Abstract

 
 
 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.
 
 
Highlights
Quantum Computing (QC) is a computational paradigm, based on the Quantum Mechanics (QM ), that predicts the development of quantum algorithms
Performance analysis considering the optimizations for the sequential simulation considering the concepts of Quantum Process (QP) s and Quantum Partial Process (QP P) s
The main performance comparison was made against the previous version of the qGM-Analyzer, which supports the simulation of quantum algorithms using elementary processes (EPs), considering the optimizations described in [21]
Summary
Quantum Computing (QC) is a computational paradigm, based on the Quantum Mechanics (QM ), that predicts the development of quantum algorithms In many scenarios, these algorithms can be faster than their classical versions, as described in [1] and [2]. These algorithms can be faster than their classical versions, as described in [1] and [2] Such algorithms can only be efficiently executed by quantum computers, which are being developed and are still restricted in the number of qubits. A bottleneck generated by inter-node communication limits the performance of such simulators Such statements motivate the search for new solutions focused on the modeling, interpretation and simulation of quantum algorithms
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