On the effects of pseudorandom and quantum-random number generators in soft computing

Research output: Contribution to journalArticle


In this work, we argue that the implications of pseudorandom and quantum-random number generators (PRNG and QRNG)
inexplicably affect the performances and behaviours of various machine learning models that require a random input. These
implications are yet to be explored in soft computing until this work. We use a CPU and a QPU to generate random numbers
for multiple machine learning techniques. Random numbers are employed in the random initial weight distributions of dense
and convolutional neural networks, in which results show a profound difference in learning patterns for the two. In 50 dense
neural networks (25 PRNG/25 QRNG), QRNG increases over PRNG for accent classification at + 0.1%, and QRNG exceeded
PRNG for mental state EEG classification by + 2.82%. In 50 convolutional neural networks (25 PRNG/25 QRNG), the MNIST
and CIFAR-10 problems are benchmarked, and in MNIST the QRNG experiences a higher starting accuracy than the PRNG
but ultimately only exceeds it by 0.02%. In CIFAR-10, the QRNG outperforms PRNG by + 0.92%. The n-random split of a
Random Tree is enhanced towards and new Quantum Random Tree (QRT) model, which has differing classification abilities to
its classical counterpart, 200 trees are trained and compared (100 PRNG/100 QRNG). Using the accent and EEG classification
data sets, a QRT seemed inferior to a RT as it performed on average worse by −0.12%. This pattern is also seen in the EEG
classification problem, where a QRT performs worse than a RT by −0.28%. Finally, the QRT is ensembled into a Quantum
Random Forest (QRF), which also has a noticeable effect when compared to the standard Random Forest (RF). Ten to 100
ensembles of trees are benchmarked for the accent and EEG classification problems. In accent classification, the best RF
(100 RT) outperforms the best QRF (100 QRF) by 0.14% accuracy. In EEG classification, the best RF (100 RT) outperforms
the best QRF (100 QRT) by 0.08% but is extremely more complex, requiring twice the amount of trees in committee. All
differences are observed to be situationally positive or negative and thus are likely data dependent in their observed functional
Original languageEnglish
JournalSoft computing
Early online date28 Oct 2019
Publication statusE-pub ahead of print - 28 Oct 2019

Bibliographical note

© The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.


  • Classification
  • Machine learning
  • Neural networks
  • Quantum computing
  • Soft computing

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