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ORCIDQuantum Machine Intelligence, Volume 1
Volume 1, Numbers 1-2, May 2019
- Giovanni Acampora
:
Quantum machine intelligence. 1-3 - L. O'Driscoll, Rosanna Nichols, Paul A. Knott:
A hybrid machine learning algorithm for designing quantum experiments. 5-15 - Davide Venturelli, Alexei Kondratyev:
Reverse quantum annealing approach to portfolio optimization problems. 17-30 - Thomas R. Bromley, Patrick Rebentrost:
Batched quantum state exponentiation and quantum Hebbian learning. 31-40 - Zhikuan Zhao, Alejandro Pozas-Kerstjens, Patrick Rebentrost, Peter Wittek:
Bayesian deep learning on a quantum computer. 41-51 - Yousef Younes, Ingo Schmitt:
On quantum implication. 53-63
Volume 1, Numbers 3-4, December 2019
- Riccardo Mengoni, Alessandra Di Pierro:
Kernel methods in Quantum Machine Learning. 65-71 - Serafeim P. Moustakidis, Eirini Christodoulou, Elpiniki Papageorgiou, Christos Kokkotis, Nikolaos I. Papandrianos, Dimitrios Tsaopoulos:
Application of machine intelligence for osteoarthritis classification: a classical implementation and a quantum perspective. 73-86 - D. V. Babukhin, A. A. Zhukov, W. V. Pogosov:
Nondestructive classification of quantum states using an algorithmic quantum computer. 87-96 - Akram Youssry, Ahmed El-Rafei, Ri-Gui Zhou:
A continuous-variable quantum-inspired algorithm for classical image segmentation. 97-111 - Giovanni Acampora, Vittorio Cataudella, Pratibha Raghupati Hegde, Procolo Lucignano, Gianluca Passarelli, Autilia Vitiello:
An evolutionary strategy for finding effective quantum 2-body Hamiltonians of p-body interacting systems. 113-122
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