When Machine Learning Meets Quantum Computers: A Case Study
Along with the development of AI democratization, the machine learning
approach, in particular neural networks, has been applied to wide-range
applications. In different application scenarios, the neural network will be
accelerated on the tailored computing platform. The acceleration of neural
networks on classical computing platforms, such as CPU, GPU, FPGA, ASIC, has
been widely studied; however, when the scale of the application consistently
grows up, the memory bottleneck becomes obvious, widely known as memory-wall.
In response to such a challenge, advanced quantum computing, which can
represent 2^N states with N quantum bits (qubits), is regarded as a promising
solution. It is imminent to know how to design the quantum circuit for
accelerating neural networks. Most recently, there are initial works studying
how to map neural networks to actual quantum processors. To better understand
the state-of-the-art design and inspire new design methodology, this paper
carries out a case study to demonstrate an end-to-end implementation. On the
neural network side, we employ the multilayer perceptron to complete image
classification tasks using the standard and widely used MNIST dataset. On the
quantum computing side, we target IBM Quantum processors, which can be
programmed and simulated by using IBM Qiskit. This work targets the
acceleration of the inference phase of a trained neural network on the quantum
processor. Along with the case study, we will demonstrate the typical procedure
for mapping neural networks to quantum circuits.
Weiwen Jiang Jinjun Xiong Yiyu Shi