arXiv 2302.104672023-02-21quant-phcs.ETcs.LG

脉冲量子比特的行为及其在前馈网络中的应用

On the Behaviour of Pulsed Qubits and their Application to Feed Forward Networks

Deakin University（迪肯大学）

Matheus Moraes Hammes, Antonio Robles-Kelly

摘要：

在过去的二十年中，机器学习和量子计算的结合一直是一个不断增长的感兴趣的话题，但到目前为止，量子计算硬件的限制在某种程度上限制了复杂的多量子位操作用于机器学习。在本文中，我们利用脉冲量子比特上观察到的量子态概率的周期性，提出了一个单量子比特前馈块，其架构允许以类似于经典神经网络的方式使用经典参数。为此，我们调制激发量子位的脉冲，以诱导围绕布洛赫球的叠加旋转。这里提出的方法具有每个块采用单个量子位的优点。因此，它相对于块的数量是线性的，而不是相对于神经元的数量的多项式，这与其他地方的大多数方法相反。此外，由于它采用经典参数，因此可以在训练时进行大量的迭代和更新，而无需停留在相干时间上，并且如果需要，可以重新使用和存储梯度。我们还展示了如何使用正弦平方激活函数来类比神经网络，并说明了如何在脉冲启用的量子计算机上使用和实现这里提出的前馈块。

英文摘要：

In the last two decades, the combination of machine learning and quantum computing has been an ever-growing topic of interest but, to this date, the limitations of quantum computing hardware have somewhat restricted the use of complex multi-qubit operations for machine learning. In this paper, we capitalize on the cyclical nature of quantum state probabilities observed on pulsed qubits to propose a single-qubit feed forward block whose architecture allows for classical parameters to be used in a way similar to classical neural networks. To do this, we modulate the pulses exciting qubits to induce superimposed rotations around the Bloch Sphere. The approach presented here has the advantage of employing a single qubit per block. Thus, it is linear with respect to the number of blocks, not polynomial with respect to the number of neurons as opposed to the majority of methods elsewhere. Further, since it employs classical parameters, a large number of iterations and updates at training can be effected without dwelling on coherence times and the gradients can be reused and stored if necessary. We also show how an analogy can be drawn to neural networks using sine-squared activation functions and illustrate how the feed-forward block presented here may be used and implemented on pulse-enabled quantum computers.

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