Parallel Quantum Computing Simulations via Quantum Accelerator Platform Virtualization

TL;DR

This paper presents a parallel quantum circuit simulation model using a virtual quantum processing unit array on classical HPC nodes, enabling scalable execution across various backends and demonstrating strong performance in quantum chemistry and machine learning tasks.

Contribution

The paper introduces a scalable parallel simulation framework for quantum circuits using virtual QPUs within the XACC platform, compatible with diverse hardware backends.

Findings

Achieved strong scaling in quantum gradient computations.

Demonstrated efficient GPU-accelerated simulation of quantum circuits.

Applicable to variational quantum algorithms and quantum machine learning.

Abstract

Quantum circuit execution is the central task in quantum computation. Due to inherent quantum-mechanical constraints, quantum computing workflows often involve a considerable number of independent measurements over a large set of slightly different quantum circuits. Here we discuss a simple model for parallelizing simulation of such quantum circuit executions that is based on introducing a large array of virtual quantum processing units, mapped to classical HPC nodes, as a parallel quantum computing platform. Implemented within the XACC framework, the model can readily take advantage of its backend-agnostic features, enabling parallel quantum circuit execution over any target backend supported by XACC. We illustrate the performance of this approach by demonstrating strong scaling in two pertinent domain science problems, namely in computing the gradients for the multi-contracted variational quantum eigensolver and in data-driven quantum circuit learning, where we vary the number of qubits and the number of circuit layers. The latter (classical) simulation leverages the cuQuantum SDK library to run efficiently on GPU-accelerated HPC platforms.