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Qiskit: An open-source framework for quantum computing

3 Pith papers cite this work. Polarity classification is still indexing.

3 Pith papers citing it

years

2025 1 2023 2

verdicts

UNVERDICTED 3

representative citing papers

Benchmarking Digital-Analog Quantum Computation

quant-ph · 2023-07-14 · unverdicted · novelty 7.0

Except for a few specific cases, digital-analog quantum computation is disadvantageous compared to digital quantum computation based on scaling analysis across three quantum algorithms.

A low-circuit-depth quantum computing approach to the nuclear shell model

nucl-th · 2025-10-02 · unverdicted · novelty 6.0

A Slater-determinant-to-qubit mapping enables low-depth VQE circuits for nuclear shell model calculations on NISQ hardware, achieving less than 4% deviation from classical predictions after zero-noise extrapolation for nuclei including lithium isotopes and 210Pb.

Robust design under uncertainty in quantum error mitigation

quant-ph · 2023-07-11 · unverdicted · novelty 6.0

Presents unbiased uncertainty quantification for post-processing error mitigation and applies it to optimize hyperparameters in Zero Noise Extrapolation and Clifford Data Regression under finite-shot noise.

citing papers explorer

Showing 3 of 3 citing papers.

  • Benchmarking Digital-Analog Quantum Computation quant-ph · 2023-07-14 · unverdicted · none · ref 63

    Except for a few specific cases, digital-analog quantum computation is disadvantageous compared to digital quantum computation based on scaling analysis across three quantum algorithms.

  • A low-circuit-depth quantum computing approach to the nuclear shell model nucl-th · 2025-10-02 · unverdicted · none · ref 49

    A Slater-determinant-to-qubit mapping enables low-depth VQE circuits for nuclear shell model calculations on NISQ hardware, achieving less than 4% deviation from classical predictions after zero-noise extrapolation for nuclei including lithium isotopes and 210Pb.

  • Robust design under uncertainty in quantum error mitigation quant-ph · 2023-07-11 · unverdicted · none · ref 72

    Presents unbiased uncertainty quantification for post-processing error mitigation and applies it to optimize hyperparameters in Zero Noise Extrapolation and Clifford Data Regression under finite-shot noise.