# On the effects of biased quantum random numbers on the initialization of artificial neural networks

arXiv: https://arxiv.org/abs/2108.13329 · HTML: https://mlsanity.com/paper/2108.13329/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 89 references that are in the map.
- First posted on arXiv 30 Aug 2021.

## Builds on (most cited first, 9 of 9)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Quantum-enhanced machine learning](https://mlsanity.com/paper/1610.08251/) (2016)
- [Pseudo Random Number Generation: a Reinforcement Learning approach](https://mlsanity.com/paper/1912.11531/) (2019)
- [Quantum Random Numbers generated by the Cloud Superconducting Quantum Computer](https://mlsanity.com/paper/1906.04410/) (2019)
- [Detecting Temporal Correlation via Quantum Random Number Generation](https://mlsanity.com/paper/2004.01330/) (2020)

## Built on by (most cited first, 2 of 2)

- [Advances in Machine Learning: Where Can Quantum Techniques Help?](https://mlsanity.com/paper/2507.08379/) (2025)
- [Quantum Adiabatic Generation of Human-Like Passwords](https://mlsanity.com/paper/2506.08917/) (2025)

## Abstract (arXiv)

Recent advances in practical quantum computing have led to a variety of cloud-based quantum computing platforms that allow researchers to evaluate their algorithms on noisy intermediate-scale quantum (NISQ) devices. A common property of quantum computers is that they can exhibit instances of true randomness as opposed to pseudo-randomness obtained from classical systems. Investigating the effects of such true quantum randomness in the context of machine learning is appealing, and recent results vaguely suggest that benefits can indeed be achieved from the use of quantum random numbers. To shed some more light on this topic, we empirically study the effects of hardware-biased quantum random numbers on the initialization of artificial neural network weights in numerical experiments. We find no statistically significant difference in comparison with unbiased quantum random numbers as well as biased and unbiased random numbers from a classical pseudo-random number generator. The quantum random numbers for our experiments are obtained from real quantum hardware.