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Quantum computing, leveraging the principles of quantum mechanics, has been found to significantly enhance computational capabilities in principle, in some cases beyond classical computing limits. This paper explores quantum computing’s potential to address complex, large-scale problems in transportation systems. It focuses on three main paradigms: gate-based quantum computing, quantum annealing, and quantum machine learning.
Abstract Closed timelike curves are among the most controversial features of modern physics. As legitimate solutions to Einstein’s field equations, they allow for time travel, which instinctively seems paradoxical. However, in the quantum regime these paradoxes can be resolved, leaving closed timelike curves consistent with relativity.
While there has been some recent success on alternative models of CTCs based on post-selection23, 24, 25, we focus on the most prominent model for describing quantum mechanics in the presence of CTCs, introduced by Deutsch6. Here a quantum state |ψ〉 interacts unitarily with an older version of itself (Fig. 1). With the inclusion of an additional swap gate, this can equivalently be treated as a two-qubit system, where a chronology-respecting qubit interacts with a qubit ρ trapped in a CTC.
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