Harnessing CUDA-Q's MPS for Tensor Network Simulations of Large-Scale Quantum Circuits

TL;DR

This paper evaluates CUDA-Q's MPS tensor network simulator on Nvidia hardware, demonstrating its potential for large-scale quantum circuit simulation and analyzing GPU utilization challenges.

Contribution

It provides a performance comparison between CUDA-Q's state vector and MPS tensor network methods on GPU hardware, highlighting the strengths and limitations of MPS simulations.

Findings

Tensor network methods enable larger quantum circuit simulations.

GPU utilization for MPS simulations is currently suboptimal.

MPS provides an approximate but scalable simulation approach.

Abstract

Quantum computer simulators are an indispensable tool for prototyping quantum algorithms and verifying the functioning of existing quantum computer hardware. The current largest quantum computers feature more than one thousand qubits, challenging their classical simulators. State-vector quantum simulators are challenged by the exponential increase of representable quantum states with respect to the number of qubits, making more than fifty qubits practically unfeasible. A more appealing approach for simulating quantum computers is adopting the tensor network approach, whose memory requirements fundamentally depend on the level of entanglement in the quantum circuit, and allows simulating the current largest quantum computers. This work investigates and evaluates the CUDA-Q tensor network simulators on an Nvidia Grace Hopper system, particularly the Matrix Product State (MPS) formulation. We compare the performance of the CUDA-Q state vector implementation and validate the correctness of MPS simulations. Our results highlight that tensor network-based methods provide a significant opportunity to simulate large-qubit circuits, albeit approximately. We also show that current GPU-accelerated computation cannot fully utilize GPU efficiently in the case of MPS simulations.