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Communicating P systems: Bio-inspired computational models for complex systems 20

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Communicating P systems: Bio-inspired computational models f...

20th Conference Information Technologies - applicationsand Theory, ITAT 2020

作者： Csuhaj-Varjú, Erzsébet Department of Algorithms and Their Applications Faculty of Informatics Eötvös Loránd University Budapest Hungary

The theory of P systems or membrane systems, a vivid scientific area in bio-inspired computing, deals with computational models inspired by architecture and functioning of living cells and tissues, and neural systems. This paper contains ideas on why and how purely communicating P systems (important variants of P systems) can be interpreted as complex natural systems. We give a summary of the most relevant results concerning these P systems and provide their interpretation in terms of complex systems. We propose open problems and new directions for future research. Copyright © 2020 for this paper by its authors.

关键词： Computational methods

Communicating reaction systems with direct communication 21

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Communicating reaction systems with direct communication

21st International Conference on Membrane Computing, CMC 2020

作者： Csuhaj-Varjú, Erzsébet Sethy, Pramod Kumar Department of Algorithms and Their Applications Faculty of Informatics Eötvös Loránd University ELTE Budapest Hungary

ISBN: (纸本)9783030771010

We introduce and examine two variants of networks of reaction systems, called communicating reaction systems with direct communication, where the reaction systems send products or reactions to each other. We show that these types of networks of reaction systems can be obtained by simple mappings from single reaction systems. We also discuss some aspects of communication within these networks, and suggest open problems for future research. © Springer Nature Switzerland AG 2021.

关键词： Computers

Surrogate-based optimization for variational quantum algorithms

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arXiv 2022年

作者： Shaffer, Ryan Kocia, Lucas Sarovar, Mohan Department of Physics University of California BerkeleyCA94720 United States Quantum Algorithms and Applications Collaboratory Sandia National Laboratories LivermoreCA94551 United States

Variational quantum algorithms are a class of techniques intended to be used on near-term quantum computers. The goal of these algorithms is to perform large quantum computations by breaking the problem down into a large number of shallow quantum circuits, complemented by classical optimization and feedback between each circuit execution. One path for improving the performance of these algorithms is to enhance the classical optimization technique. Given the relative ease and abundance of classical computing resources, there is ample opportunity to do so. In this work, we introduce the idea of learning surrogate models for variational circuits using few experimental measurements, and then performing parameter optimization using these models as opposed to the original data. We demonstrate this idea using a surrogate model based on kernel approximations, through which we reconstruct local patches of variational cost functions using batches of noisy quantum circuit results. Through application to the quantum approximate optimization algorithm and preparation of ground states for molecules, we demonstrate the superiority of surrogate-based optimization over commonly-used optimization techniques for variational algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum computers

Spectral Gaps via Imaginary Time

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arXiv 2023年

作者： Leamer, Jacob M. Magann, Alicia B. Baczewski, Andrew D. McCaul, Gerard Bondar, Denys I. Department of Physics and Engineering Physics Tulane University 6823 St. Charles Ave. New OrleansLA70118 United States Quantum Algorithms and Applications Collaboratory Sandia National Laboratories AlbuquerqueNM87185 United States

The spectral gap occupies a role of central importance in many open problems in physics. We present an approach for evaluating the spectral gap of a Hamiltonian from a simple ratio of two expectation values, both of which are evaluated using a quantum state that is evolved in imaginary time. In principle, the only requirement is that the initial state is supported on both the ground and first excited states. We demonstrate this approach for the Fermi-Hubbard and transverse field Ising models through numerical simulation. © 2023, CC BY.

关键词： Excited states

Randomized adaptive quantum state preparation

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arXiv 2023年

作者： Magann, Alicia B. Economou, Sophia E. Arenz, Christian Quantum Algorithms and Applications Collaboratory Sandia National Laboratories AlbuquerqueNM87185 United States Department of Physics Virginia Tech BlacksburgVA24061 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

关键词： Cost functions

Shining light on data: Geometric data analysis through quantum dynamics

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arXiv 2022年

作者： Kumar, Akshat Sarovar, Mohan Department of Mathematics Clarkson University PotsdamNY13699 United States Instituto de Telecomunicações Lisbon Portugal Quantum Algorithms and Applications Collaboratory Sandia National Laboratories LivermoreCA94550 United States

Experimental sciences have come to depend heavily on our ability to organize, interpret and analyze high-dimensional datasets produced from observations of a large number of variables governed by natural processes. Natural laws, conservation principles, and dynamical structure introduce intricate inter-dependencies among these observed variables, which in turn yield geometric structure, with fewer degrees of freedom, on the dataset. We show how fine-scale features of this structure in data can be extracted from discrete approximations to quantum mechanical processes given by data-driven graph Laplacians and localized wavepackets. This data-driven quantization procedure leads to a novel, yet natural uncertainty principle for data analysis induced by limited data. We illustrate the new approach with algorithms and several applications to real-world data, including the learning of patterns and anomalies in social distancing and mobility behavior during the COVID-19 pandemic. Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19

Quantum simulation of exact electron dynamics can be more efficient than classical mean-field methods

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arXiv 2023年

作者： Babbush, Ryan Huggins, William J. Berry, Dominic W. Ung, Shu Fay Zhao, Andrew Reichman, David R. Neven, Hartmut Baczewski, Andrew D. Lee, Joonho Google Quantum AI VeniceCA United States Department of Physics and Astronomy Macquarie University SydneyNSW Australia Department of Chemistry Columbia University New YorkNY United States Department of Physics and Astronomy University of New Mexico AlbuquerqueNM United States Quantum Algorithms and Applications Collaboratory Sandia National Laboratories AlbuquerqueNM United States

Quantum algorithms for simulating electronic ground states are slower than popular classical mean-field algorithms such as Hartree-Fock and density functional theory, but offer higher accuracy. Accordingly, quantum computers have been predominantly regarded as competitors to only the most accurate and costly classical methods for treating electron correlation. However, here we tighten bounds showing that certain first quantized quantum algorithms enable exact time evolution of electronic systems with exponentially less space and polynomially fewer operations in basis set size than conventional real-time time-dependent Hartree-Fock and density functional theory. Although the need to sample observables in the quantum algorithm reduces the speedup, we show that one can estimate all elements of the k-particle reduced density matrix with a number of samples scaling only polylogarithmically in basis set size. We also introduce a more efficient quantum algorithm for first quantized mean-field state preparation that is likely cheaper than the cost of time evolution. We conclude that quantum speedup is most pronounced for finite temperature simulations and suggest several practically important electron dynamics problems with potential quantum advantage. Copyright © 2023, The Authors. All rights reserved.

关键词： Density functional theory

Quantum simulation of weak-field light-matter interactions

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arXiv 2021年

作者： Young, Steve M. Häffner, Hartmut Sarovar, Mohan Quantum Algorithms and Applications Collaboratory Sandia National Laboratories AlbuquerqueNM87185 United States Department of Physics University of California BerkeleyCA94720 United States Quantum Algorithms and Applications Collaboratory Sandia National Laboratories LivermoreCA94550 United States

Simulation of the interaction of light with matter, including at the few-photon level, is important for understanding the optical and optoelectronic properties of materials, and for modeling next-generation non-linear spectroscopies that use entangled light. At the few-photon level the quantum properties of the electromagnetic field must be accounted for with a quantized treatment of the field, and then such simulations quickly become intractable, especially if the matter subsystem must be modeled with a large number of degrees of freedom, as can be required to accurately capture many-body effects and quantum noise sources. Motivated by this we develop a quantum simulation framework for simulating such light-matter interactions on platforms with controllable bosonic degrees of freedom, such as vibrational modes in the trapped ion platform. The key innovation in our work is a scheme for simulating interactions with a continuum field using only a few discrete bosonic modes, which is enabled by a Green's function (response function) formalism. We develop the simulation approach, sketch how the simulation can be performed using trapped ions, and then illustrate the method with numerical examples. Our work expands the reach of quantum simulation to important light-matter interaction models and illustrates the advantages of extracting dynamical quantities such as response functions from quantum simulations. Copyright © 2021, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)

Feedback-based quantum optimization

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arXiv 2021年

作者： Magann, Alicia B. Rudinger, Kenneth M. Grace, Matthew D. Sarovar, Mohan Quantum Algorithms and Applications Collaboratory Sandia National Laboratories LivermoreCA94550 United States Quantum Algorithms and Applications Collaboratory Sandia National Laboratories AlbuquerqueNM87185 United States Department of Chemical & Biological Engineering Princeton University PrincetonNJ08544 United States

It is hoped that quantum computers will offer advantages over classical computers for combinatorial optimization. Here, we introduce a feedback-based strategy for quantum optimization, where the results of qubit measurements are used to constructively assign values to quantum circuit parameters. We show that this procedure results in an estimate of the combinatorial optimization problem solution that improves monotonically with the depth of the quantum circuit. Importantly, the measurement-based feedback enables approximate solutions to the combinatorial optimization problem without the need for any classical optimization effort, as would be required for the quantum approximate optimization algorithm (QAOA). We experimentally demonstrate this feedback-based protocol on a superconducting quantum processor for the graph-partitioning problem MaxCut, and present a series of numerical analyses that further investigate the protocol’s performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Combinatorial optimization