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Journal of Computational Interdisciplinary Sciences

Quandoop: A Classical Simulator of Quantum Walks on Computer Clusters

David Rodrigues de Souza, Franklin de Lima Marquezino, ALEXANDRE ANTÔNIO DE LIMA JUNIOR

Published Jan 1, 2017• 0 citations
Publisher

Abstract

The Ordinary Kriging (OK) algorithm is an important method used in geostatistics applications for data interpolation usually based on field measurements of a variable of interest.The OK algorithm is used to estimate the value of a variable based on the variance of previous measurements.The OK algorithm has applications in areas such as environmental science, hydrogeology, mining, weather prediction and remote sensing.However, the OK algorithm is many times not suitable for large volumes of data due to its computational cost.In this work we present a study of a parallel implementation of the OK algorithm that takes advantage of heterogeneous computing environments through the usage of the Open Computing Language (OpenCL) framework.We use the OK algorithm to estimate a Digital Terrain Model (DTM) from a point cloud of a mangrove forest obtained via a terrestrial laser 3D scanning device.A downsampling procedure is applied to the raw data in order to extract the points that belong to the ground and then the OK algorithm is applied to obtain the DTM.The objective of our implementation is to effectively use all the computational resources available to accelerate the OK procedure.We show that our implementation can scale across multiple CPUs and GPUs devices and it is able to handle volumes of data that are typically produced by real world field measurements.The interpolation is a crucial step to make predictions of a variable at unknown locations, it is important to use the maximum amount of measurements possible in order to increase the performance of the prediction algorithm.Reducing the time to generate the interpolation model is, therefore, extremely important.We also compare the run times of a reference sequential implementation with our parallel one to show that the usage of parallel programming techniques can accelerate the fundamental steps of many important scientific researches.

DOI
10.6062/jcis.2017.08.03.0131

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The Ordinary Kriging (OK) algorithm is an important method used in geostatistics applications for data interpolation usually based on field measurements of a variable of interest.The OK algorithm is used to estimate the value of a variable based on the variance of previous measurements.The OK algorithm has applications in areas such as environmental science, hydrogeology, mining, weather prediction and remote sensing.However, the OK algorithm is many times not suitable for large volumes of data due to its computational cost.In this work we present a study of a parallel implementation of the OK algorithm that takes advantage of heterogeneous computing environments through the usage of the Open Computing Language (OpenCL) framework.We use the OK algorithm to estimate a Digital Terrain Model (DTM) from a point cloud of a mangrove forest obtained via a terrestrial laser 3D scanning device.A downsampling procedure is applied to the raw data in order to extract the points that belong to the ground and then the OK algorithm is applied to obtain the DTM.The objective of our implementation is to effectively use all the computational resources available to accelerate the OK procedure.We show that our implementation can scale across multiple CPUs and GPUs devices and it is able to handle volumes of data that are typically produced by real world field measurements.The interpolation is a crucial step to make predictions of a variable at unknown locations, it is important to use the maximum amount of measurements possible in order to increase the performance of the prediction algorithm.Reducing the time to generate the interpolation model is, therefore, extremely important.We also compare the run times of a reference sequential implementation with our parallel one to show that the usage of parallel programming techniques can accelerate the fundamental steps of many important scientific researches.
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 Hiperwalk package is designed to facilitate the simulation of quantum walks using heterogeneous high-performance computing, taking advantage of the parallel processing power of diverse processors such as CPUs, GPUs, and acceleration cards. This package enables the simulation of both the continuous-time and discrete-time quantum walk models, effectively modeling the behavior of quantum systems on large graphs. Hiperwalk features a user-friendly Python package frontend with comprehensive documentation, as well as a high-performance C-based inner core that leverages parallel computing for efficient linear algebra calculations. This versatile tool empowers researchers to better understand quantum walk behavior, optimize implementation, and explore a wide range of potential applications, including spatial search algorithms.
Hybrid quantum-HPC algorithms advance research by delegating complex tasks to quantum processors and using HPC systems to orchestrate workflows and complementary computations. Sample-based quantum diagonalization (SQD) is a hybrid quantum-HPC method in which information from a molecular Hamiltonian is encoded into a quantum circuit for evaluation on a quantum computer. A set of measurements on the quantum computer yields electronic configurations that are filtered on the classical computer, which also performs diagonalization on the selected subspace and identifies configurations to be carried over to the next step in an iterative process. Diagonalization is the most demanding task for the classical computer. Previous studies used the Fugaku supercomputer and a highly scalable diagonalization code designed for CPUs. In this work, we describe our efforts to enable efficient scalable and portable diagonalization on heterogeneous systems using GPUs as the main compute engines based on the previous work. GPUs provide massive on-device thread-level parallelism that is well aligned with the algorithms used for diagonalization. We focus on the computation of ground-state energies and wavefunctions using the Davidson algorithm with a selected set of electron configurations. We describe the offload strategy, code transformations, and data-movement, with examples of measurements on the Frontier supercomputer and five other GPU accelerated systems. Our measurements show that GPUs provide an outstanding performance boost of order 100x on a per-node basis. This dramatically expedites the diagonalization step-essential for extracting ground and excited state energies-bringing the classical processing time down from hours to minutes.
If classical algorithms have been successful in reproducing the estimation of expectation values of observables of some quantum circuits using off-the-shelf computing resources, matching the performance of the most advanced quantum devices on sampling problems usually requires extreme cost in terms of memory and computing operations, making them accessible to only a handful of supercomputers around the world. In this work, we demonstrate for the first time a classical simulation outperforming Gaussian boson sampling experiments of one hundred modes on established benchmark tests using a single CPU or GPU. Being embarrassingly parallelizable, a small number of CPUs or GPUs allows us to match previous sampling rates that required more than one hundred GPUs. We believe algorithmic and implementation improvements will generalize our tools to photo-counting, single-photon inputs, and pseudo-photon-number-resolving scenarios beyond one thousand modes. Finally, most of the innovations in our tools remain valid for generic probability distributions over binary variables, rendering it potentially applicable to the simulation of qubit-based sampling problems and creating classical surrogates for classical-quantum algorithms.
The article introduces Q-ClusterSim, a resource-efficient hybrid quantum-classical framework designed to accelerate clustering operations on large-scale and high-dimensional datasets. The proposed model combines quantum-enhanced k-means and density-based clustering within a unified quantum simulation environment to achieve improved computational efficiency and scalability. By leveraging the parallelism of quantum circuits and the adaptability of classical optimization, Q-ClusterSim significantly reduces execution time while maintaining clustering accuracy. Simulations are conducted using IBM Qiskit and TensorFlow Quantum on benchmark datasets, where the proposed model consistently outperforms classical clustering algorithms in terms of convergence speed, precision, and resource consumption. Results validate the model’s effectiveness in managing complex data structures under limited qubit resources, making it suitable for real-world large data applications. This study highlights the transformative potential of quantum-assisted clustering in reshaping future data analytics and computational intelligence paradigms..
Quantum annealing (QA) is a method to find the global optimum of a combinatorial optimization problem by using quantum fluctuations. Quantum annealers such as D-wave are efficient to solve small problems with less than 2048 variables. Simulated quantum annealing on digital computers allows us to solve large real-world problems. However, the processing time increases exponentially with the number of variables. This article proposes a highly-parallel accelerator for simulated quantum annealing exploiting spatial and temporal parallelism. The accelerator is implemented using “open computing language (OpenCL)” on FPGA. For 8,192 spin models, we achieve 145 times speed using 32 Trotters in one FPGA and 290 times speed-up using 64 Trotters in two FPGAs, compared to single-core CPU implementation.