3 citations · 5 across the 12 of their papers we have counts for
12 papers
Q-Fusion: Diffusing Quantum Circuits
Collin Beaudoin, Swaroop Ghosh
Quantum computing holds great potential for solving socially relevant and computationally complex problems. Furthermore, quantum machine learning (QML) promises to rapidly improve…
Evaluating Effects of Augmented SELFIES for Molecular Understanding Using QK-LSTM
Collin Beaudoin, Swaroop Ghosh
Identifying molecular properties, including side effects, is a critical yet time-consuming step in drug development. Failing to detect these side effects before regulatory submissi…
Inverse-Transpilation: Reverse-Engineering Quantum Compiler Optimization Passes from Circuit Snapshots
Satwik Kundu, Swaroop Ghosh
Circuit compilation, a crucial process for adapting quantum algorithms to hardware constraints, often operates as a ``black box,'' with limited visibility into the optimization tec…
Dataset Distillation for Quantum Neural Networks
Koustubh Phalak, Junde Li, Swaroop Ghosh
Training Quantum Neural Networks (QNNs) on large amount of classical data can be both time consuming as well as expensive. Higher amount of training data would require higher numbe…
Optimizing Quantum Embedding using Genetic Algorithm for QML Applications
Koustubh Phalak, Archisman Ghosh, Swaroop Ghosh
Quantum Embeddings (QE) are essential for loading classical data into quantum systems for Quantum Machine Learning (QML). The performance of QML algorithms depends on the type of Q…
Adversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era
Satwik Kundu, Swaroop Ghosh
With the growing interest in Quantum Machine Learning (QML) and the increasing availability of quantum computers through cloud providers, addressing the potential security risks as…
AI-driven Reverse Engineering of QML Models
Archisman Ghosh, Swaroop Ghosh
Quantum machine learning (QML) is a rapidly emerging area of research, driven by the capabilities of Noisy Intermediate-Scale Quantum (NISQ) devices. With the progress in the resea…
Security Concerns in Quantum Machine Learning as a Service
Satwik Kundu, Swaroop Ghosh
Quantum machine learning (QML) is a category of algorithms that employ variational quantum circuits (VQCs) to tackle machine learning tasks. Recent discoveries have shown that QML…
The Quantum Imitation Game: Reverse Engineering of Quantum Machine Learning Models
Archisman Ghosh, Swaroop Ghosh
Quantum Machine Learning (QML) amalgamates quantum computing paradigms with machine learning models, providing significant prospects for solving complex problems. However, with the…
QuaLITi: Quantum Machine Learning Hardware Selection for Inferencing with Top-Tier Performance
Koustubh Phalak, Swaroop Ghosh
Quantum Machine Learning (QML) is an accelerating field of study that leverages the principles of quantum computing to enhance and innovate within machine learning methodologies. H…
AltGraph: Redesigning Quantum Circuits Using Generative Graph Models for Efficient Optimization
Collin Beaudoin, Koustubh Phalak, Swaroop Ghosh
Quantum circuit transformation aims to produce equivalent circuits while optimizing for various aspects such as circuit depth, gate count, and compatibility with modern Noisy Inter…
Non-parametric Greedy Optimization of Parametric Quantum Circuits
Koustubh Phalak, Swaroop Ghosh
The use of Quantum Neural Networks (QNN) that are analogous to classical neural networks, has greatly increased in the past decade owing to the growing interest in the field of Qua…