Queen: A quick, scalable, and comprehensive quantum circuit simulation for supercomputing

• URL: http://arxiv.org/abs/2406.14084v1
• Date: Thu, 20 Jun 2024 08:00:41 GMT
• Title: Queen: A quick, scalable, and comprehensive quantum circuit simulation for supercomputing
• Authors: Chuan-Chi Wang, Yu-Cheng Lin, Yan-Jie Wang, Chia-Heng Tu, Shih-Hao Hung,
• Abstract summary: We present an innovative quantum circuit simulation toolkit comprising gate optimization and simulation modules. We achieve averaging 9 times speedup compared to state-of-the-art simulators, including QuEST, IBM-Aer, and NVIDIA-cuQuantum. We believe the proposed toolkit paves the way for faster quantum circuit simulations, thereby facilitating the development of novel quantum algorithms.
• Score: 2.821829060100186
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The state vector-based simulation offers a convenient approach to developing and validating quantum algorithms with noise-free results. However, limited by the absence of cache-aware implementations and unpolished circuit optimizations, the past simulators were severely constrained in performance, leading to stagnation in quantum computing. In this paper, we present an innovative quantum circuit simulation toolkit comprising gate optimization and simulation modules to address these performance challenges. For the performance, scalability, and comprehensive evaluation, we conduct a series of particular circuit benchmarks and strong scaling tests on a DGX-A100 workstation and achieve averaging 9 times speedup compared to state-of-the-art simulators, including QuEST, IBM-Aer, and NVIDIA-cuQuantum. Moreover, the critical performance metric FLOPS increases by up to a factor of 8-fold, and arithmetic intensity experiences a remarkable 96x enhancement. We believe the proposed toolkit paves the way for faster quantum circuit simulations, thereby facilitating the development of novel quantum algorithms.

Related papers

• QOPS: A Compiler Framework for Quantum Circuit Simulation Acceleration with Profile Guided Optimizations [0.38836072943850625]
  This work proposes a quantum compiler framework QOPS to enable profile-guided optimization (PGO) for quantum circuit simulation acceleration. The simulator-specific PGO can be applied to the benchmarks to accelerate the simulation speed by a factor of 1.19. As for the hardware-independent PGO, compared with the brute force mechanism, which achieves 21% performance improvement against the non-optimized version, the PGO can achieve 16% speedup with a factor of 63 less compilation time than the brute force approach.
  arXiv  Detail & Related papers  (2024-10-12T01:54:09Z)
• DiaQ: Efficient State-Vector Quantum Simulation [2.2854510798816525]
  We present a novel observation regarding the prevalent sparsity patterns inherent in quantum circuits. We introduce DiaQ, a new sparse matrix format tailored to exploit this quantum-specific sparsity, thereby enhancing simulation performance.
  arXiv  Detail & Related papers  (2024-04-30T23:15:55Z)
• TANQ-Sim: Tensorcore Accelerated Noisy Quantum System Simulation via QIR on Perlmutter HPC [16.27167995786167]
  TANQ-Sim is a full-scale density matrix based simulator designed to simulate practical deep circuits with both coherent and non-coherent noise. To address the significant computational cost associated with such simulations, we propose a new density-matrix simulation approach. To optimize performance, we also propose specific gate fusion techniques for density matrix simulation.
  arXiv  Detail & Related papers  (2024-04-19T21:16:29Z)
• Quantum Tunneling: From Theory to Error-Mitigated Quantum Simulation [49.1574468325115]
  This study presents the theoretical background and the hardware aware circuit implementation of a quantum tunneling simulation. We use error mitigation techniques (ZNE and REM) and multiprogramming of the quantum chip for solving the hardware under-utilization problem.
  arXiv  Detail & Related papers  (2024-04-10T14:27:07Z)
• State of practice: evaluating GPU performance of state vector and tensor network methods [2.7930955543692817]
  This article investigates the limits of current state-of-the-art simulation techniques on a test bench made of eight widely used quantum subroutines. We highlight how to select the best simulation strategy, obtaining a speedup of up to an order of magnitude.
  arXiv  Detail & Related papers  (2024-01-11T09:22:21Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• A Reorder Trick for Decision Diagram Based Quantum Circuit Simulation [0.4358626952482686]
  We study two classes of quantum circuits on which the state-of-the-art decision diagram based simulators failed to perform well in terms of simulation time. We propose a simple and powerful reorder trick to boost the simulation of such quantum circuits.
  arXiv  Detail & Related papers  (2022-11-14T04:55:25Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale [57.84751206630535]
  We present a modernized version of the Quantum Virtual Machine (TNQVM) which serves as a quantum circuit simulation backend in the e-scale ACCelerator (XACC) framework. The new version is based on the general purpose, scalable network processing library, ExaTN, and provides multiple quantum circuit simulators. By combining the portable XACC quantum processors and the scalable ExaTN backend we introduce an end-to-end virtual development environment which can scale from laptops to future exascale platforms.
  arXiv  Detail & Related papers  (2021-04-21T13:26:42Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.