Related Experiment Video
Updated: May 19, 2026
05:39
Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform
Published on: August 2, 2019
Parameterized quantum random number generators for superconducting quantum hardware: novel architectures and NIST SP
Ramin Salehi1, Asad W Malik2, Mark A Novotny3
1Department of Electrical and Computer Engineering, Kansas State University, 1701B Platt St, Manhattan, 66506, KS, USA. raminsalehi@ksu.edu.
Scientific Reports
|May 17, 2026
Summary
Parameterized quantum random number generators (PQRNGs) enhance randomness quality on noisy quantum devices. Specific PQRNG architectures show improved robustness against errors and noise, outperforming others in statistical tests.
Area of Science:
- Quantum Information Science
- Quantum Computing
- Cryptography
Background:
- Quantum Random Number Generators (QRNGs) offer superior randomness compared to classical methods.
- Noisy Intermediate-Scale Quantum (NISQ) platforms face challenges like decoherence and errors, impacting QRNG statistical randomness.
- Systematic characterization of noise impact on QRNGs is needed.
Purpose of the Study:
- Introduce Parameterized Quantum Random Number Generator (PQRNG) architectures using Parameterized Quantum Circuits (PQCs).
- Enhance tunability and expressive power of QRNGs under realistic noise conditions.
- Evaluate the statistical randomness of different PQRNG architectures on NISQ devices.
Main Methods:
- Investigated three PQRNG architectures: PQC-H-CH, H-PQC-CH, and H-CH-PQC.
- Applied Transpiler optimization and circuit-level error mitigation as preprocessing steps.
- Validated randomness using the NIST SP 800-22 statistical test suite.
Main Results:
- PQC-H-CH achieved the highest number of fully passing preprocessing configurations (126) under default NIST settings.
- H-PQC-CH and H-CH-PQC showed fewer passing configurations (110 and 69, respectively).
- Preprocessing choices significantly impacted randomness quality, more than NIST test parameter adjustments.
Conclusions:
- The proposed PQRNG approach using PQCs offers a robust foundation for reliable QRNGs on NISQ platforms.
- Demonstrated improved tunability and noise resilience in quantum random number generation.
- Highlighted the critical role of preprocessing techniques in optimizing QRNG performance.