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Analysis of a Programmable Quantum Annealer as a Random Number Generator

Elijah Pelofske

TL;DR

This work evaluates whether a programmable quantum annealer (D-Wave 2000Q) can serve as an unbiased QRNG despite hardware noise and drift. It collects over 2.5e9 time-ordered bits per configuration across eight settings and subjects them to the NIST SP800-22 Rev 1a and SP800-90B non-IID min-entropy tests. The findings show that no device setting yields fully random bits; server-side post-processing improves entropy and test performance but raw data remain biased, with min-entropy values ranging from about 0.824 to 0.889. The study highlights that current QA hardware biases and correlations limit QRNG viability, pointing to the need for higher coherence, better calibration, and careful post-processing strategies in future quantum RNG implementations.

Abstract

Quantum devices offer a highly useful function - that is generating random numbers in a non-deterministic way since the measurement of a quantum state is not deterministic. This means that quantum devices can be constructed that generate qubits in a uniform superposition and then measure the state of those qubits. If the preparation of the qubits in a uniform superposition is unbiased, then quantum computers can be used to create high entropy, secure random numbers. Quantum annealing (QA) is a type of analog quantum computation that is a relaxed form of adiabatic quantum computation and uses quantum fluctuations in order to search for ground state solutions of a programmable Ising model. Here we present extensive experimental random number results from a D-Wave 2000Q quantum annealer, totaling over 20 billion bits of QA measurements, which is significantly larger than previous D-Wave QA random number generator studies. Current quantum annealers are susceptible to noise from environmental sources and calibration errors, and are not in general unbiased samplers. Therefore, it is of interest to quantify whether noisy quantum annealers can effectively function as an unbiased QRNG. The amount of data that was collected from the quantum annealer allows a comprehensive analysis of the random bits to be performed using the NIST SP 800-22 Rev 1a testsuite, as well as min-entropy estimates from NIST SP 800-90B. The randomness tests show that the generated random bits from the D-Wave 2000Q are biased, and not unpredictable random bit sequences. With no server-side sampling post-processing, the $1$ microsecond annealing time measurements had a min-entropy of $0.824$.

Analysis of a Programmable Quantum Annealer as a Random Number Generator

TL;DR

This work evaluates whether a programmable quantum annealer (D-Wave 2000Q) can serve as an unbiased QRNG despite hardware noise and drift. It collects over 2.5e9 time-ordered bits per configuration across eight settings and subjects them to the NIST SP800-22 Rev 1a and SP800-90B non-IID min-entropy tests. The findings show that no device setting yields fully random bits; server-side post-processing improves entropy and test performance but raw data remain biased, with min-entropy values ranging from about 0.824 to 0.889. The study highlights that current QA hardware biases and correlations limit QRNG viability, pointing to the need for higher coherence, better calibration, and careful post-processing strategies in future quantum RNG implementations.

Abstract

Quantum devices offer a highly useful function - that is generating random numbers in a non-deterministic way since the measurement of a quantum state is not deterministic. This means that quantum devices can be constructed that generate qubits in a uniform superposition and then measure the state of those qubits. If the preparation of the qubits in a uniform superposition is unbiased, then quantum computers can be used to create high entropy, secure random numbers. Quantum annealing (QA) is a type of analog quantum computation that is a relaxed form of adiabatic quantum computation and uses quantum fluctuations in order to search for ground state solutions of a programmable Ising model. Here we present extensive experimental random number results from a D-Wave 2000Q quantum annealer, totaling over 20 billion bits of QA measurements, which is significantly larger than previous D-Wave QA random number generator studies. Current quantum annealers are susceptible to noise from environmental sources and calibration errors, and are not in general unbiased samplers. Therefore, it is of interest to quantify whether noisy quantum annealers can effectively function as an unbiased QRNG. The amount of data that was collected from the quantum annealer allows a comprehensive analysis of the random bits to be performed using the NIST SP 800-22 Rev 1a testsuite, as well as min-entropy estimates from NIST SP 800-90B. The randomness tests show that the generated random bits from the D-Wave 2000Q are biased, and not unpredictable random bit sequences. With no server-side sampling post-processing, the microsecond annealing time measurements had a min-entropy of .
Paper Structure (7 sections, 3 equations, 3 figures, 3 tables)

This paper contains 7 sections, 3 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Quantum Annealing
  4. Testing for randomness
  5. Results
  6. Discussion and Conclusion
  7. Acknowledgments

Figures (3)

  • Figure 1: LANL D-Wave 2000Q hardware connectivity graph (the name of this type of connectivity graph is Chimera, which is in general a sparse but scalable hardware implementation of quantum annealing). This device has 2032 active qubits (due to hardware defects the full Chimera lattice of $2048$ qubits is not active). The chip id of this device is DW_2000Q_LANL.
  • Figure 2: Bit plot of a subset of the D-Wave QRNG measurements visually showing $+1$ and $-1$ qubit states, for the $1$ microsecond annealing time and no server side post processing runs (Test 5). There are $2032$ row indices, corresponding to the $2032$ qubit indices, and $300$ column indices corresponding to $300$ time ordered anneal-readout cycles. Noticeably, there are clearly correlations in the time ordered bit sequences.
  • Figure 3: Bit plot of a subset of the D-Wave QRNG measurements visually showing $+1$ and $-1$ qubit states, for the $2000$ microsecond annealing time and no server side post processing runs (Test 6). There are $2032$ row indices, corresponding to the $2032$ qubit indices, and $300$ column indices corresponding to $300$ time ordered anneal-readout cycles.