clifford_qc: A Python Toolkit for Quantum Simulation
Abstract
clifford_qc is a Python research toolkit for constructing quantum models, comparing variational and subspace eigensolvers, and estimating measurement resources. A common sparse Pauli algebra connects Hamiltonians, density operators, gates, and candidate-selection observables; dedicated interfaces handle circuits, execution, and statistical estimates. A worked gradient example illustrates shared measurements, and a molecular workflow separates reusable preparation, solving, and reference validation. Matrix-element banks support virtual subspaces, exact Ritz residuals, and folded-spectrum roots. Process-isolated benchmarks show that packed coefficients and streaming reduce observed peak memory on the larger tested inputs at increased runtime. Measurement studies separate setting counts, shot requirements, and device cost: fully commuting readout certifies a fixed first-order Ritz functional with fewer shots, while anticommuting cliques help some fixed reference energies and not others. Entangling-gate time and connectivity can offset shot savings or make a protocol inadmissible under illustrative device models. Further studies expose subspace bias, indeterminate encoding comparisons, failed approximate-restriction screens, and high residual-measurement costs under the tested uniform allocations. Generated tables, provenance records, a versioned evidence contract, and manuscript checks connect claims to their inputs. These small-system results support reproducible method development and establish neither hardware performance nor a general simulation speedup.
Related
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BibTeX
@misc{utama2026cliffordqc,
title = {{clifford_qc: A Python Toolkit for Quantum Simulation}},
author = {Ginanjar Utama and Hermawan Kresno Dipojono},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2609.23059},
eprint = {2609.23059},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.23059},
}
RIS
TY - UNPB
TI - clifford_qc: A Python Toolkit for Quantum Simulation
AU - Ginanjar Utama
AU - Hermawan Kresno Dipojono
PY - 2026
DA - 2026/10/18
PB - arXiv
DO - 10.48550/arxiv.2609.23059
UR - https://arxiv.org/abs/2609.23059
AB - clifford_qc is a Python research toolkit for constructing quantum models, comparing variational and subspace eigensolvers, and estimating measurement resources. A common sparse Pauli algebra connects Hamiltonians, density operators, gates, and candidate-selection observables; dedicated interfaces handle circuits, execution, and statistical estimates. A worked gradient example illustrates shared measurements, and a molecular workflow separates reusable preparation, solving, and reference validation. Matrix-element banks support virtual subspaces, exact Ritz residuals, and folded-spectrum roots. Process-isolated benchmarks show that packed coefficients and streaming reduce observed peak memory on the larger tested inputs at increased runtime. Measurement studies separate setting counts, shot requirements, and device cost: fully commuting readout certifies a fixed first-order Ritz functional with fewer shots, while anticommuting cliques help some fixed reference energies and not others. Entangling-gate time and connectivity can offset shot savings or make a protocol inadmissible under illustrative device models. Further studies expose subspace bias, indeterminate encoding comparisons, failed approximate-restriction screens, and high residual-measurement costs under the tested uniform allocations. Generated tables, provenance records, a versioned evidence contract, and manuscript checks connect claims to their inputs. These small-system results support reproducible method development and establish neither hardware performance nor a general simulation speedup.
ER -