Parallel quantum computer simulation on the GPU

Eladio Gutiérrez, Sergio Romero, Marı́a A. Trenas +3 more

Due to the increasing computing power of the graphics processing units (GPU), they are becoming more and more popular when solving general purpose algorithms. As the simulation of quantum computers results on a problem with exponential complexity, it is advisable to perform a parallel computation, such as the one provided by the SIMD multiprocessors present in recent GPUs. In this paper, we focus on an important quantum algorithm, the quantum Fourier transform (QTF), in order to evaluate different parallelization strategies on a novel GPU architecture. Our implementation makes use of the new CUDA software/hardware architecture developed recently by NVIDIA.

Eladio Gutiérrez, Sergio Romero, Marı́a A. Trenas +1 more

Sengthai Heng, Tae-Kyung Kim, Youngsun Han

As the advent of quantum computers comes closer, research on quantum algorithms is actively being conducted in various fields. Quantum computer simulation is mainly employed for the study of quantum algorithms due to the imperfections of quantum computers. Quantum computer simulation using classical computers has encountered significant challenges in emulating quantum algorithms, where computational time and memory usage increase exponentially due to superposition and entanglement. To resolve these problems, there have been many studies to exploit GPU-based parallelism in quantum computer simulation. In this review paper, we present various studies on implementing quantum computer simulations using single or multiple GPUs. We also analyze the performance of these studies in terms of speedup and summarize the simulation methods.

Koen De Raedt, Kristel F. L. Michielsen, Hans A. De Raedt +6 more

Pei Zhang, Jiabin Yuan, Xiangwen Lu

Andrei Amariutei, Simona Caraiman

Simulation of quantum computers using classical computers is a hard problem with high memory and computational requirements. Parallelization can alleviate this problem, allowing the simulation of more qubits at the same time or the same number of qubits to be simulated in less time. A promising approach is to exploit the high performance computing capabilities provided by the latest graphical processing units. In this paper we present a parallel implementation of the QC-lib quantum computer simulator on the GPU using the CUDA programming model. The proposed scheme for partitioning the terms that describe the state of a quantum register takes advantage of the specific characteristics of the CUDA memory spaces and allows for an efficient parallelization of the general singe qubit operator. Experimental results indicate that very good speed-ups can be obtained in contrast with the sequential implementation.