论文标题
神经网络和量子电路的共同设计框架
A Co-Design Framework of Neural Networks and Quantum Circuits Towards Quantum Advantage
论文作者
论文摘要
尽管在各种应用中追求量子优势，但神经网络计算中量子计算机的功能主要是未知的，这主要是由于缺失的链接有效地设计了适合量子电路实现的神经网络模型。在本文中，我们介绍了共同设计框架，即量子流，以提供这样的缺失链接。量子流由新颖的量子友好神经网络（QF-NET），一个映射工具（QF-MAP）组成，用于生成QF-NET的量子电路（QF-CIRC）和执行引擎（QF-FB）。我们发现，为了充分利用量子表示的强度，最好将数据表示为随机变量或单位矩阵中的数字，以便可以通过基本的量子逻辑门直接操作它们。基于这些数据表示，我们提出了两个量子友好的神经网络，即QF-PNET和QF-HNET。使用随机变量的QF-PNET具有更好的灵活性，并且与QF-HNET相比，可以无缝连接两层而无需测量QBIT和逻辑门。另一方面，具有单一矩阵的QF-HNET可以将2^k数据编码为k Qbits，而新颖的算法可以保证成本复杂性为O（k^2）。与经典计算中O（2^k）的成本相比，QF-HNET证明了量子优势。评估结果表明，QF-PNET和QF-HNET分别可以达到97.10％和98.27％的精度。结果进一步表明，对于神经计算的输入尺寸，从16倍增加到2,048，量子流的成本降低从2.4倍增加到64倍。此外，在MNIST数据集上，QF-HNET可以达到94.09％的准确性，而对经典计算机的成本降低达到10.85倍。据我们所知，量子流是第一项证明神经网络计算上潜在量子优势的工作。
Despite the pursuit of quantum advantages in various applications, the power of quantum computers in neural network computations has mostly remained unknown, primarily due to a missing link that effectively designs a neural network model suitable for quantum circuit implementation. In this article, we present the co-design framework, namely QuantumFlow, to provide such a missing link. QuantumFlow consists of novel quantum-friendly neural networks (QF-Nets), a mapping tool (QF-Map) to generate the quantum circuit (QF-Circ) for QF-Nets, and an execution engine (QF-FB). We discover that, in order to make full use of the strength of quantum representation, it is best to represent data in a neural network as either random variables or numbers in unitary matrices, such that they can be directly operated by the basic quantum logical gates. Based on these data representations, we propose two quantum friendly neural networks, QF-pNet and QF-hNet in QuantumFlow. QF-pNet using random variables has better flexibility, and can seamlessly connect two layers without measurement with more qbits and logical gates than QF-hNet. On the other hand, QF-hNet with unitary matrices can encode 2^k data into k qbits, and a novel algorithm can guarantee the cost complexity to be O(k^2). Compared to the cost of O(2^k)in classical computing, QF-hNet demonstrates the quantum advantages. Evaluation results show that QF-pNet and QF-hNet can achieve 97.10% and 98.27% accuracy, respectively. Results further show that for input sizes of neural computation grow from 16 to 2,048, the cost reduction of QuantumFlow increased from 2.4x to 64x. Furthermore, on MNIST dataset, QF-hNet can achieve accuracy of 94.09%, while the cost reduction against the classical computer reaches 10.85x. To the best of our knowledge, QuantumFlow is the first work to demonstrate the potential quantum advantage on neural network computation.
