Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Kundu, Satwik"'
Autor:
Kundu, Satwik, Ghosh, Swaroop
With the growing interest in Quantum Machine Learning (QML) and the increasing availability of quantum computers through cloud providers, addressing the potential security risks associated with QML has become an urgent priority. One key concern in th
Externí odkaz:
http://arxiv.org/abs/2411.14412
Autor:
Kundu, Satwik, Ghosh, Swaroop
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 models can effectively generalize from limited training data samples.
Externí odkaz:
http://arxiv.org/abs/2408.09562
Autor:
Kundu, Satwik, Ghosh, Swaroop
The high expenses imposed by current quantum cloud providers, coupled with the escalating need for quantum resources, may incentivize the emergence of cheaper cloud-based quantum services from potentially untrusted providers. Deploying or hosting qua
Externí odkaz:
http://arxiv.org/abs/2405.18746
Cloud hosting of quantum machine learning (QML) models exposes them to a range of vulnerabilities, the most significant of which is the model stealing attack. In this study, we assess the efficacy of such attacks in the realm of quantum computing. We
Externí odkaz:
http://arxiv.org/abs/2402.11687
The exponential run time of quantum simulators on classical machines and long queue times and high costs of real quantum devices present significant challenges in the efficient optimization of Variational Quantum Algorithms (VQAs) like Variational Qu
Externí odkaz:
http://arxiv.org/abs/2307.12449
Using quantum computing, this paper addresses two scientifically pressing and day-to-day relevant problems, namely, chemical retrosynthesis which is an important step in drug/material discovery and security of the semiconductor supply chain. We show
Externí odkaz:
http://arxiv.org/abs/2208.08273
Recent assertions of a potential advantage of Quantum Neural Network (QNN) for specific Machine Learning (ML) tasks have sparked the curiosity of a sizable number of application researchers. The parameterized quantum circuit (PQC), a major building b
Externí odkaz:
http://arxiv.org/abs/2207.01801
Autor:
Kundu, Satwik, Ghosh, Swaroop
In the last few years, quantum computing has experienced a growth spurt. One exciting avenue of quantum computing is quantum machine learning (QML) which can exploit the high dimensional Hilbert space to learn richer representations from limited data
Externí odkaz:
http://arxiv.org/abs/2204.03625
Image classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use convolutional neural
Externí odkaz:
http://arxiv.org/abs/2109.02862
The exponential run time of quantum simulators on classical machines and long queue depths and high costs of real quantum devices present significant challenges in the effective training of Variational Quantum Algorithms (VQAs) like Quantum Neural Ne
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=arXiv_______::d77ddb1f8147108b6f1e2a4597a73a48
http://arxiv.org/abs/2307.12449
http://arxiv.org/abs/2307.12449