A Comparison of Encoding Techniques for an Analog Quantum Emulation Device

Quantum computers can outperform classical computers in certain tasks. However, there are still many challenges to the current quantum computers such as decoherence and fault tolerance, and other drawbacks such as portability and accessibility. In this study, we circumvent these issues by realizing an analog quantum emulation device (AQED) where each qubit state is represented by a unique analog signal. It is possible to do this because previously it was shown that Hermitian operations on a Hilbert space are not unique to quantum systems and can also be applied to a basis of complex signals that form a Hilbert space. Orthogonality of the complex signal basis can be maintained by separating the signals into the frequency domain or the spatial domain. We study both these approaches and present a comparison. We finally realize the entire device on a UMC 180nm processing node and demonstrate the computational advantage of an AQED by emulating Grover's search algorithm (GSA) and Quantum Fourier Transform (QFT). We also present the equivalent quantum volume achieved by this device

Graph Neural Networks-Based User Pairing in Wireless Communication Systems

Recently, deep neural networks have emerged as a solution to solve NP-hard wireless resource allocation problems in real-time. However, multi-layer perceptron (MLP) and convolutional neural network (CNN) structures, which are inherited from image processing tasks, are not optimized for wireless network problems. As network size increases, these methods get harder to train and generalize. User pairing is one such essential NP-hard optimization problem in wireless communication systems that entails selecting users to be scheduled together while minimizing interference and maximizing throughput. In this paper, we propose an unsupervised graph neural network (GNN) approach to efficiently solve the user pairing problem. Our proposed method utilizes the Erdos goes neural pipeline to significantly outperform other scheduling methods such as k-means and semi-orthogonal user scheduling (SUS). At 20 dB SNR, our proposed approach achieves a 49% better sum rate than k-means and a staggering 95% better sum rate than SUS while consuming minimal time and resources. The scalability of the proposed method is also explored as our model can handle dynamic changes in network size without experiencing a substantial decrease in performance. Moreover, our model can accomplish this without being explicitly trained for larger or smaller networks facilitating a dynamic functionality that cannot be achieved using CNNs or MLPs