10 papers
Quantum-Native Maximum Likelihood Detection in Random Access Channel with Overloaded MIMO
Hyoga Iizumi, Naoki Ishikawa, Shunsuke Uehashi +3
In this paper, we propose a quantum-native formulation of maximum likelihood detection (MLD) for overloaded multiple-input multiple-output (MIMO) systems in a random access channel…
Geo-ADAPT-VQE: Quantum Information Metric-Aware Circuit Optimization for Quantum Chemistry
Mohammad Aamir Sohail, Toshiaki Koike-Akino
Adaptive ansatz construction has emerged as a powerful technique for reducing circuit depth and improving optimization efficiency in variational quantum eigensolvers. However, exis…
EDRP: Enhanced Dynamic Relay Point Protocol for Data Dissemination in Multi-hop Wireless IoT Networks
Jothi Prasanna Shanmuga Sundaram, Magzhan Gabidolla, Luis Fujarte +11
Emerging IoT applications are transitioning from battery-powered to grid-powered nodes. DRP, a contention-based data dissemination protocol, was developed for these applications. T…
Quantum-PEFT: Ultra parameter-efficient fine-tuning
Toshiaki Koike-Akino, Francesco Tonin, Yongtao Wu +3
This paper introduces Quantum-PEFT that leverages quantum computations for parameter-efficient fine-tuning (PEFT). Unlike other additive PEFT methods, such as low-rank adaptation (…
Exploring User-level Gradient Inversion with a Diffusion Prior
Zhuohang Li, Andrew Lowy, Jing Liu +4
We explore user-level gradient inversion as a new attack surface in distributed learning. We first investigate existing attacks on their ability to make inferences about private in…
Hybrid Quantum-Classical Neural Networks for Downlink Beamforming Optimization
Juping Zhang, Gan Zheng, Toshiaki Koike-Akino +2
This paper investigates quantum machine learning to optimize the beamforming in a multiuser multiple-input single-output downlink system. We aim to combine the power of quantum neu…