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Overcoming Memory Constraints in Quantum Circuit Simulation with a High-Fidelity Compression Framework

Boyuan Zhang, Bo Fang, Fanjiang Ye, Yida Gu, Nathan Tallent, Guangming Tan, Dingwen Tao

TL;DR

BMQSim is introduced, a novel state vector quantum simulation framework that employs lossy compression to address the memory constraints on graphics processing unit (GPU) machines and incorporates the first GPU-based lossy compression technique with point-wise error control.

Abstract

Full-state quantum circuit simulation requires exponentially increased memory size to store the state vector as the number of qubits scales, presenting significant limitations in classical computing systems. Our paper introduces BMQSim, a novel state vector quantum simulation framework that employs lossy compression to address the memory constraints on graphics processing unit (GPU) machines. BMQSim effectively tackles four major challenges for state-vector simulation with compression: frequent compression/decompression, high memory movement overhead, lack of dedicated error control, and unpredictable memory space requirements. Our work proposes an innovative strategy of circuit partitioning to significantly reduce the frequency of compression occurrences. We introduce a pipeline that seamlessly integrates compression with data movement while concealing its overhead. Additionally, BMQSim incorporates the first GPU-based lossy compression technique with point-wise error control. Furthermore, BMQSim features a two-level memory management system, ensuring efficient and stable execution. Our evaluations demonstrate that BMQSim can simulate the same circuit with over 10 times less memory usage on average, achieving fidelity over 0.99 and maintaining comparable simulation time to other state-of-the-art simulators.

Overcoming Memory Constraints in Quantum Circuit Simulation with a High-Fidelity Compression Framework

TL;DR

BMQSim is introduced, a novel state vector quantum simulation framework that employs lossy compression to address the memory constraints on graphics processing unit (GPU) machines and incorporates the first GPU-based lossy compression technique with point-wise error control.

Abstract

Full-state quantum circuit simulation requires exponentially increased memory size to store the state vector as the number of qubits scales, presenting significant limitations in classical computing systems. Our paper introduces BMQSim, a novel state vector quantum simulation framework that employs lossy compression to address the memory constraints on graphics processing unit (GPU) machines. BMQSim effectively tackles four major challenges for state-vector simulation with compression: frequent compression/decompression, high memory movement overhead, lack of dedicated error control, and unpredictable memory space requirements. Our work proposes an innovative strategy of circuit partitioning to significantly reduce the frequency of compression occurrences. We introduce a pipeline that seamlessly integrates compression with data movement while concealing its overhead. Additionally, BMQSim incorporates the first GPU-based lossy compression technique with point-wise error control. Furthermore, BMQSim features a two-level memory management system, ensuring efficient and stable execution. Our evaluations demonstrate that BMQSim can simulate the same circuit with over 10 times less memory usage on average, achieving fidelity over 0.99 and maintaining comparable simulation time to other state-of-the-art simulators.

Paper Structure

This paper contains 21 sections, 7 equations, 15 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Background
  3. Principles of State-Vector Simulation
  4. Floating-Point Lossy Compression
  5. CUDA Memory Architecture
  6. Feasibility Analysis
  7. Design of BMQSim
  8. Optimal-Compression Circuit Partition
  9. Transfer-Concealed Workflow
  10. Point-wise Error Control for GPU Compression
  11. Two-Level Memory Management
  12. Experimental Evaluation
  13. Experimental Setup
  14. Evaluation of Supported Qubit Number
  15. Comparison with SC19-Sim
  16. ...and 6 more sections

Figures (15)

  • Figure 1: A demonstration of state vector partitioning. We refer to the higher $c$ bits as the global index and the lower $b$ bits as the local index.
  • Figure 2: A demonstration of how the target qubit location influences amplitude updates. The same alphabet denotes the same value (0 or 1).
  • Figure 3: An overview of our proposed BMQSim.
  • Figure 4: An example of how SV blocks are involved based on target global index changes. The same alphabet denotes the same value (0 or 1).
  • Figure 5: An example of the proposed circuit partition process.
  • ...and 10 more figures