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.

翻译：近年来实用量子计算的进展催生了多种基于云的量子计算平台，使研究人员能够在含噪中等规模量子（NISQ）设备上评估其算法。量子计算机的一个共同特性是能够产生真正的随机性，而非经典系统获得的伪随机性。探究这种真实量子随机性在机器学习领域的影响具有吸引力，近期研究结果隐约表明，使用量子随机数确实可能带来益处。为更清晰地阐明这一主题，我们通过数值实验，实证研究了硬件有偏量子随机数对人工神经网络权重初始化的影响。与无偏量子随机数、经典伪随机数生成器产生的有偏及无偏随机数相比，我们未发现统计学显著差异。实验所用的量子随机数均来自真实量子硬件。