting these cyberattacks. Additionally, we anticipate future developments aimed at enhancing EV performance and reducing security risks. 

In recent years, computing has been moving rapidly from the centralized cloud to various edges. For instance, electric vehicles (EVs), one of the next-generation computing platforms, have grown in popularity as a sustainable alternative to conventional vehicles. Compared with traditional ones, EVs have many unique advantages, such as less environmental pollution, high energy utilization efficiency, simple structure, and convenient maintenance etc. Meanwhile, it is also currently facing lots of challenges, including short cruising range, long charging time, inadequate supporting facilities, cyber security risks, etc. Nevertheless, electric vehicles are still developing as a future industry, and the number of users keeps growing, with governments and companies around the world continuously investing in promoting EV-related supply chains. As an emerging and important computing platform, we comprehensively study electric vehicular systems and state-of-the-art EV-related technologies. Specifically, this paper outlines electric vehicles’ history, major architecture and components in hardware and software, current state-of-the-art technologies, and anticipated future developments to reduce drawbacks and difficulties. 

Quantum computing is gaining momentum in revolutionizing the way we approach complex problem-solving. However, the practical implementation of quantum algorithms remains a significant challenge due to the error-prone and hardware limits of near-term quantum devices. For instance, physical qubit connections are limited, which necessitates the use of quantum SWAP gates to dynamically transform the logical topology during execution. In addition, to optimize fidelity, it is essential to ensure that 1) the allocated hardware has a low error rate and 2) the number of SWAP gates injected into the circuit is minimized. To address these challenges, we propose a suite of algorithms: the Fidelity-aware Graph Extraction Algorithm (FGEA) is used to identify the hardware region with the lowest probability of error, the Frequency-based Mapping Algorithm (FMA) allocates logical-physical qubits that reduce the potential distance of topological transformation, and the Heuristic Routing Algorithm (HRA) searches for an optimal swapping injection strategy. We evaluate the proposed algorithms on the IBM-provided Noisy Intermediate-Scale Quantum (NISQ) computer, using a dataset consisting of 17 different quantum circuits of various sizes. The circuits are executed on the IBM Toronto Falcon processor. The three proposed algorithms outperform the existing SABRE algorithm in reducing the number of SWAP gates required. Therefore, our proposed algorithms hold significant promise in enhancing the fidelity and reducing the number of SWAP gates required in implementing Quantum algorithms. 

Quantum annealing (QA) is a promising optimization technique used to find global optimal solution of a combinatorial optimization problem by leveraging quantum fluctuations. In QA, the problem being solved is mapped onto the quantum processing unit (QPU) composed of qubits through a procedure called minor-embedding. The qubits are connected by a network of couplers, which determine the strength of the interactions between the qubits. The strength of the couplers that connect qubits within a chain is often referred to as the chain strength. The appropriate balance of chain strength is equally imperative in enabling the qubits to interact with one another in a way that is strong enough to obtain the optimal solution, but not excessively strong so as not to bias the original problem terms. To this end, we address the problem of identifying the optimal chain strength through the utilization of Path Integral Monte Carlo (PIMC) quantum simulation algorithm. The results indicate that our judicious choice of chain strength parameter facilitates enhancements in quantum annealer performance and solution quality, thereby paving the way for QA to compete with, or potentially outperform, classical optimization algorithms.