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

TL;DR

This paper optimizes and evaluates the QTensor quantum circuit simulator on GPUs, achieving significant speedups for simulating quantum algorithms like QAOA on large-scale GPU supercomputers.

Contribution

It introduces GPU-optimized implementations and a dynamic mixed backend for the QTensor simulator, enabling efficient large-scale quantum circuit simulations.

Findings

Achieved 176x speedup on GPU over CPU baseline.

Demonstrated efficient simulation of QAOA circuits for MaxCut.

Optimized tensor allocation between CPU and GPU for performance.

Abstract

This work studies the porting and optimization of the tensor network simulator QTensor on GPUs, with the ultimate goal of simulating quantum circuits efficiently at scale on large GPU supercomputers. We implement NumPy, PyTorch, and CuPy backends and benchmark the codes to find the optimal allocation of tensor simulations to either a CPU or a GPU. We also present a dynamic mixed backend to achieve optimal performance. To demonstrate the performance, we simulate QAOA circuits for computing the MaxCut energy expectation. Our method achieves $176\times$ speedup on a GPU over the NumPy baseline on a CPU for the benchmarked QAOA circuits to solve MaxCut problem on a 3-regular graph of size 30 with depth $p=4$.