Hybrid Quantum Neural Network based Indoor User Localization using Cloud Quantum Computing

This paper proposes a hybrid quantum neural network (HQNN) for indoor user localization using received signal strength indicator (RSSI) values. We use publicly available RSSI datasets for indoor localization using WiFi, Bluetooth, and Zigbee to test the performance of the proposed HQNN. We also compare the performance of the HQNN with the recently proposed quantum fingerprinting-based user localization method. Our results show that the proposed HQNN performs better than the quantum fingerprinting algorithm since the HQNN has trainable parameters in the quantum circuits, whereas the quantum fingerprinting algorithm uses a fixed quantum circuit to calculate the similarity between the test data point and the fingerprint dataset. Unlike prior works, we also test the performance of the HQNN and quantum fingerprint algorithm on a real IBM quantum computer using cloud quantum computing services. Therefore, this paper examines the performance of the HQNN on noisy intermediate scale (NISQ) quantum devices using real-world RSSI localization datasets. The novelty of our approach lies in the use of simple feature maps and ansatz with fewer neurons, alongside testing on actual quantum hardware using real-world data, demonstrating practical applicability in real-world scenarios.

Optimal Grouping Strategy for Reconfigurable Intelligent Surface Assisted Wireless Communications

The channel estimation overhead of reconfigurable intelligent surface (RIS) assisted communication systems can be prohibitive. Prior works have demonstrated via simulations that grouping neighbouring RIS elements can help to reduce the pilot overhead and improve achievable rate. In this paper, we present an analytical study of RIS element grouping. We derive a tight closed-form upper bound for the achievable rate and then maximize it with respect to the group size. Our analysis reveals that more coarse-grained grouping is important-when the channel coherence time is low (high mobility scenarios) or the transmit power is large. We also demonstrate that optimal grouping can yield significant performance improvements over simple `On-Off' RIS element switching schemes that have been recently considered.

Machine Learning Based Parameter Estimation of Gaussian Quantum States

We propose a machine learning framework for parameter estimation of single mode Gaussian quantum states. Under a Bayesian framework, our approach estimates parameters of suitable prior distributions from measured data. For phase-space displacement and squeezing parameter estimation, this is achieved by introducing Expectation-Maximization (EM) based algorithms, while for phase parameter estimation an empirical Bayes method is applied. The estimated prior distribution parameters along with the observed data are used for finding the optimal Bayesian estimate of the unknown displacement, squeezing and phase parameters. Our simulation results show that the proposed algorithms have estimation performance that is very close to that of Genie Aided Bayesian estimators, that assume perfect knowledge of the prior parameters. Our proposed methods can be utilized by experimentalists to find the optimum Bayesian estimate of parameters of Gaussian quantum states by using only the observed measurements without requiring any knowledge about the prior distribution parameters.

MIMO Terahertz Quantum Key Distribution

We propose a multiple-input multiple-output (MIMO) quantum key distribution (QKD) scheme for improving the secret key rates and increasing the maximum transmission distance for terahertz (THz) frequency range applications operating at room temperature. We propose a transmit beamforming and receive combining scheme that converts the rank-$r$ MIMO channel between Alice and Bob into $r$ parallel lossy quantum channels whose transmittances depend on the non-zero singular values of the MIMO channel. The MIMO transmission scheme provides a multiplexing gain of $r$, along with a beamforming and array gain equal to the product of the number of transmit and receive antennas. This improves the secret key rate and extends the maximum transmission distance. Our simulation results show that multiple antennas are necessary to overcome the high free-space path loss at THz frequencies. Positive key rates are achievable in the $10-30$ THz frequency range that can be used for both indoor and outdoor QKD applications for beyond fifth generation ultra-secure wireless communications systems.