Quantum processes: A new interpretation for quantum transformations in the VPE-qGM environment

Maron, Adriano; Reiser, Renata; Pilla, Maurı́cio L.; Yamin, Adenauer

doi:10.1109/clei.2012.6426919

Cited by 10 publications

(7 citation statements)

References 7 publications

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“…It is worth remembering that this computation is analogous to the Kronecker Product, however our execution library does not store an exponentially larger intermediary values. A detailed view of this solution was previously published in [8], and it is extended in this work. For each pt, corresponding to a partial complex value generated from the vectors in LV, there is a inner iteration with the computation described in Eq.…”

Section: Figure 4: Selecting the Component Vectors For Each Threadmentioning

confidence: 96%

“…(8). By using the positions each complex value occupy in its corresponding LV it is possible to dynamically generate the amplitude to be read from CS for each sub step in Eq.…”

Section: Figure 4: Selecting the Component Vectors For Each Threadmentioning

confidence: 99%

“…For the actual computation, all the data related to the Matrices List (ML) and Last Matrix (LM ) is stored in a single onedimensional vector. Also, every dot represents a complex value, obtained from a pre-processing phase of the library better discussed in [8].…”

Section: Gpu-aware Simulation In the Dg-mmentioning

confidence: 99%

See 2 more Smart Citations

GPU-aware distributed quantum simulation

Ávila

Maron

Reiser

et al. 2014

Proceedings of the 29th Annual ACM Symposium on Applied Computing

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The simulation of algorithms from quantum computing is currently the most affordable solution for development of new applications. Due to the high computational cost of such simulation, solutions towards novel features that increase the performance are always desired. This work proposes an extension for the D-GM's simulation framework, establishing the support for GPU-aware distributed quantum simulation. The project explores the concepts of heterogeneous computing, merging distributed and GPU computing in a single programming environment. Our results comprehend the distributed simulation of systems comprised by Hadamard transformations up to 21 qubits. Detailed analysis and a performance comparison between PyCUDA and JCUDA frameworks for our application are discussed. This work is a significant step towards the ultimate goal of our project, which is the hybrid simulation of quantum algorithms, i.e., exploring multi-core CPUs and GPUs distributed along a cluster, achieving scalability when larger systems are simulated.

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Section: Figure 4: Selecting the Component Vectors For Each Threadmentioning

confidence: 96%

“…(8). By using the positions each complex value occupy in its corresponding LV it is possible to dynamically generate the amplitude to be read from CS for each sub step in Eq.…”

Section: Figure 4: Selecting the Component Vectors For Each Threadmentioning

confidence: 99%

See 1 more Smart Citation

GPU-aware distributed quantum simulation

Ávila

Maron

Reiser

et al. 2014

Proceedings of the 29th Annual ACM Symposium on Applied Computing

Self Cite

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show abstract

“…The VPE-qGM (Visual Programming Environment for the Quantum Geometric Machine Model ), previously described in [10] and [11], is a quantum simulator which is being developed considering both characterizations, visual modeling and distributed simulation of quantum algorithms. VPE-qGM presents the application and evolution of the simulation through integrated graphical interfaces.…”

Section: Introductionmentioning

confidence: 99%

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

Maron

Reiser

Pilla

et al. 2013

CLEIej

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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.

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“…In previous works [3,5,6], we defined processes related to n-dimensional QTs through lower order basic matrices to reduce the memory volume used on simulations due to the exponential growth of their representation using a unique matrix (2 n × 2 n ). QT elements required for the computation of a QA are generated during execution time through iterations on those elementary processes, simulating the tensor product behavior on quantum processes and states.…”

Section: Reducing Simulation Complexitymentioning

confidence: 99%

Reduction and Decomposition Optimizations in Quantum Computing Simulation Applied to the Shor’s Algorithm

Ávila¹,

Reiser²,

Pilla³

2017

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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Abstract. Due to the expansion of transformations and read/write memory states by tensor products in multidimensional quantum applications, the exponential increase in temporal and spatial complexities constitutes one of the main challenges for quantum computing simulations. Simulation of these systems is important in order to develop and test new quantum algorithms. This work presents reduction and decomposition optimizations for the Distributed Geometric Machine environment. By exploring properties as the sparsity of the Identity operator and partiality of dense unitary transformations, better storage and distribution of quantum information are achieved. The main improvements are implemented by decreasing replication and void elements inherited from quantum operators. In the evaluation of this proposal, Shor's algorithm considering 2n+3 qubits in the order-finding quantum algorithm was simulated up to 25 qubits over CPU, sequentially and in parallel, and over GPU. Results confirm that temporal complexity is reduced. When comparing our implementations running on the same hardware with LIQUi| , academic release version, our new simulator was faster and allowed for the simulation of more qubits.

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Quantum processes: A new interpretation for quantum transformations in the VPE-qGM environment

Cited by 10 publications

References 7 publications

GPU-aware distributed quantum simulation

GPU-aware distributed quantum simulation

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

Reduction and Decomposition Optimizations in Quantum Computing Simulation Applied to the Shor’s Algorithm

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