Zobrazeno 1 - 10
of 310
pro vyhledávání: '"Ghosh, Swaroop"'
Autor:
Roy, Rupshali, Ghosh, Swaroop
Quantum circuits constitute Intellectual Property (IP) of the quantum developers and users, which needs to be protected from theft by adversarial agents, e.g., the quantum cloud provider or a rogue adversary present in the cloud. This necessitates th
Externí odkaz:
http://arxiv.org/abs/2409.01484
Autor:
Ghosh, Archisman, Ghosh, Swaroop
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 research of QML models, there is a rise in third-party quantum cloud servic
Externí odkaz:
http://arxiv.org/abs/2408.16929
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:
Upadhyay, Suryansh, Ghosh, Swaroop
Quantum computing (QC) has the potential to revolutionize fields like machine learning, security, and healthcare. Quantum machine learning (QML) has emerged as a promising area, enhancing learning algorithms using quantum computers. However, QML mode
Externí odkaz:
http://arxiv.org/abs/2407.14687
Autor:
Ghosh, Archisman, Ghosh, Swaroop
Quantum Machine Learning (QML) amalgamates quantum computing paradigms with machine learning models, providing significant prospects for solving complex problems. However, with the expansion of numerous third-party vendors in the Noisy Intermediate-S
Externí odkaz:
http://arxiv.org/abs/2407.07237
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
Autor:
Phalak, Koustubh, Ghosh, Swaroop
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. However, Noisy Intermediate-Scale Quantum (NISQ) computers suffer from
Externí odkaz:
http://arxiv.org/abs/2405.11194
In this paper, we investigate the impact of bit flip errors in FPGA memories in control electronics on quantum computing systems. FPGA memories are integral in storing the amplitude and phase information pulse envelopes, which are essential for gener
Externí odkaz:
http://arxiv.org/abs/2405.05511
Autor:
Upadhyay, Suryansh, Ghosh, Swaroop
Quantum computing (QC) is poised to revolutionize problem solving across various fields, with research suggesting that systems with over 50 qubits may achieve quantum advantage surpassing supercomputers in certain optimization tasks. As the hardware
Externí odkaz:
http://arxiv.org/abs/2405.00863
Quantum Generative Adversarial Networks (qGANs) are at the forefront of image-generating quantum machine learning models. To accommodate the growing demand for Noisy Intermediate-Scale Quantum (NISQ) devices to train and infer quantum machine learnin
Externí odkaz:
http://arxiv.org/abs/2404.16156