We identify four implementation error classes in NIST SP 800-22 statistical tests: (1) run-counting substituted for Berlekamp-Massey in Test 10, (2) p-value boosting in Test 12, (3) chi-squared deflation in Test 14, (4) unconditional health test passes. Corrected implementations produce genuine 15/15 PASS on PHM quantum entropy. Hardware validation reveals that IBM superconducting qubit readout asymmetry (0.39%) causes NIST failures at 1M bits, requiring readout error mitigation for certification. IBM job: d8e3t73o3njc73eu7880. Note: Also available on IACR ePrint https://eprint.iacr.org/2026/109748
We identify four implementation error classes in NIST SP 800-22 statistical tests: (1) run-counting substituted for Berlekamp-Massey in Test 10, (2) p-value boosting in Test 12, (3) chi-squared deflation in Test 14, (4) unconditional health test passes. Corrected implementations produce genuine 15/15 PASS on PHM quantum entropy. Hardware validation reveals that IBM superconducting qubit readout asymmetry (0.39%) causes NIST failures at 1M bits, requiring readout error mitigation for certification. IBM job: d8e3t73o3njc73eu7880. Note: Also available on IACR ePrint https://eprint.iacr.org/2026/109748
Quantum Random Number Generators (QRNGs) exploit intrinsic quantum mechanical randomness and are promising candidates for higher-quality randomness than classical RNGs. However, QRNGs on Noisy Intermediate-Scale Quantum (NISQ) platforms are constrained by decoherence, gate and measurement errors, and limited circuit depth, and the resulting impact on statistical randomness is not yet systematically characterized. This work introduces parameterized quantum random number generator (PQRNG) architectures built from parameterized quantum circuits (PQCs) to improve tunability and expressive power under realistic noise. We study three architectures, PQC-H-CH, H-PQC-CH, and H-CH-PQC, and evaluate their randomness under Transpiler optimization and circuit-level error mitigation as preprocessing steps, using NIST SP 800-22 for statistical validation. Utilizing default NIST settings, PQC-H-CH attains the largest number of fully passing preprocessing configurations (126 combinations passing all 15 tests), compared with 110 for H-PQC-CH and 69 for H-CH-PQC, indicating strong robustness across Transpiler optimization and mitigation settings, and we find that preprocessing choices influence randomness quality more strongly than adjusting NIST test parameters. Overall, these results demonstrate that the proposed approach provides a solid foundation for developing more reliable and practical QRNGs using PQCs in NISQ devices.
Random number generators (RNGs) are foundational to modern cryptographic systems.While quantum random number generators (QRNGs) leverage inherently stochastic quantum measurements, practical deployments still exhibit implementation artifacts (e.g., detector dead time, afterpulsing, drift) that can introduce bias, correlation, and non-stationarity.This paper presents a unified validation framework for both CMOS true random number generators (TRNGs) and QRNGs under NIST SP 800-90B entropy source validation.Rather than simulating quantum physics, we model observable raw-output behaviors of practical implementations and evaluate them using a consistent pipeline: raw capture constraints, IID screening, conservative min-entropy bounding, and online health testing (repetition count test and adaptive proportion test).Using synthetic sources that emulate typical CMOS and QRNG failure modes, we demonstrate how similar artifacts drive the IID versus non-IID decision and tighten entropy bounds, motivating a vendor-agnostic, reproducible validation methodology.
The rapid development of digital communications, cloud computing, the Internet of Things (IoT), and integrated data systems has raised concerns about securely and reliably protecting sensitive information transmitted over public and private networks.The security of cryptographic schemes such as Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC) relies mainly on the inherent hardness of the underlying mathematical problem.Yet the ABSTRACT: This paper evaluates a simulated quantum security pipeline integrating a Quantum Random Number Generator (QRNG), BB84 Quantum Key Distribution (QKD), and Quantum One-Time Pad (QOTP) bitwise XOR encryption.Developed using IBM Qiskit v0.45 within a controlled environment using a seed value of 12345, the proposed framework addresses the lack of multi-stage randomness testing across quantum cryptographic workflows.The execution follows a four-stage sequential workflow starting with QRNG bit generation and entropy verification, which provides the groundwork and measurement bases for BB84.Next, BB84 QKD handles qubit transmission, basis sifting, paritycheck error correction, and Toeplitz matrix hashing for privacy amplification to extract the secret key.The derived key is then used for bitwise QOTP encryption and decryption.Finally, the pipeline undergoes multi-stage statistical validation at the raw QRNG, privacy-amplified key, and ciphertext levels.Across 1,000 independent runs with 10 6 -bit systems, the framework achieved a 98.4% overall pass rate on selected NIST SP 800-22 tests at a 0.01 significance level.Under a depolarizing noise model, the Quantum Bit Error Rate (QBER) remained ≤ 4.2%.Using finite-key evaluation and composable security principles, the system maintained statistical integrity across all stages.The findings demonstrate a successful simulation-based proof-of-concept rather than a hardware-ready solution.Future research will focus on executing the pipeline on physical quantum hardware and evaluating performance under realistic channel loss models.
We present a scalable experimental framework for validating quantum-generated bitstreams using the NIST SP 800-22 statistical test suite. Quantum randomness was simulated using Qiskit’s aer_simulator, generating 10 independent sequences of 10,000,000 bits each. These bitstreams were evaluated across 15 statistical categories including Monobit, Block Frequency, FFT, Linear Complexity, and Excursion-based tests. All but one test passed the NIST-recommended thresholds for both pass rates and p-value distributions. Specifically, the Random Excursion (Standard) test narrowly missed the threshold with 52 out of 56 sequences passing. Despite this, the overall results confirm the high entropy and statistical randomness of the quantum-generated sequences. Our framework also incorporates a batch-based architecture that enforces streak continuity across sequence boundaries and uses SHA-256 hashing for integrity verification. This infrastructure supports reproducibility and lays the foundation for future exploration of probabilistic thresholds in ultra-large-scale quantum bitstreams. In particular, we plan to generate and analyze up to one trillion quantum bits to investigate whether there exists a natural limit to the length of repeated stochastic patterns (streaks) in true randomness.
We evaluated the quality of conventional quantum True Random Number Generator (TRNG) using superconducting qubits from IBM. Our analysis indicated that the 1/0 ratio is severely affected by the noise. We proposed swapping of readout qubit and parameter optimization to fix readout and gate errors. We validate our proposals by running experiments of IBM's su-perconducitng qubit based quantum computer. Experiments show deviation of ≈ 35% from ideal 1/0 ratio for baseline implementation. After the application of our proposed techniques the ratio improves and reaches close to ideal value of 1. The random bits generated through our techniques passes NIST statistical tests as well.
Abstract We present a quantum random number generator (QRNG) based on vacuum fluctuations that meets the statistical and structural entropy source validation criteria defined in NIST standards. We measured how optical imbalance in a balanced homodyne detector affects the dominance of vacuum fluctuations over the quadrature-phase noise of the local oscillator. Based on this analysis, we developed a simple and direct method to quantify the relative strength of vacuum noise, enabling precise balancing of the detector to maximize the vacuum fluctuation contribution. Our approach ensures that the raw entropy exhibits negligible auto-correlation and that the output samples are independent and identically distributed (i.i.d.), without relying on external post-processing beyond standard NIST-approved conditioning. Following a symbol-space reduction strategy guided by NIST SP 800-90B, we extract high-quality 8-bit entropy samples from 16-bit raw data. To produce full-entropy outputs, the samples are processed using the 384-bit version of the Secure Hash algorithm 3 (SHA3-384). The resulting quantum random numbers successfully pass all tests in the NIST statistical test suite (NIST SP 800-22) and achieve a generation rate of approximately 4 Gbps. This work demonstrates a practical and fully standards-aligned QRNG implementation using only vacuum fluctuations and vetted conditioning.