REVIEW 4 cited by
Benchmarking Quantum Computer Simulation Software Packages: State Vector Simulators
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Rapid advances in quantum computing technology lead to an increasing need for software simulators that enable both algorithm design and the validation of results obtained from quantum hardware. This includes calculations that aim at probing regimes of quantum advantage, where a quantum computer outperforms a classical computer in the same task. High performance computing (HPC) platforms play a crucial role as today's quantum devices already reach beyond the limits of what powerful workstations can model, but a systematic evaluation of the individual performance of the many offered simulation packages is lacking so far. In this Technical Review, we benchmark several software packages capable of simulating quantum dynamics with a special focus on HPC capabilities. We develop a containerized toolchain for benchmarking a large set of simulation packages on a local HPC cluster using different parallelisation capabilities, and compare the performance and system size-scaling for three paradigmatic quantum computing tasks. Our results can help finding the right package for a given simulation task and lay the foundation for a systematic community effort to benchmark and validate upcoming versions of existing and also newly developed simulation packages.
Forward citations
Cited by 4 Pith papers
-
VQCSim: When Does Compile-Once Statevector Simulation Beat Generic Quantum Frameworks?
Compile-once PyTorch-native statevector simulation with native autograd yields large median speedups for static VQC inference and training, with an open selector for when to use it.
-
Comparing performance of variational quantum algorithm simulations on HPC systems
A parser-based toolchain can port the same Hamiltonian and ansatz across seven quantum simulators, but variational algorithms on 15 to 20 qubits show limited parallel speedup.
-
Toolchain for Faster Iterations in Quantum Software Development
A q8s Jupyter kernel offloads quantum circuit simulation to remote GPU clusters, showing up to 10x faster execution for 29-qubit circuits than a local CPU laptop.
-
Project-Based Learning in Introductory Quantum Computing Courses: A Case Study on Quantum Algorithms for Medical Imaging
A first-person teaching case study reports that a project-based HHL-for-CT-imaging assignment helped the authors learn quantum computing, without measured learning outcomes, and confirms HHL is impractical for real CT today.
Discussion (0). Sign in to comment.