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Simulação em alta performance de caminhadas quânticas de multipartículas interativas com Apache Spark

Albuquerque, André Luiz Figueiredo de

Published Mar 1, 2018• 0 citations
OpenAlex

Abstract

Although many quantum algorithms have been developed in the last few decades with considerable speedup when compared to their best classical counterparts, the task of building a general purpose quantum computer is still a technological challenge. While the hardware necessary to run quantum algorithms is not available, researchers rely on classical simulations. However, the most interesting simulations are very demanding of computational resources due to the amount of data growing exponentially with the instance sizes and, thus, high performance computing techniques are necessary. Multiparticle quantum walks have been receiving a great deal of attention recently as a tool for designing quantum algorithms and for modeling physical phenomena. In the present work, we show that Apache Spark, a framework for large-scale data processing, can be used to simulate quantum walks with multiple interacting particles, with instance sizes that are impractical on single-processor, general-purpose computers. We also provide a prototype for quantum walks simulations, suitable to computer clusters, being developed atop of Spark.

Related papers

High-performance simulation of interacting multiparticle quantum walks with Apache Spark

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Embora muitos algoritmos quânticos tem sido desenvolvidos nas ultimas decadas com consideraveis ganhos em complexidade quando comparados a seus equivalentes classicos, a construcao de um computador quântico de proposito geral ainda e um desafio tecnologico. Enquanto o hardware necessario para rodar algoritmos quânticos ainda nao esta disponivel, pesquisadores dependem de simulacoes classicas. Entretanto, as simulacoes mais interessantes demandam grandes quantidades de recurso computacional devido a quantidade de dados crescer exponencialmente em relacao ao tamanho das instâncias e, assim, tecnicas de computacao de alto desempenho sao necessarias. Caminhadas quânticas de multiparticulas tem recebido grande atencao recentemente como uma ferramenta para desenvolvimento de algoritmos quânticos e para modelagem de fenomenos fisicos. No presente trabalho, nos mostramos que o Apache Spark, um arcabouco para processamento de dados em larga escala, pode ser usado para simular caminhadas quânticas de multiparticulas interativas, com tamanhos que sao impraticaveis em computadores de proposito geral e com apenas um processador. Nos tambem disponibilizamos um prototipo para simulacoes de caminhadas quânticas, adequado para clusters de computadores, desenvolvido utilizando o Spark.

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With the dramatic growth of experimental data in high-energy physics and the increasing demand for accuracy of physical analysis results, the current classical computing model and computing power can no longer meet the needs of data processing and analysis. As a new computing device with great potential, quantum computers have become one of the possible ways to solve the bottleneck of high-energy physics data processing and computation, though quantum computers are still far away from general computing. At such a stage that quantum computers have so many shortcomings like few physical qubits, short coherent time, and poor fidelity to solve most of practical problems, it's necessary to build a development and simulation platform using classical computers to accelerate research on quantum algorithms and applications. We've set up an interactive development platform for quantum algorithms research and quantum computing simulation, and are preparing one web portal for public education and training on quantum computing. In the future, we will pay more attention to quantum computing applications in HEP and QCD simulation.

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Although many quantum algorithms have been developed in the last few decades with considerable speedup when compared to their best classical counterparts, the task of building a general purpose quantum computer is still a technological challenge. While the hardware necessary to run quantum algorithms is not available, researchers rely on classical simulations. However, the most interesting simulations are very demanding of computational resources due to the amount of data growing exponentially with the instance sizes and, thus, high performance computing techniques are necessary. Multiparticle quantum walks have been receiving a great deal of attention recently as a tool for designing quantum algorithms and for modeling physical phenomena. In the present work, we show that Apache Spark, a framework for large-scale data processing, can be used to simulate quantum walks with multiple interacting particles, with instance sizes that are impractical on single-processor, general-purpose computers. We also provide a prototype for quantum walks simulations, suitable to computer clusters, being developed atop of Spark.
Embora muitos algoritmos quânticos tem sido desenvolvidos nas ultimas decadas com consideraveis ganhos em complexidade quando comparados a seus equivalentes classicos, a construcao de um computador quântico de proposito geral ainda e um desafio tecnologico. Enquanto o hardware necessario para rodar algoritmos quânticos ainda nao esta disponivel, pesquisadores dependem de simulacoes classicas. Entretanto, as simulacoes mais interessantes demandam grandes quantidades de recurso computacional devido a quantidade de dados crescer exponencialmente em relacao ao tamanho das instâncias e, assim, tecnicas de computacao de alto desempenho sao necessarias. Caminhadas quânticas de multiparticulas tem recebido grande atencao recentemente como uma ferramenta para desenvolvimento de algoritmos quânticos e para modelagem de fenomenos fisicos. No presente trabalho, nos mostramos que o Apache Spark, um arcabouco para processamento de dados em larga escala, pode ser usado para simular caminhadas quânticas de multiparticulas interativas, com tamanhos que sao impraticaveis em computadores de proposito geral e com apenas um processador. Nos tambem disponibilizamos um prototipo para simulacoes de caminhadas quânticas, adequado para clusters de computadores, desenvolvido utilizando o Spark.
We describe software Quandoop, a novel simulator of discrete-time quantum walks using high-performance computing (HPC) with the goal of handling simulations with very large Hilbert spaces.Those simulations are usually restricted to a relatively small number of steps due to memory limitations or reduced processing power.The most important feature of Quandoop when compared to previous simulators is to provide a low cost solution for simulations of quantum walks with extreme memory requirements.Examples of such simulations include quantum walks on fractals or with multiple interacting walkers.Quandoop uses Apache Hadoop to parallelize the calculations on a computer cluster, thus reducing the amount of time required to perform the quantum walk simulation and allowing its execution with more steps.Our simulator takes the input as text files containing the initial state, the evolution operators and the description of simulation parameters.This approach allows the integration with other quantum walk simulators such as HiPerWalk or QWalk.After running the necessary calculations in parallel, Quandoop outputs text files describing the results associated to the quantum walk.
The last two decades feature an ever-increasing interest in quantum information processing - generalization of the classical computational models in the view of quantum mechanics (QM). In theory, quantum computational devices are capable of solving in polynomial time problems for which only algorithms with exponential time complexity are known. Such enormous advantage explains the global scale of scientific efforts for creating the quantum computer - physical realization of such computational device. While the results of those efforts are still constrained inside the large experimental laboratories, an adequate tool for studying quantum algorithms will be of great help for educating the next generation of computer scientists - the engineers that will be responsible for operating these devices. The paper describes a quantum computer simulation model that exhibits a certain attractive property - the simulation slowdown does not depend directly on the size of the input data but rather on the complexity of the quantum state, meaning that transformations upon less-entangled data are performed faster. Based on this property the simulation can be spread between the nodes of a grid cluster in a way as to keep the entanglement in each job minimal
Quantum computing is a developing field that applies the properties of quantum mechanics to develop solutions to complex problems that the most powerful classical computers cannot solve or cannot solve efficiently. Solving difficult tasks requires sophisticated and efficient algorithms that can utilize stochastic processes as the primary techniques. The typical models of these stochastic processes include discrete-time random walks and continuous ones. Quantum walks represent a quantum extension of discrete random walks, whose demonstrated utility in algorithm design has motivated signi cant interest in quantum computational research. Therefore, the purpose of this thesis is to explore the potential of discrete-time coined quantum walks using the Python package with high-performance computing (HPC) compared to classical walks by conducting a literature review. To supplement this research, some simulations are implemented to analyze the spreading behavior of coined quantum walks, compare them with classical walks, and demonstrate their application in search tasks, such as binary hypercube search algorithm. These simulations aim to clarify the extent to which quantum walks can be effectively modeled and applied in computational contexts. The results gained show that coined quantum walks exhibit faster spreading than classical random walks, which contributes to a deeper understanding of potential applications of quantum walks, such as quantum simulation, in the future.
This thesis describes the design and implementation of an environment to allow quantum computation to be simulated on classical computers. Although it is believed that quantum computers cannot in general be efficiently simulated classically, it is nevertheless possible to simulate small but interesting systems, on the order of a few tens of quantum bits. Since the state of the art of physical implementations is less than 10 bits, simulation remains a useful tool for understanding the behavior of quantum algorithms. To create a suitable envrionment for simulation, we constructed a 32-node cluster of workstation class computers linked with a high speed (gigabit Ethernet) network. We then wrote an initial simulation environment based on parallel linear algebra libraries with a Matlab front end. These libraries operated on large matrices representing the problem being simulated. The parallel Matlab environment demonstrated a degree of parallel speedup as we added processors, but overall execution times were high, since the amount of data scaled exponentially with the size of the problem. This increased both the number of operations that had to be performed to compute the simulation, and the volume of data that had to be communicated between the nodes as they were computing. The scaling also affected memory utilization, limiting us to a maximum problem size of 14 qubits. In an attempt to increase simulation efficiency, we revisited the design of the simulation environment. Many quantum algorithms have a structure that can be described using the tensor product operator from linear algebra. We believed that a new simulation environment based on this tensor product structure would be substantially more efficient than one based on large matrices. We designed a new simulation envrionment that exploited this tensor product structure. Benchmarks that we performed on the new simulation environment confirmed that it was substantially more efficient, allowing us to perform simulations of the quantum Fourier transform and the discrete approximation to the solution of 3-SAT by adiabatic evolution up to 25 qubits in a reasonable time. Thesis Supervisor: Stephen A. Benton Title: Professor of Media Arts and Sciences
With the dramatic growth of experimental data in high-energy physics and the increasing demand for accuracy of physical analysis results, the current classical computing model and computing power can no longer meet the needs of data processing and analysis. As a new computing device with great potential, quantum computers have become one of the possible ways to solve the bottleneck of high-energy physics data processing and computation, though quantum computers are still far away from general computing. At such a stage that quantum computers have so many shortcomings like few physical qubits, short coherent time, and poor fidelity to solve most of practical problems, it's necessary to build a development and simulation platform using classical computers to accelerate research on quantum algorithms and applications. We've set up an interactive development platform for quantum algorithms research and quantum computing simulation, and are preparing one web portal for public education and training on quantum computing. In the future, we will pay more attention to quantum computing applications in HEP and QCD simulation.