arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2108.00698quant-ph
收藏

基于随机振荡器网络的在线量子时间序列处理

Online quantum time series processing with random oscillator networks

Johannes Nokkala

摘要:

水库计算是一个强大的机器学习范例在线时间序列处理。它在混沌时间序列预测和连续语音识别等任务中达到了最先进的性能,这要归功于其高计算能力和低训练成本的独特组合,这使得它与传统训练的递归神经网络等替代方案不同,而且可以在专用硬件中实现,可能导致非常紧凑和高效的水库计算机。最近已经提出了使用随机量子系统,利用量子动力学的复杂性进行经典时间序列处理。然而,从量子系统中提取输出而不过多地干扰其状态是有问题的,并且可以预期将成为这种方法的瓶颈。在这里,我们提出了一个水库计算启发的方法,在线处理的时间序列组成的量子信息,回避测量问题。我们通过将两个典型的基准任务从经典的水库计算推广到量子信息,并引入一个没有经典模拟的任务来说明它的力量,在这个任务中,一个随机系统被训练成在从不直接相互作用的系统之间创建和分配纠缠。最后,我们讨论部分推广,只有输入或只有输出时间序列是量子的。

英文摘要:

Reservoir computing is a powerful machine learning paradigm for online time series processing. It has reached state-of-the-art performance in tasks such as chaotic time series prediction and continuous speech recognition thanks to its unique combination of high computational power and low training cost which sets it aside from alternatives such as traditionally trained recurrent neural networks, and furthermore is amenable to implementations in dedicated hardware, potentially leading to extremely compact and efficient reservoir computers. Recently the use of random quantum systems has been proposed, leveraging the complexity of quantum dynamics for classical time series processing. Extracting the output from a quantum system without disturbing its state too much is problematic however, and can be expected to become a bottleneck in such approaches. Here we propose a reservoir computing inspired approach to online processing of time series consisting of quantum information, sidestepping the measurement problem. We illustrate its power by generalizing two paradigmatic benchmark tasks from classical reservoir computing to quantum information and introducing a task without a classical analogue where a random system is trained to both create and distribute entanglement between systems that never directly interact. Finally, we discuss partial generalizations where only the input or only the output time series is quantum.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑