In the increasing traffic scenario and the presence of automated four-wheeler vehicle-like situations, we need to consider the possible safe driving for the two-wheelers also. As two-wheelers need manual balances, embedding the autopilot systems is a tougher task. To resolve this issue, we attempt to address this problem with probability spaces. Then, these classical probability spaces are promoted into quantum probability spaces. The quantum computing aspects such as quantum gates are embedded in the analysis of the quantum probability space. With those quantum computing protocols, various traffic scenarios are calculated, such as the probability of accidents and the avoidance probability of accidents. A set of mathematical lemmas with certain conditions and their proof for mapping classical to quantum probability space and the singularities in this quantum probability space are also discussed in this work.
In the increasing traffic scenario and the presence of automated four-wheeler vehicle-like situations, we need to consider the possible safe driving for the two-wheelers also. As two-wheelers need manual balances, embedding the autopilot systems is a tougher task. To resolve this issue, we attempt to address this problem with probability spaces. Then, these classical probability spaces are promoted into quantum probability spaces. The quantum computing aspects such as quantum gates are embedded in the analysis of the quantum probability space. With those quantum computing protocols, various traffic scenarios are calculated, such as the probability of accidents and the avoidance probability of accidents. A set of mathematical lemmas with certain conditions and their proof for mapping classical to quantum probability space and the singularities in this quantum probability space are also discussed in this work.
This work provides an updated report on the progress and prospects of quantum computing for advancing transport modeling. Despite recent developments in classical methods, their use in computationally intensive transport tasks such as traffic assignment, traffic signal control, logistics, and pedestrian modeling remains limited due to the combinatorial complexity and uncertainty associated with the real-world transport scenarios. Quantum optimization algorithms offer the potential for addressing transport problems, although most studies still remain at the proof-of-concept stage. In this work, we highlight key challenges in traffic and logistics optimization and investigate how quantum optimization tools could lead to novel and efficient solutions. Our perspective offers a clear and concise timeline for the development of quantum optimization techniques and their practical implementation aspects by highlighting key hardware/software challenges and proposed solutions. We also discuss the barriers to adoption within transport contexts and possible pathways to overcome current limitations to the application of quantum optimization techniques in real-world transport systems. Finally, we provide our perspective for the applications of quantum computing in addressing challenges pertaining to large crowd management and transport optimization during the Olympics and Paralympics 2032 Games which may coincide with the development of a large-scale quantum computer.
Quantum-enhanced probability distribution algorithms represent a significant advancement in computational modeling, particularly for complex probabilistic scenarios. This paper explores the potential of leveraging quantum mechanics to dramatically improve the accuracy and speed of Monte Carlo simulations, addressing the inherent limitations of classical approaches. The core claim is to develop a novel algorithm that utilizes quantum superposition and entanglement to efficiently explore the space of possible distributions, thereby reducing computational cost and enhancing performance. The proposed mechanism centers around exploiting quantum phenomena to overcome the computational bottlenecks encountered in traditional Monte Carlo methods, offering a paradigm shift in probabilistic modeling. This work will detail the theoretical foundations of the proposed algorithm, its implementation and testing with illustrative examples, and its potential impact on various applications.
Integrating quantum computing and artificial intelligence (AI) in automotive safety presents a paradigm shift, addressing complex challenges such as real-time traffic management, accident prevention, and system optimization. Quantum computing's principles of superposition, entanglement, and parallelism enhance AI's ability to process and analyze vast datasets, enabling precise and efficient decision-making in dynamic environments. Industry leaders like BMW, Volkswagen, and Waymo demonstrate the transformative potential of quantum-enhanced systems with applications in traffic optimization, autonomous navigation, and predictive maintenance. However, hardware scalability, ethical concerns, and regulatory gaps persist.
Quantum entanglement, a cornerstone of quantum mechanics, presents a profound challenge to our classical understanding of reality. This research investigates the potential of using a probabilistic framework to rigorously analyze entanglement, offering a pathway toward a deeper comprehension of its fundamental properties. We propose to construct a probability space representation of entangled states, leveraging the unique characteristics of entanglement to generate a more accurate and precise probability distribution than traditional classical methods. This work aims to delineate the inherent probabilistic nature of entanglement and explore its implications for quantum computation and information processing. Specifically, we will detail a methodology for representing entangled states within a probabilistic framework, demonstrating the potential for enhanced accuracy in quantum state estimation and analysis.
Mission planning for multiple unmanned aerial vehicles (UAVs) is a complex problem that is expected to be solved by quantum computing. With the increasing application of UAVs, the demand for efficient conflict management strategies to ensure airspace safety continues to increase. In the era of noisy intermediate-scale quantum (NISQ) devices, variational quantum algorithms (VQA) for optimizing parameterized quantum circuits with the help of classical optimizers are currently one of the most promising strategies to gain quantum advantage. In this paper, we propose a mathematical model for the UAV collision avoidance problem that maps the collision avoidance problem to a quadratic unconstrained binary optimization (QUBO) problem. The problem is formulated as an Ising Hamiltonian, then the ground state is solved using two kinds of VQAs: the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA). We select conditional value-at-risk (CVaR) to further promote the performance of our model. Four examples are given to validate that with our method the probability of obtaining a feasible solution can exceed 90% based on appropriate parameters, and our method can enhance the efficiency of a UAVs' collision avoidance model.
We discuss a new approach to simulate quantum algorithms using classical probabilistic bits and circuits. Each qubit (a two-level quantum system) is initially mapped to a vector in an eight dimensional probability space (equivalently, to a classical random variable with eight probabilistic outcomes). The key idea in this mapping is to store both the amplitude and phase information of the complex coefficients that describe the qubit state in the probabilities. Due to the identical tensor product structure of combining multiple quantum systems as well as multiple probability spaces, n qubits are then mapped to a tensor product of n 8-dimensional probabilistic vectors (i.e., the Hilbert space of dimension 2n is mapped to a probability space of dimension 8n). After this initial mapping, we show how to implement the analogs of single-qubit and two-qubit gates in the probability space using correlation-inducing operations on these classical random variables. The key defining feature of both the mapping to the probability space and the transformations in this space (i.e., operations on the random variables) is that they are not linear, but instead affine. Using this architecture, the evolution of the 2n complex coefficients of the quantum system can be tracked in the joint fully-correlated probabilities of the polynomial number of random variables. We then give specific procedures for implementing (1) the Deutsch-Jozsa algorithm, and (2) the Quantum Fourier Transform in the probability space. Identical to the Quantum case, simulating the Quantum Fourier Transform in the probability space requires O(n) probabilistic bits and O(n2) (i.e., quadratic in the number of quantum bits) operations.