TY - JOUR

T1 - Parallel quantum computing simulations via quantum accelerator platform virtualization

AU - Claudino, Daniel

AU - Lyakh, Dmitry I.

AU - McCaskey, Alexander J.

N1 - Publisher Copyright:
© 2024 Elsevier B.V.

PY - 2024/11

Y1 - 2024/11

N2 - Quantum circuit execution is a 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 such quantum circuit executions that is based on introducing a large array of virtual quantum processing units (mapped to HPC nodes in our case) 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 computing/simulation 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 simulation leverages the cuQuantum library to run efficiently on GPU-accelerated HPC platforms.

AB - Quantum circuit execution is a 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 such quantum circuit executions that is based on introducing a large array of virtual quantum processing units (mapped to HPC nodes in our case) 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 computing/simulation 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 simulation leverages the cuQuantum library to run efficiently on GPU-accelerated HPC platforms.

KW - Distributed computing

KW - Quantum computing

KW - Quantum software

UR - http://www.scopus.com/inward/record.url?scp=85195687701&partnerID=8YFLogxK

U2 - 10.1016/j.future.2024.06.007

DO - 10.1016/j.future.2024.06.007

M3 - Article

AN - SCOPUS:85195687701

SN - 0167-739X

VL - 160

SP - 264

EP - 273

JO - Future Generation Computer Systems

JF - Future Generation Computer Systems

ER -