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

Abstract

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.

26 pages, 12 figures, 3 tables

References in corpus (15)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• ADADELTA: An Adaptive Learning Rate Method
• Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods
• Strong quantum computational advantage using a superconducting quantum processor
• Quantum-enhanced machine learning
• Superconducting Quantum Computing: A Review
• Quantum Error Correction: An Introductory Guide
• The Bitter Truth About Quantum Algorithms in the NISQ Era
• Real time demonstration of high bitrate quantum random number generation with coherent laser light
• Maximization of Extractable Randomness in a Quantum Random-Number Generator
• Experimental Evidence of Quantum Randomness Incomputability
• Quantum information and randomness
• Detecting Temporal Correlation via Quantum Random Number Generation
• Pointer-based simultaneous measurements of conjugate observables in a thermal environment