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Quantum computing proposes quantum algorithms exponentially faster than their classical analogues when executed by a quantum computer. As quantum computers are currently unavailable for general use, one approach for analyzing the behavior and results of such algorithms is the simulation using classical computers. As this simulation is inecient due to the exponential growth of the temporal and spatial complexities, solutions for these two problems are essential in order to increase the simulation capabilities of any simulator. This work proposes the development of a methodology dened by two main steps: the rst consists of the sequential implementation of the abstractions corresponding to the Quantum Processes and Quantum Partial Processes dened 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 simulation 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 signicant performance improvement in the VPE-qGM environment when compared with its previous limitations.

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