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pro vyhledávání: '"Heredge, Jamie A."'
We introduce several probabilistic quantum algorithms that overcome the normal unitary restrictions in quantum machine learning by leveraging the Linear Combination of Unitaries (LCU) method. Among our investigations are quantum native implementation
Externí odkaz:
http://arxiv.org/abs/2405.17388
Autor:
Heredge, Jamie, Kumar, Niraj, Herman, Dylan, Chakrabarti, Shouvanik, Yalovetzky, Romina, Sureshbabu, Shree Hari, Li, Changhao, Pistoia, Marco
Ensuring data privacy in machine learning models is critical, particularly in distributed settings where model gradients are typically shared among multiple parties to allow collaborative learning. Motivated by the increasing success of recovering in
Externí odkaz:
http://arxiv.org/abs/2405.08801
The data encoding circuits used in quantum support vector machine (QSVM) kernels play a crucial role in their classification accuracy. However, manually designing these circuits poses significant challenges in terms of time and performance. To addres
Externí odkaz:
http://arxiv.org/abs/2312.01562
Publikováno v:
PRX Quantum 5, 030320, 2024
Exploiting the power of quantum computation to realise superior machine learning algorithmshas been a major research focus of recent years, but the prospects of quantum machine learning (QML) remain dampened by considerable technical challenges. A pa
Externí odkaz:
http://arxiv.org/abs/2311.05873
Autor:
Kumar, Niraj, Heredge, Jamie, Li, Changhao, Eloul, Shaltiel, Sureshbabu, Shree Hari, Pistoia, Marco
Federated learning has emerged as a viable distributed solution to train machine learning models without the actual need to share data with the central aggregator. However, standard neural network-based federated learning models have been shown to be
Externí odkaz:
http://arxiv.org/abs/2309.13002
Autor:
West, Maxwell T., Nakhl, Azar C., Heredge, Jamie, Creevey, Floyd M., Hollenberg, Lloyd C. L., Sevior, Martin, Usman, Muhammad
Publikováno v:
Intell. Comput. 3, 0100, (2024)
Quantum machine learning (QML) is emerging as an application of quantum computing with the potential to deliver quantum advantage, but its realisation for practical applications remains impeded by challenges. Amongst those, a key barrier is the compu
Externí odkaz:
http://arxiv.org/abs/2309.09424
Autor:
Fontana, Enrico, Herman, Dylan, Chakrabarti, Shouvanik, Kumar, Niraj, Yalovetzky, Romina, Heredge, Jamie, Sureshbabu, Shree Hari, Pistoia, Marco
Publikováno v:
Nat Commun 15, 7171 (2024)
Using tools from the representation theory of compact Lie groups, we formulate a theory of Barren Plateaus (BPs) for parameterized quantum circuits whose observables lie in their dynamical Lie algebra (DLA), a setting that we term Lie algebra Support
Externí odkaz:
http://arxiv.org/abs/2309.07902
Publikováno v:
Quantum Machine Intelligence (2024)
Quantum Computing offers a potentially powerful new method for performing Machine Learning. However, several Quantum Machine Learning techniques have been shown to exhibit poor generalisation as the number of qubits increases. We address this issue b
Externí odkaz:
http://arxiv.org/abs/2304.03601
Quantum computers have the potential to speed up certain computational tasks. A possibility this opens up within the field of machine learning is the use of quantum techniques that may be inefficient to simulate classically but could provide superior
Externí odkaz:
http://arxiv.org/abs/2103.12257
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