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Related papers: Markov Process Simulation with Quantum Computer

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The possibility of simulating a stochastic process by the intrinsic randomness of quantum system is investigated. Two simulations of Markov Chains by the measurements of quantum systems are proposed.

Mathematical Physics · Physics 2009-09-28 X. F. Liu

Numerical simulation of quantum systems is crucial to further our understanding of natural phenomena. Many systems of key interest and importance, in areas such as superconducting materials and quantum chemistry, are thought to be described…

Quantum Physics · Physics 2010-11-17 Katherine L Brown , William J Munro , Vivien M Kendon

Quantum computing promises to revolutionize several scientific and technological domains through fundamentally new ways of processing information. Among its most compelling applications is digital quantum simulation, where quantum computers…

Quantum Physics · Physics 2026-02-05 Laurin E. Fischer

A new method for simulation of a binary homogeneous Markov process using a quantum computer was proposed. This new method allows using the distinguished properties of the quantum mechanical systems -- superposition, entanglement and…

Quantum Physics · Physics 2021-03-12 Petar Nikolov

This brief article gives an overview of quantum mechanics as a {\em quantum probability theory}. It begins with a review of the basic operator-algebraic elements that connect probability theory with quantum probability theory. Then quantum…

Quantum Physics · Physics 2020-02-04 Hendra I. Nurdin

We study how generators of Markovian dynamics of a qubit can be simulated using a programmable quantum processor.

Quantum Physics · Physics 2007-05-23 Matyas Koniorczyk , Vladimir Buzek , Peter Adam , Akos Laszlo

Simulations of stochastic processes play an important role in the quantitative sciences, enabling the characterisation of complex systems. Recent work has established a quantum advantage in stochastic simulation, leading to quantum devices…

Quantum Physics · Physics 2019-05-20 Farzad Ghafari , Nora Tischler , Carlo Di Franco , Jayne Thompson , Mile Gu , Geoff J. Pryde

The difficulty of simulating quantum systems, well-known to quantum chemists, prompted the idea of quantum computation. One can avoid the steep scaling associated with the exact simulation of increasingly large quantum systems on…

Classical simulation is important because it sets a benchmark for quantum computer performance. Classical simulation is currently the only way to exercise larger numbers of qubits. To achieve larger simulations, sparse matrix processing is…

Quantum Physics · Physics 2007-05-23 John Robert Burger

Quantum algorithms can potentially solve a handful of problems more efficiently than their classical counterparts. In that context, it has been discussed that Markov chains problems could be solved significantly faster using quantum…

Quantum Physics · Physics 2025-12-01 Baptiste Claudon , Jean-Philip Piquemal , Pierre Monmarché

Simulating quantum mechanics is known to be a difficult computational problem, especially when dealing with large systems. However, this difficulty may be overcome by using some controllable quantum system to study another less controllable…

Quantum Physics · Physics 2014-03-14 I. M. Georgescu , S. Ashhab , Franco Nori

A quantum computer promises efficient processing of certain computational tasks that are intractable with classical computer technology. While basic principles of a quantum computer have been demonstrated in the laboratory, scalability of…

Quantum Physics · Physics 2007-05-23 Robert Raussendorf , Hans J. Briegel

Quantum computing, an innovative computing system carrying prominent processing rate, is meant to be the solutions to problems in many fields. Among these realms, the most intuitive application is to help chemical researchers correctly…

Quantum Physics · Physics 2022-12-29 Qingchun Wang , Huan-Yu Liu , Qing-Song Li , Jianyu Zhao , Qiankun Gong , Ye Li , Yu-Chun Wu , Guo-Ping Guo

Simulating the stochastic evolution of real quantities on a digital computer requires a trade-off between the precision to which these quantities are approximated, and the memory required to store them. The statistical accuracy of the…

Quantum Physics · Physics 2017-10-16 Andrew J. P. Garner , Qing Liu , Jayne Thompson , Vlatko Vedral , Mile Gu

Quantum computers are not yet up to the task of providing computational advantages for practical stochastic diffusion models commonly used by financial analysts. In this paper we introduce a class of stochastic processes that are both…

Quantum Physics · Physics 2023-11-03 Eric Ghysels , Jack Morgan , Hamed Mohammadbagherpoor

We discuss a model for quantum computing with initially mixed states. Although such a computer is known to be less powerful than a quantum computer operating with pure (entangled) states, it may efficiently solve some problems for which no…

Quantum Physics · Physics 2015-03-17 Michael Siomau , Stephan Fritzsche

We show that the time evolution of the wave function of a quantum mechanical many particle system can be implemented very efficiently on a quantum computer. The computational cost of such a simulation is comparable to the cost of a…

Quantum Physics · Physics 2009-10-30 Christof Zalka

We present a novel method for solving eigenvalue problems on a quantum computer based on spectroscopy. The method works by coupling a "probe" qubit to a set of system simulation qubits and then time evolving both the probe and the system…

Quantum Physics · Physics 2023-02-14 John P. T. Stenger , Gilad Ben-Shach , David Pekker , Nicholas T. Bronn

Stochastic processes underlie a vast range of natural and social phenomena. Some processes such as atomic decay feature intrinsic randomness, whereas other complex processes, e.g. traffic congestion, are effectively probabilistic because we…

This paper describes an algorithmic system called SQT for the computer simulation of a wide class of quantum experiments on entangled particles. SQT maintains a hidden internal state for each particle and it provides an initialization…

Physics Education · Physics 2024-11-28 John R Rankin
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