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pro vyhledávání: '"Oberoi, Jaspreet S."'
Autor:
Vedaie, Seyed Shakib, Noori, Moslem, Oberoi, Jaspreet S., Sanders, Barry C., Zahedinejad, Ehsan
Kernel methods play an important role in machine learning applications due to their conceptual simplicity and superior performance on numerous machine learning tasks. Expressivity of a machine learning model, referring to the ability of the model to
Externí odkaz:
http://arxiv.org/abs/2011.09694
Autor:
Noori, Moslem, Vedaie, Seyed Shakib, Singh, Inderpreet, Crawford, Daniel, Oberoi, Jaspreet S., Sanders, Barry C., Zahedinejad, Ehsan
Publikováno v:
Phys. Rev. Applied 14, 034034 (2020)
Quantum information processing is likely to have far-reaching impact in the field of artificial intelligence. While the race to build an error-corrected quantum computer is ongoing, noisy, intermediate-scale quantum (NISQ) devices provide an immediat
Externí odkaz:
http://arxiv.org/abs/1909.08131
Kernel methods are used extensively in classical machine learning, especially in the field of pattern analysis. In this paper, we propose a kernel-based quantum machine learning algorithm that can be implemented on a near-term, intermediate scale qua
Externí odkaz:
http://arxiv.org/abs/1905.01390
Clustering, or grouping, dataset elements based on similarity can be used not only to classify a dataset into a few categories, but also to approximate it by a relatively large number of representative elements. In the latter scenario, referred to as
Externí odkaz:
http://arxiv.org/abs/1903.08256
Signed graphs serve as a primary tool for modelling social networks. They can represent relationships between individuals (i.e., nodes) with the use of signed edges. Finding communities in a signed graph is of great importance in many areas, for exam
Externí odkaz:
http://arxiv.org/abs/1901.04873
Autor:
Levit, Anna, Crawford, Daniel, Ghadermarzy, Navid, Oberoi, Jaspreet S., Zahedinejad, Ehsan, Ronagh, Pooya
Recent theoretical and experimental results suggest the possibility of using current and near-future quantum hardware in challenging sampling tasks. In this paper, we introduce free energy-based reinforcement learning (FERL) as an application of quan
Externí odkaz:
http://arxiv.org/abs/1706.00074
Publikováno v:
Quantum Information & Computation, Volume 18 (1-2), pp. 0051-0074 (2018)
We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks. We associate a transverse field Ising spin Hamiltonian with a layout of qubits similar to that of a deep Boltzmann m
Externí odkaz:
http://arxiv.org/abs/1612.05695
Publikováno v:
Quantum Information and Computation. 18:51-74
We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks. We associate a transverse field Ising spin Hamiltonian with a layout of qubits similar to that of a deep Boltzmann m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::54a4047e54975b2f732689d9d4929925
https://doi.org/10.26421/qic18.1-2-3
https://doi.org/10.26421/qic18.1-2-3
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