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HybriD-GM: A Framework for Quantum Computing Simulation Targeted to Hybrid Parallel Architectures.

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This summary is machine-generated.

The HybriD-GM model enhances quantum computing simulations on hybrid architectures. It optimizes hardware resource usage for faster execution of algorithms like Shor's and Grover's.

Keywords:
Grover’s algorithmShor’s algorithmhybrid computingquantum computingquantum simulation

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Area of Science:

  • Quantum Computing
  • High-Performance Computing
  • Computer Architecture

Background:

  • Existing quantum computing simulators face challenges in efficiently utilizing hybrid CPU and GPU architectures.
  • Scalability and performance optimization are critical for complex quantum simulations.

Purpose of the Study:

  • To introduce the HybriD-GM model for advanced quantum computing simulations.
  • To enhance the D-GM environment for efficient parallel execution on hybrid systems.
  • To optimize hardware resource management in distributed quantum computations.

Main Methods:

  • Developed the HybriD-GM model, integrating CPU and GPU resources.
  • Extended the D-GM environment for parallel quantum computing simulations.
  • Implemented projection operator management and coalescing memory access patterns.
  • Organized distributed computations using tree data structures for granularity control.

Main Results:

  • Achieved significant performance improvements in Shor's and Grover's algorithm simulations using HybriD-GM.
  • Demonstrated superior performance compared to the previous D-GM version.
  • Showcased advantages over other quantum simulators like LIQUi|⟩ and ProjectQ.

Conclusions:

  • The HybriD-GM model offers substantial performance gains for quantum simulations on hybrid architectures.
  • Effective management of hardware resources and computational granularity is key to optimizing quantum computing performance.
  • HybriD-GM represents a significant advancement in simulating quantum algorithms efficiently.