102 citations · 331 across the 58 of their papers we have counts for
59 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…
Variational Randomized Smoothing for Sample-Wise Adversarial Robustness
Ryo Hase, Ye Wang, Toshiaki Koike-Akino +2
Randomized smoothing is a defensive technique to achieve enhanced robustness against adversarial examples which are small input perturbations that degrade the performance of neural…
Efficient Differentially Private Fine-Tuning of Diffusion Models
Jing Liu, Andrew Lowy, Toshiaki Koike-Akino +2
The recent developments of Diffusion Models (DMs) enable generation of astonishingly high-quality synthetic samples. Recent work showed that the synthetic samples generated by the…
TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models
Haomiao Ni, Bernhard Egger, Suhas Lohit +5
Text-conditioned image-to-video generation (TI2V) aims to synthesize a realistic video starting from a given image (e.g., a woman's photo) and a text description (e.g., "a woman is…
SuperLoRA: Parameter-Efficient Unified Adaptation of Multi-Layer Attention Modules
Xiangyu Chen, Jing Liu, Ye Wang +4
Low-rank adaptation (LoRA) and its variants are widely employed in fine-tuning large models, including large language models for natural language processing and diffusion models fo…
AutoHLS: Learning to Accelerate Design Space Exploration for HLS Designs
Md Rubel Ahmed, Toshiaki Koike-Akino, Kieran Parsons +1
High-level synthesis (HLS) is a design flow that leverages modern language features and flexibility, such as complex data structures, inheritance, templates, etc., to prototype har…
Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?
Andrew Lowy, Zhuohang Li, Jing Liu +3
For small privacy parameter , -differential privacy (DP) provides a strong worst-case guarantee that no membership inference attack (MIA) can succeed at determining whether a…
Stabilizing Subject Transfer in EEG Classification with Divergence Estimation
Niklas Smedemark-Margulies, Ye Wang, Toshiaki Koike-Akino +4
Classification models for electroencephalogram (EEG) data show a large decrease in performance when evaluated on unseen test sub jects. We reduce this performance decrease using ne…
Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image Synthesis
Nithin Gopalakrishnan Nair, Anoop Cherian, Suhas Lohit +4
Conditional generative models typically demand large annotated training sets to achieve high-quality synthesis. As a result, there has been significant interest in designing models…
quEEGNet: Quantum AI for Biosignal Processing
Toshiaki Koike-Akino, Ye Wang
In this paper, we introduce an emerging quantum machine learning (QML) framework to assist classical deep learning methods for biosignal processing applications. Specifically, we p…
Adversarial Bi-Regressor Network for Domain Adaptive Regression
Haifeng Xia, Pu Perry Wang, Toshiaki Koike-Akino +3
Domain adaptation (DA) aims to transfer the knowledge of a well-labeled source domain to facilitate unlabeled target learning. When turning to specific tasks such as indoor (Wi-Fi)…
Quantum Feature Extraction for THz Multi-Layer Imaging
Toshiaki Koike-Akino, Pu Wang, Genki Yamashita +2
A learning-based THz multi-layer imaging has been recently used for contactless three-dimensional (3D) positioning and encoding. We show a proof-of-concept demonstration of an emer…
AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications
Toshiaki Koike-Akino, Pu Wang, Ye Wang
Commercial Wi-Fi devices can be used for integrated sensing and communications (ISAC) to jointly exchange data and monitor indoor environment. In this paper, we investigate a proof…
Learning to Learn Quantum Turbo Detection
Bryan Liu, Toshiaki Koike-Akino, Ye Wang +1
This paper investigates a turbo receiver employing a variational quantum circuit (VQC). The VQC is configured with an ansatz of the quantum approximate optimization algorithm (QAOA…
Variational Quantum Compressed Sensing for Joint User and Channel State Acquisition in Grant-Free Device Access Systems
Bryan Liu, Toshiaki Koike-Akino, Ye Wang +1
This paper introduces a new quantum computing framework integrated with a two-step compressed sensing technique, applied to a joint channel estimation and user identification probl…
Quantum Transfer Learning for Wi-Fi Sensing
Toshiaki Koike-Akino, Pu Wang, Ye Wang
Beyond data communications, commercial-off-the-shelf Wi-Fi devices can be used to monitor human activities, track device locomotion, and sense the ambient environment. In particula…
Multi-Modal Recurrent Fusion for Indoor Localization
Jianyuan Yu, Pu, Wang +2
This paper considers indoor localization using multi-modal wireless signals including Wi-Fi, inertial measurement unit (IMU), and ultra-wideband (UWB). By formulating the localizat…
Line-field Coherent Sensing with LED Illumination
Celalettin Yurdakul, David S. Millar, Hassan Mansour +5
We describe a method of low-coherence interferometry based optical profilometry using standard light-emitting diode (LED) illumination and complementary metal-oxide-semiconductor (…
Federated AirNet: Hybrid Digital-Analog Neural Network Transmission for Federated Learning
Takuya Fujihashi, Toshiaki Koike-Akino, Takashi Watanabe
A key issue in federated learning over wireless channels is how to exchange a large number of the model parameters via time-varying channels. Two types of solutions based on digita…