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

Workshop-School on Theoretical Computer Science

qGMC-Analyzer -- Quantum Simulation on Multicore Architectures

Murilo Schmalfuss, Renata Reiser, Mauricio Lima Pilla, Adriano Maron

Published Oct 1, 2013• 0 citations
Publisher

Abstract

This paper presents an extension of the VPE-qGM execution environment in order to support parallel execution using OpenMP. As a relevant part of a broader project represented by the simulation framework D-GM which aims to achieve quantum simulation on hybrid architectures (clusters, multicore CPUs and GPUs), this work enables the control and execution of the simulation from multicore architectures. This proposal is shown by providing relevant management simulation on multicore CPUs, which will be later combined with GPU targeted implementations. From the synergy between these implementations, this work aims to establish a general platform for hybrid simulation, exploring different architectures usually found in clusters and expanding the capabilities of the simulation environment VPE-qGM.

DOI
10.1109/weit.2013.25

Related papers

HybriD-GM: A Framework for Quantum Computing Simulation Targeted to Hybrid Parallel Architectures

Anderson Ávila, Hélida Santos, Anderson Paiva Cruz +5 more

This paper presents the HybriD-GM model conception, from modeling to consolidation. The D-GM environment is also extended, providing efficient parallel executions for quantum computing simulations, targeted to hybrid architectures considering the CPU and GPU integration. By managing projection operators over quantum structures, and exploring coalescing memory access patterns, the HybriD-GM model enables granularity control, optimizing hardware resources in distributed computations organized as tree data structures. In the HybriD-GM evaluation, simulations of Shor's and Grover's algorithms achieve significant performance improvements in comparison to the previous D-GM version, and also with other related works, for example, LIQUi|⟩ and ProjectQ simulators.

Entropy•Mar 14, 2023•5 citations

Expanding the VPE-qGM Environment Towards a Parallel Quantum Simulation of Quantum Processes Using GPUs

Adriano Maron, Renata Reiser, Mauricio Lima Pilla +1 more

Quantum computing proposes quantum algorithms exponentially faster than their clas- sical analogues when executed by a quantum computer. As quantum computers are currently unavailable for general use, one approach for analyzing the behavior and re- sults of such algorithms is the simulation using classical computers. As this simulation is inefficient due to the exponential growth of the temporal and spatial complexities, solutions for these two problems are essential in order to increase the simulation capa- bilities of any simulator. This work proposes the development of a methodology defined by two main steps: the first consists of the sequential implementation of the abstractions corresponding to the Quantum Processes and Quantum Partial Processes defined in the qGM model for reduction in memory consumption related to multidimensional quantum transformations; the second is the parallel implementation of such abstractions allowing its execution on GPUs. The results obtained by this work embrace the sequential simu- lation of controlled transformations up to 24 qubits. In the parallel simulation approach, Hadamard gates up to 20 qubits were simulated with a speedup of ? 50× over an 8-core parallel simulation, which is a significant performance improvement in the VPE-qGM environment when compared with its previous limitations.

CLEI electronic journal•Dec 1, 2013•1 citation

HybriD-GM: Um Modelo Paralelo para Computação Quântica direcionado às Arquiteturas Híbridas

Anderson Ávila, Bruno Moura, Rafael Bastos +6 more

This paper has as its primary objective to introduce the the HybriD-GM model conception, as well as extend the D-GM environment, providing efficient parallel executions for quantum computing simulations, targeted to hybrid architectures considering both CPU and GPU. By managing projection operators over quantum structures, and exploring coalescing memory access patterns, the HybriD-GM model enables the granularity control, optimizing hardware resources in distributed computations organized as tree data-structures. In the HybriD-GM evaluation, simulations of Shor’s and Grover’s algorithms achieve significant performance improvements in comparison to the D-GM previous version and to the LIQUi|⟩ and ProjectQ simulators.

Simpósio em Sistemas Computacionais de Alto Desempenho (SSCAD)•Oct 23, 2024•2 citations

High-Performance Quantum Computing Simulation for the Quantum Geometric Machine Model

A. Maron, Renata Reiser, Mauricio Lima Pilla

Due to the unavailability of quantum computers, simulation of quantum algorithms using classical computers is still the most affordable option for research of algorithms and models for quantum computing. As such simulation requires high computing power and memory resources, and computations are regular and data-intensive, GPUs become a suitable solution for accelerating the simulation of quantum algorithms. This work introduces an extension of the execution library for the VPE-qGM environment to support GPU acceleration. Hadamard gates with up to 20 quantum bits were simulated, and speedups of up to approximately 540× over a sequential simulation and of approximately 85× over a 8-core distributed simulation in the VirD-GM environment were achieved.

IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)•May 1, 2013•14 citations

Optimizing Quantum Simulation for Heterogeneous Computing: a Hadamard Transformation Study

Anderson Ávila, Murilo Schumalfuss, Renata Reiser +2 more

The D-GM execution environment improves distributed simulation of quantum algorithms in heterogeneous computing environments comprising both multi-core CPUs and GPUs. The main contribution of this work consists in the optimization of the environment VirD-GM, conceived in three steps: ( i ) the theoretical studies and implementation of the abstractions of the Mixed Partial Process defined in the qGM model, focusing on the reduction of the memory consumption regarding multidimensional QTs; ( ii ) the distributed/parallel implementation of such abstractions allowing its execution on clusters of GPUs; ( iii ) and optimizations that predict multiplications by zero-value of the quantum states/transformations, implying reduction in the number of computations. The results obtained in this work embrace the distribute/parallel simulation of Hadamard gates up to 21 qubits, showing scalability with the increase in the number of computing nodes.

Journal of Physics Conference Series•Oct 19, 2015•7 citations

Distributed Quantum Simulation on the VPE-qGM Environment

Adriano Maron, Anderson Boettge Pinheiro, Renata Reiser +1 more

This work describes a proposal for a platform focused on modeling and simulation (sequential or distributed) of quantum algorithms. More specifically, the integration between the environments VPE-qGM and VirD-GM is considered, providing a solution to the high computational cost associated to the simulation of quantum algorithms on classical computers. In this context, the algorithms are visually programmed and the details of distribution, communication and synchronization, required for the execution in distributed systems, are transparently managed by the execution environment. The validation and performance analysis consist in simulating a quantum search algorithm, obtaining results which demonstrate the performance gain of the platform compared to the sequential simulation.

Symposium on High Performance Computing Systems (WSCAD)•Oct 1, 2010•3 citations
This paper presents an extension of the VPE-qGM execution environment in order to support parallel execution using OpenMP. As a relevant part of a broader project represented by the simulation framework D-GM which aims to achieve quantum simulation on hybrid architectures (clusters, multicore CPUs and GPUs), this work enables the control and execution of the simulation from multicore architectures. This proposal is shown by providing relevant management simulation on multicore CPUs, which will be later combined with GPU targeted implementations. From the synergy between these implementations, this work aims to establish a general platform for hybrid simulation, exploring different architectures usually found in clusters and expanding the capabilities of the simulation environment VPE-qGM.
This paper presents the HybriD-GM model conception, from modeling to consolidation. The D-GM environment is also extended, providing efficient parallel executions for quantum computing simulations, targeted to hybrid architectures considering the CPU and GPU integration. By managing projection operators over quantum structures, and exploring coalescing memory access patterns, the HybriD-GM model enables granularity control, optimizing hardware resources in distributed computations organized as tree data structures. In the HybriD-GM evaluation, simulations of Shor's and Grover's algorithms achieve significant performance improvements in comparison to the previous D-GM version, and also with other related works, for example, LIQUi|⟩ and ProjectQ simulators.
Quantum computing proposes quantum algorithms exponentially faster than their clas- sical analogues when executed by a quantum computer. As quantum computers are currently unavailable for general use, one approach for analyzing the behavior and re- sults of such algorithms is the simulation using classical computers. As this simulation is inefficient due to the exponential growth of the temporal and spatial complexities, solutions for these two problems are essential in order to increase the simulation capa- bilities of any simulator. This work proposes the development of a methodology defined by two main steps: the first consists of the sequential implementation of the abstractions corresponding to the Quantum Processes and Quantum Partial Processes defined in the qGM model for reduction in memory consumption related to multidimensional quantum transformations; the second is the parallel implementation of such abstractions allowing its execution on GPUs. The results obtained by this work embrace the sequential simu- lation of controlled transformations up to 24 qubits. In the parallel simulation approach, Hadamard gates up to 20 qubits were simulated with a speedup of ? 50× over an 8-core parallel simulation, which is a significant performance improvement in the VPE-qGM environment when compared with its previous limitations.
This paper has as its primary objective to introduce the the HybriD-GM model conception, as well as extend the D-GM environment, providing efficient parallel executions for quantum computing simulations, targeted to hybrid architectures considering both CPU and GPU. By managing projection operators over quantum structures, and exploring coalescing memory access patterns, the HybriD-GM model enables the granularity control, optimizing hardware resources in distributed computations organized as tree data-structures. In the HybriD-GM evaluation, simulations of Shor’s and Grover’s algorithms achieve significant performance improvements in comparison to the D-GM previous version and to the LIQUi|⟩ and ProjectQ simulators.
Due to the unavailability of quantum computers, simulation of quantum algorithms using classical computers is still the most affordable option for research of algorithms and models for quantum computing. As such simulation requires high computing power and memory resources, and computations are regular and data-intensive, GPUs become a suitable solution for accelerating the simulation of quantum algorithms. This work introduces an extension of the execution library for the VPE-qGM environment to support GPU acceleration. Hadamard gates with up to 20 quantum bits were simulated, and speedups of up to approximately 540× over a sequential simulation and of approximately 85× over a 8-core distributed simulation in the VirD-GM environment were achieved.
The D-GM execution environment improves distributed simulation of quantum algorithms in heterogeneous computing environments comprising both multi-core CPUs and GPUs. The main contribution of this work consists in the optimization of the environment VirD-GM, conceived in three steps: ( i ) the theoretical studies and implementation of the abstractions of the Mixed Partial Process defined in the qGM model, focusing on the reduction of the memory consumption regarding multidimensional QTs; ( ii ) the distributed/parallel implementation of such abstractions allowing its execution on clusters of GPUs; ( iii ) and optimizations that predict multiplications by zero-value of the quantum states/transformations, implying reduction in the number of computations. The results obtained in this work embrace the distribute/parallel simulation of Hadamard gates up to 21 qubits, showing scalability with the increase in the number of computing nodes.
This work describes a proposal for a platform focused on modeling and simulation (sequential or distributed) of quantum algorithms. More specifically, the integration between the environments VPE-qGM and VirD-GM is considered, providing a solution to the high computational cost associated to the simulation of quantum algorithms on classical computers. In this context, the algorithms are visually programmed and the details of distribution, communication and synchronization, required for the execution in distributed systems, are transparently managed by the execution environment. The validation and performance analysis consist in simulating a quantum search algorithm, obtaining results which demonstrate the performance gain of the platform compared to the sequential simulation.