Simulation of Quantum Computing on Classical Supercomputers

TL;DR

This paper introduces a graph-cutting scheme for tensor network quantum circuit simulation on supercomputers, enabling larger scale simulations than previously possible, verified with QAOA algorithm tests.

Contribution

It proposes a novel graph-cutting method based on undirected graph edges to improve distributed quantum circuit simulation on supercomputers.

Findings

Simulates 120-qubit QAOA on 4096-core supercomputer.

Outperforms single-core 100-qubit simulation.

Uses heuristic algorithms for edge cutting optimization.

Abstract

Simulation of quantum computing on supercomputers is a significant research topic, which plays a vital role in quantum algorithm verification, error-tolerant verification and other applications. Tensor network contraction based on density matrix is an important single-amplitude simulation strategy, but it is hard to execute on the distributed computing systems currently. In this paper, we dive deep into this problem, and propose a scheme based on cutting edges of undirected graphs. This scheme cuts edges of undirected graphs with large tree width to obtain many undirected subgraphs with small tree width, and these subgraphs contracted on different computing cores. The contraction results of slave cores are summarized in the master node, which is consistent with the original tensor network contraction. Thus, we can simulate the larger scale quantum circuit than single core. Moreover, it's an NP-hard problem to find the global optimum cutting edges, and we propose a search strategy based on a heuristic algorithm to approach it. In order to verify the effectiveness of our scheme, we conduct tests based on QAOA algorithm, and it can simulate 120-qubit 3-regular QAOA algorithm on 4096-core supercomputer, which greatly exceeds the simulation scale on a single core of 100-qubit.