Quantum Information Processing
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Published By Springer-Verlag

1573-1332, 1570-0755

2022 ◽  
Vol 21 (1) ◽  
Jianwei Xu ◽  
Lin Zhang ◽  
Shao-Ming Fei

2022 ◽  
Vol 21 (1) ◽  
Yi-Ru Sun ◽  
Xiu-Bo Chen ◽  
Jun Shao ◽  
Song Han ◽  
Haibo Hong ◽  

2021 ◽  
Vol 21 (1) ◽  
Piotr Fra̧ckiewicz ◽  
Katarzyna Rycerz ◽  
Marek Szopa

AbstractThe aim of the paper is to study the problem of absentminded driver in the quantum domain. In the classical case, it is a well-known example of a decision problem with imperfect recall that exhibits lack of equivalence between mixed and behavioral strategies. The optimal payoff outcome is significantly lower than the maximum payoff appearing in the game. This raises the question whether a quantum approach to the problem can increase the strategic position of the decision maker. The results that we present in the paper clearly reveal the benefits from playing the absentminded problem with the aid of quantum objects. Through appropriately chosen initial quantum state, the unitary strategies enable the decision maker to obtain the maximum possible payoff. At the same time, our scheme comes down to the classical problem with a suitable restriction of unitary strategies.

2021 ◽  
Vol 21 (1) ◽  
Furkan Oz ◽  
Rohit K. S. S. Vuppala ◽  
Kursat Kara ◽  
Frank Gaitan

AbstractComputational fluid dynamics (CFD) simulations are a vital part of the design process in the aerospace industry. Although reliable CFD results can be obtained with turbulence models, direct numerical simulation of complex bodies in three spatial dimensions (3D) is impracticable due to the massive amount of computational elements. For instance, a 3D direct numerical simulation of a turbulent boundary-layer over the wing of a commercial jetliner that resolves all relevant length scales using a serial CFD solver on a modern digital computer would take approximately 750 million years or roughly 20% of the earth’s age. Over the past 25 years, quantum computers have become the object of great interest worldwide as powerful quantum algorithms have been constructed for several important, computationally challenging problems that provide enormous speed-up over the best-known classical algorithms. In this paper, we adapt a recently introduced quantum algorithm for partial differential equations to Burgers’ equation and develop a quantum CFD solver that determines its solutions. We used our quantum CFD solver to verify the quantum Burgers’ equation algorithm to find the flow solution when a shockwave is and is not present. The quantum simulation results were compared to: (i) an exact analytical solution for a flow without a shockwave; and (ii) the results of a classical CFD solver for flows with and without a shockwave. Excellent agreement was found in both cases, and the error of the quantum CFD solver was comparable to that of the classical CFD solver.

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