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pro vyhledávání: '"GHOSH, SWAROOP"'
Many third-party cloud providers set up quantum hardware as a service that includes a wide range of qubit technologies and architectures to maximize performance at minimal cost. However, there is little visibility to where the execution of the circui
Externí odkaz:
http://arxiv.org/abs/2412.18939
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 QE and how features are mapped to qubits. Traditionally, the optimal em
Externí odkaz:
http://arxiv.org/abs/2412.00286
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
Quantum error correction (QEC) is crucial for ensuring the reliability of quantum computers. However, implementing QEC often requires a significant number of qubits, leading to substantial overhead. One of the major challenges in quantum computing is
Externí odkaz:
http://arxiv.org/abs/2411.12813
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