Quantum-Classical-Quantum Workflow in Quantum-HPC Middleware with GPU Acceleration

Published in 2024 International Conference on Quantum Communications, Networking, and Computing (QCNC), 2024

This paper presents a Quantum-Classical-Quantum (QCQ) middleware framework that tightly couples quantum and high-performance classical computing. Key components include:

  • Variational Quantum Eigensolver (VQE) algorithms executed on QPUs for quantum state preparation.
  • Tensor Network classifiers and Quantum Convolutional Neural Networks (QCNNs) running on GPU-accelerated classical systems for accurate state identification.
  • Integration of NVIDIA cuQuantum SDK and PennyLane’s Lightning plugin, achieving up to 10× computational speedup over CPU-based methods.

The QCQ architecture demonstrates 99.5% accuracy in classifying quantum phase transitions in Ising and XXZ models, offering a powerful hybrid paradigm for simulating and understanding complex quantum phenomena at scale.

Figure: QCQ workflow integrating VQE, CNNs, and quantum layers for phase transition classification.

Recommended citation: Chen, Kuan-Cheng; Li, Xiaoren; Xu, Xiaotian; Wang, Yun-Yuan; & Liu, Chen-Yu. (2024). "Quantum-Classical-Quantum Workflow in Quantum-HPC Middleware with GPU Acceleration." Proceedings of the 2024 International Conference on Quantum Communications, Networking, and Computing (QCNC), 304–311.
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