Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Time Travel Simulations using Quantum Algorithms: Theoretical Models, Scientific Debates, and Computational Possibilities -A Literature Review

Abid Hossain Rion, Mashrufa Meghla Any

Published Jan 1, 2025• 0 citations
Publisher
DOI
10.2139/ssrn.5381055

Related papers

Validation of quantum simulations: assessing efficiency and reliability in experimental implementations

Afonso Miguel Fernandes Rodrigues

Quantum simulation is one of the most relevant applications of quantum computation for the near future, due to its scientific impact and also because quantum simulation algorithms are typically less demanding than generalized quantum computations. Ultimately, the success of a quantum simulation depends on the amount and reliability of information one is able to extract from the results. In such a context, this work reviews the theory behind quantum simulation, with a focus on digital quantum simulation. The concepts of efficiency and reliability in quantum simulations are discussed, particularly for implementations of digital simulation algorithms in state-of-the-art quantum computers. A review of approaches for quantum characterization, verification and validation techniques (QCVV) is also presented. A digital quantum simulation of the Schrödinger equation for a single particle in 1 spatial dimension was experimentally implemented and analyzed, along with a quantum state tomography procedure for characterization of the final quantum state and evaluation of simulation reliability. From the literature, it is shown that digital quantum simulation is theoretically sound and experimentally feasible, with several applications in a wide range of physics-related fields. Nonetheless, a number of conditions arise that must be observed for a truly efficient implementation of a digital quantum simulation, from theoretical conception to experimental circuit design. The review of QCVV techniques highlights the need for characterization and validation techniques that could be efficiently implemented for current models of quantum computation, particularly in instances where classical verification is not tractable. However, there are proposals for efficient verification procedures when a set of parameters defining the final result of the simulation is known. The experimental simulation demonstrated partial success in comparison with an ideal quantum simulation. From the results it is apparent that better coherence times, better reliability and finer control are as decisive for the advancement of quantum computing power as the more-publicized number of qubits of a given device.

•Jan 1, 2018•0 citations

Quantum Computing: Algorithms and Applications - A Literature Review

Abid Hossain Rion, Mashrufa Meghla Any

•Jan 1, 2025•0 citations

The Quantum Physics of Time Travel

David Deutsch, Michael Lockwood

Scientific American•Mar 1, 1994•93 citations

Quantum Computing Role in Advancing Medical Physics Research

Alaa Salim Abdalrazzaq, Ameer Badr Khudhair, Nidhal Raoof Mahdi +2 more

Background: Quantum computing represents a paradigm leap in computational capabilities, with potential applications across various scientific disciplines. The complicated computations required for imaging, radiation therapy, and molecular modeling in medical physics give novel prospects for quantum computing. Objective: This article investigates the role of quantum computing in medical physics research, with an emphasis on its potential to improve computational efficiency and accuracy and develop novel treatment approaches. Methods: A thorough literature analysis was undertaken to investigate recent advances in quantum computing and their implications in medical physics. Case studies were reviewed to demonstrate practical applications and possible advantages. Theoretical models were tested to forecast future developments. Statistical data from quantum computing implementations were gathered from many sources, particularly on improving processing speed and accuracy in medical physics applications. Results: The findings show that quantum computing can significantly improve the processing speed of complex simulations and imaging techniques, with some quantum algorithms outperforming classical algorithms by up to 100 times. In radiation therapy planning, quantum computing has exhibited a 25% boost in dose distribution calculation precision. Quantum algorithms have lowered calculation times in molecular modeling by about 40%, potentially boosting drug development and customized therapy by 30%. Conclusion: Quantum computing has the potential to alter medical physics by increasing computational capacity, which could lead to advancements in diagnostics, treatment planning, and medication development. The statistical results corroborate the promise of quantum computing in these domains, emphasizing the importance of ongoing interdisciplinary collaboration and research to fully achieve these benefits and address present hurdles in incorporating quantum computing into clinical practice.

Radioelectronics Nanosystems Information Technologies•Sep 17, 2024•2 citations

Simulation of time-dependent quantum dynamics using quantum computers

Erik Lötstedt, Takanori Nishi, Kaoru Yamanouchi

Advances in atomic, molecular, and optical physics•Jan 1, 2024•0 citations

Modern finance through quantum computing—A systematic literature review

Liliana Bunescu, Andreea-Mădălina VÂRTEI

Human intellectual restlessness originates from the need for knowledge of the modern world. The financial world is struggling to prototype accurate and fast data at low risk. The quantum approach to finance can support this desire. The goal of this paper is to provide a comprehensive review of the literature on how quantum computing can be used in finance. This research aims to expose an architecture of the state of the art in quantum finance. In terms of methodology, the PSALSAR framework was used to conduct this systematic literature review. The selection procedure followed the PRISMA guidelines and was applied in two databases (Web of Science and Scopus) without time limit. In total, 94 out of 1646 articles were included for data extraction and assessment of content evaluation covering the period 2001-2023. The current review of quantum finance literature is structured around the following themes: journals, research methods, tested data series, research topics in quantum finance, and future research directions. Within the financial sector, quantum computing is used in three main areas: simulation, optimization, and machine learning. These areas are supported by algorithms that have been created in recent years. Finally, we propose to highlight the benefits and the applications of quantum finance and to stimulate the interest in the future prospects of the debates.

PLoS ONE•Jul 18, 2024•17 citations
Quantum simulation is one of the most relevant applications of quantum computation for the near future, due to its scientific impact and also because quantum simulation algorithms are typically less demanding than generalized quantum computations. Ultimately, the success of a quantum simulation depends on the amount and reliability of information one is able to extract from the results. In such a context, this work reviews the theory behind quantum simulation, with a focus on digital quantum simulation. The concepts of efficiency and reliability in quantum simulations are discussed, particularly for implementations of digital simulation algorithms in state-of-the-art quantum computers. A review of approaches for quantum characterization, verification and validation techniques (QCVV) is also presented. A digital quantum simulation of the Schrödinger equation for a single particle in 1 spatial dimension was experimentally implemented and analyzed, along with a quantum state tomography procedure for characterization of the final quantum state and evaluation of simulation reliability. From the literature, it is shown that digital quantum simulation is theoretically sound and experimentally feasible, with several applications in a wide range of physics-related fields. Nonetheless, a number of conditions arise that must be observed for a truly efficient implementation of a digital quantum simulation, from theoretical conception to experimental circuit design. The review of QCVV techniques highlights the need for characterization and validation techniques that could be efficiently implemented for current models of quantum computation, particularly in instances where classical verification is not tractable. However, there are proposals for efficient verification procedures when a set of parameters defining the final result of the simulation is known. The experimental simulation demonstrated partial success in comparison with an ideal quantum simulation. From the results it is apparent that better coherence times, better reliability and finer control are as decisive for the advancement of quantum computing power as the more-publicized number of qubits of a given device.
Background: Quantum computing represents a paradigm leap in computational capabilities, with potential applications across various scientific disciplines. The complicated computations required for imaging, radiation therapy, and molecular modeling in medical physics give novel prospects for quantum computing. Objective: This article investigates the role of quantum computing in medical physics research, with an emphasis on its potential to improve computational efficiency and accuracy and develop novel treatment approaches. Methods: A thorough literature analysis was undertaken to investigate recent advances in quantum computing and their implications in medical physics. Case studies were reviewed to demonstrate practical applications and possible advantages. Theoretical models were tested to forecast future developments. Statistical data from quantum computing implementations were gathered from many sources, particularly on improving processing speed and accuracy in medical physics applications. Results: The findings show that quantum computing can significantly improve the processing speed of complex simulations and imaging techniques, with some quantum algorithms outperforming classical algorithms by up to 100 times. In radiation therapy planning, quantum computing has exhibited a 25% boost in dose distribution calculation precision. Quantum algorithms have lowered calculation times in molecular modeling by about 40%, potentially boosting drug development and customized therapy by 30%. Conclusion: Quantum computing has the potential to alter medical physics by increasing computational capacity, which could lead to advancements in diagnostics, treatment planning, and medication development. The statistical results corroborate the promise of quantum computing in these domains, emphasizing the importance of ongoing interdisciplinary collaboration and research to fully achieve these benefits and address present hurdles in incorporating quantum computing into clinical practice.
Human intellectual restlessness originates from the need for knowledge of the modern world. The financial world is struggling to prototype accurate and fast data at low risk. The quantum approach to finance can support this desire. The goal of this paper is to provide a comprehensive review of the literature on how quantum computing can be used in finance. This research aims to expose an architecture of the state of the art in quantum finance. In terms of methodology, the PSALSAR framework was used to conduct this systematic literature review. The selection procedure followed the PRISMA guidelines and was applied in two databases (Web of Science and Scopus) without time limit. In total, 94 out of 1646 articles were included for data extraction and assessment of content evaluation covering the period 2001-2023. The current review of quantum finance literature is structured around the following themes: journals, research methods, tested data series, research topics in quantum finance, and future research directions. Within the financial sector, quantum computing is used in three main areas: simulation, optimization, and machine learning. These areas are supported by algorithms that have been created in recent years. Finally, we propose to highlight the benefits and the applications of quantum finance and to stimulate the interest in the future prospects of the debates.