On training variational quantum circuits
ORAL
Abstract
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Publication: Quantum machine learning<br>J Biamonte, P Wittek, N Pancotti, P Rebentrost, N Wiebe, and S Lloyd <br>Nature 549, 195–202 (2017) 10.1038/nature23474<br><br>Ion-native variational ansatz for quantum approximate optimization<br>D Rabinovich, S Adhikary, E Campos, V Akshay, E Anikin, R Sengupta, O Lakhmanskaya, K Lakhmanskiy, and J Biamonte<br>Physical Review A 106, 032418 (2022) 10.1103/PhysRevA.106.032418<br><br>Progress towards analytically optimal angles in quantum approximate optimisation<br>D Rabinovich, R Sengupta, E Campos, V Akshay, and J Biamonte <br>Mathematics 10, 2601 (2022) 10.3390/math10152601<br><br>Reachability deficits implicit in quantum approximate optimization of graph problems<br>V Akshay, H Philathong, I Zacharov, and J Biamonte <br>Quantum 5, 532 (2021) 10.22331/q-2021-08-30-532<br><br>Parameter concentrations in quantum approximate optimization<br>V Akshay, D Rabinovich, E Campos, and J Biamonte <br>(Letter) Physical Review A 104, L010401 (2021) 10.1103/PhysRevA.104.L010401<br><br>Universal variational quantum computation<br>J Biamonte<br>(Letter) Physical Review A 103, L030401 (2021) 10.1103/PhysRevA.103.L030401<br><br>Quantum machine learning tensor network states<br>A Kardashin, A Uvarov, and J Biamonte <br>Frontiers in Physics 8, 586374 (2021) 10.3389/fphy.2020.586374<br><br>Variational simulation of Schwinger's Hamiltonian with polarization qubits<br>O Borzenkova, G Struchalin, A Kardashin, V Krasnikov, N Skryabin, S Straupe, S Kulik, and J Biamonte <br>Applied Physics Letters 118, 144002 (2021) 10.1063/5.0043322<br><br>Abrupt transitions in variational quantum circuit training<br>E Campos, A Nasrallah, and J Biamonte <br>Physical Review A 103, 032607 (2021) 10.1103/PhysRevA.103.032607<br><br>Training saturation in layerwise quantum approximate optimisation<br>E Campos, D Rabinovich, V Akshay, and J Biamonte<br>(Letter) Physical Review A 104, L030401 (2021) 10.1103/PhysRevA.104.L030401<br><br>On barren plateaus and cost function locality in variational quantum algorithms<br>A Uvarov and J Biamonte<br>Journal of Physics A: Mathematical and Theoretical 54, 245–301 (2021) 10.1088/1751-8121/abfac7<br><br>Reachability deficits in quantum approximate optimization<br>V Akshay, H Philathong, M Morales, and J Biamonte <br>Physical Review Letters 124, 090504 (2020) 10.1103/PhysRevLett.124.090504<br><br>On the universality of the quantum approximate optimization algorithm<br>M Morales, J Biamonte, and Z Zimborás <br>Quantum Information Processing 19, 291 (2020) 10.1007/s11128-020-02748-9<br><br>Variational quantum eigensolver for frustrated quantum systems<br>A Uvarov, J Biamonte, and D Yudin <br>Physical Review B 102, 075104 (2020) 10.1103/PhysRevB.102.075104<br><br>Machine learning phase transitions with a quantum processor<br>A Uvarov, A Kardashin, and J Biamonte <br>Physical Review A 102, 012415 (2020) 10.1103/PhysRevA.102.012415<br><br>Variational learning of Grover's quantum search algorithm<br>M Morales, T Tlyachev, and J Biamonte <br>Physical Review A 98, 062333 (2018) 10.1103/PhysRevA.98.062333
Presenters
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Jacob Biamonte
Beijing Institute of Mathematical Sciences and Applications
Authors
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Jacob Biamonte
Beijing Institute of Mathematical Sciences and Applications