3 citations · 5 across the 19 of their papers we have counts for
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Quantum Sensing Applications in Consumer Electronics: Emerging Methods and Near-Term Use Cases
Sounak Bhowmik, Himanshu Thapliyal
Quantum sensing is emerging as a practical means of improving sensing capabilities in consumer electronics, particularly where size, power, and noise constrain classical approaches…
QML for Quantum Sensing under Measurement-Induced Information Loss
Sounak Bhowmik, Himanshu Thapliyal
Nitrogen-vacancy (NV) centers in diamond can serve as highly sensitive solid-state quantum sensors for high-sensitivity magnetometry. However, in the noisy intermediate-scale quant…
Quantum Transfer Learning to Boost Dementia Detection
Sounak Bhowmik, Talita Perciano, Himanshu Thapliyal
Dementia is a devastating condition with profound implications for individuals, families, and healthcare systems. Early and accurate detection of dementia is critical for timely in…
Quantum Properties Trojans (QuPTs) for Attacking Quantum Neural Networks
Sounak Bhowmik, Travis S. Humble, Himanshu Thapliyal
Quantum neural networks (QNN) hold immense potential for the future of quantum machine learning (QML). However, QNN security and robustness remain largely unexplored. In this work,…
Quantum-Hybrid Support Vector Machines for Anomaly Detection in Industrial Control Systems
Tyler Cultice, Md. Saif Hassan Onim, Annarita Giani +1
Sensitive data captured by Industrial Control Systems (ICS) play a large role in the safety and integrity of many critical infrastructures. Detection of anomalous or malicious data…
Residue Number System (RNS) based Distributed Quantum Multiplication
Bhaskar Gaur, Himanshu Thapliyal
Multiplication of quantum states is a frequently used function or subroutine in quantum algorithms and applications, making quantum multipliers an essential component of quantum ar…
Detection of Physiological Data Tampering Attacks with Quantum Machine Learning
Md. Saif Hassan Onim, Himanshu Thapliyal
The widespread use of cloud-based medical devices and wearable sensors has made physiological data susceptible to tampering. These attacks can compromise the reliability of healthc…
Quantum Hybrid Support Vector Machines for Stress Detection in Older Adults
Md Saif Hassan Onim, Travis S. Humble, Himanshu Thapliyal
Stress can increase the possibility of cognitive impairment and decrease the quality of life in older adults. Smart healthcare can deploy quantum machine learning to enable prevent…
Crosstalk Attack Resilient RNS Quantum Addition
Bhaskar Gaur, Himanshu Thapliyal
As quantum computers scale, the rise of multi-user and cloud-based quantum platforms can lead to new security challenges. Attacks within shared execution environments become increa…
GTQCP: Greedy Topology-Aware Quantum Circuit Partitioning
Joseph Clark, Travis S. Humble, Himanshu Thapliyal
We propose Greedy Topology-Aware Quantum Circuit Partitioning (GTQCP), a novel quantum gate circuit partitioning method which partitions circuits by applying a greedy heuristic to…
Peephole Optimization for Quantum Approximate Synthesis
Joseph Clark, Himanshu Thapliyal
Peephole optimization of quantum circuits provides a method of leveraging standard circuit synthesis approaches into scalable quantum circuit optimization. One application of this…
Anomaly Detection for Real-World Cyber-Physical Security using Quantum Hybrid Support Vector Machines
Tyler Cultice, Md. Saif Hassan Onim, Annarita Giani +1
Cyber-physical control systems are critical infrastructures designed around highly responsive feedback loops that are measured and manipulated by hundreds of sensors and controller…
Quantum Machine Learning for Anomaly Detection in Consumer Electronics
Sounak Bhowmik, Himanshu Thapliyal
Anomaly detection is a crucial task in cyber security. Technological advancement brings new cyber-physical threats like network intrusion, financial fraud, identity theft, and prop…
Transfer Learning Based Hybrid Quantum Neural Network Model for Surface Anomaly Detection
Sounak Bhowmik, Himanshu Thapliyal
The rapid increase in the volume of data increased the size and complexity of the deep learning models. These models are now more resource-intensive and time-consuming for training…
Novel Optimized Designs of Modulo Adder for Quantum Computing
Bhaskar Gaur, Himanshu Thapliyal
Quantum modular adders are one of the most fundamental yet versatile quantum computation operations. They help implement functions of higher complexity, such as subtraction and mul…
Residue Number System (RNS) based Distributed Quantum Addition
Bhaskar Gaur, Travis S. Humble, Himanshu Thapliyal
Quantum Arithmetic faces limitations such as noise and resource constraints in the current Noisy Intermediate Scale Quantum (NISQ) era quantum computers. We propose using Distribut…