Performance Evaluation and Acceleration of the QTensor Quantum Circuit Simulator on GPUs

Author(s):  
Danylo Lykov ◽  
Angela Chen ◽  
Huaxuan Chen ◽  
Kristopher Keipert ◽  
Zheng Zhang ◽  
...  
2015 ◽  
Vol 67 (1) ◽  
pp. 168-173
Author(s):  
Stancu Mihai Dorian ◽  
Popa Emil Marin

Abstract In this paper we propose the design and implementation of a quantum circuit simulator API. Currently the API allows users to implement, debug and test the following two quantum algorithms: Bernstein-Vazirani’s algorithm and Simon’s Algorithm. The goal is to create a framework that will allow quantum computer scientists to easily develop new quantum algorithms.


Quantum ◽  
2021 ◽  
Vol 5 ◽  
pp. 559
Author(s):  
Yasunari Suzuki ◽  
Yoshiaki Kawase ◽  
Yuya Masumura ◽  
Yuria Hiraga ◽  
Masahiro Nakadai ◽  
...  

To explore the possibilities of a near-term intermediate-scale quantum algorithm and long-term fault-tolerant quantum computing, a fast and versatile quantum circuit simulator is needed. Here, we introduce Qulacs, a fast simulator for quantum circuits intended for research purpose. We show the main concepts of Qulacs, explain how to use its features via examples, describe numerical techniques to speed-up simulation, and demonstrate its performance with numerical benchmarks.


2019 ◽  
Vol 123 (19) ◽  
Author(s):  
Chu Guo ◽  
Yong Liu ◽  
Min Xiong ◽  
Shichuan Xue ◽  
Xiang Fu ◽  
...  

Author(s):  
S Maity ◽  
A Pal ◽  
T Roy ◽  
S B Mandal ◽  
A Chakrabarti

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Benjamin Villalonga ◽  
Sergio Boixo ◽  
Bron Nelson ◽  
Christopher Henze ◽  
Eleanor Rieffel ◽  
...  

Abstract Here we present qFlex, a flexible tensor network-based quantum circuit simulator. qFlex can compute both the exact amplitudes, essential for the verification of the quantum hardware, as well as low-fidelity amplitudes, to mimic sampling from Noisy Intermediate-Scale Quantum (NISQ) devices. In this work, we focus on random quantum circuits (RQCs) in the range of sizes expected for supremacy experiments. Fidelity f simulations are performed at a cost that is 1/f lower than perfect fidelity ones. We also present a technique to eliminate the overhead introduced by rejection sampling in most tensor network approaches. We benchmark the simulation of square lattices and Google’s Bristlecone QPU. Our analysis is supported by extensive simulations on NASA HPC clusters Pleiades and Electra. For our most computationally demanding simulation, the two clusters combined reached a peak of 20 Peta Floating Point Operations per Second (PFLOPS) (single precision), i.e., 64% of their maximum achievable performance, which represents the largest numerical computation in terms of sustained FLOPs and the number of nodes utilized ever run on NASA HPC clusters. Finally, we introduce a novel multithreaded, cache-efficient tensor index permutation algorithm of general application.


Author(s):  
Carl Malings ◽  
Rebecca Tanzer ◽  
Aliaksei Hauryliuk ◽  
Provat K. Saha ◽  
Allen L. Robinson ◽  
...  

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