Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Danylo Lykov"'
Publikováno v:
npj Quantum Information, Vol 9, Iss 1, Pp 1-10 (2023)
Abstract We compare the performance of the Quantum Approximate Optimization Algorithm (QAOA) with state-of-the-art classical solvers Gurobi and MQLib to solve the MaxCut problem on 3-regular graphs. We identify the minimum noiseless sampling frequenc
Externí odkaz:
https://doaj.org/article/f2a8086740cc4051b75323a9f2db76b6
Publikováno v:
Frontiers in Quantum Science and Technology, Vol 2 (2023)
The quantum approximate optimization algorithm (QAOA) is one of the most promising candidates for achieving quantum advantage through quantum-enhanced combinatorial optimization. A near-optimal solution to the combinatorial optimization problem is ac
Externí odkaz:
https://doaj.org/article/7c9a0e032ab542bd956f64accc19dd2a
Publikováno v:
2022 IEEE/ACM Third International Workshop on Quantum Computing Software (QCS).
Noisy quantum simulation is challenging since one has to take into account the stochastic nature of the process. The dominating method for it is the density matrix approach. In this paper, we evaluate conditions for which this method is inferior to a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b7d564b90fd2bd3db73a50dabcf7058
https://doi.org/10.1109/qcs56647.2022.00018
https://doi.org/10.1109/qcs56647.2022.00018
Publikováno v:
2022 IEEE High Performance Extreme Computing Conference (HPEC).
One of the key problems in tensor network based quantum circuit simulation is the construction of a contraction tree which minimizes the cost of the simulation, where the cost can be expressed in the number of operations as a proxy for the simulation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::04968e82a9f074e067e0816bfcdd7831
https://doi.org/10.1109/hpec55821.2022.9926353
https://doi.org/10.1109/hpec55821.2022.9926353
Publikováno v:
2022 IEEE International Conference on Quantum Computing and Engineering (QCE).
Quantum embedding learning is an important step in the application of quantum machine learning to classical data. In this paper we propose a quantum few-shot embedding learning paradigm, which learns embeddings useful for training downstream quantum
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fda915ad8e39ab0e06b0961652d29abb
https://doi.org/10.1109/qce53715.2022.00026
https://doi.org/10.1109/qce53715.2022.00026
In this work, we compare the performance of the Quantum Approximate Optimization Algorithm (QAOA) with state-of-the-art classical solvers such as Gurobi and MQLib to solve the combinatorial optimization problem MaxCut on 3-regular graphs. The goal is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::79df400c684eec5f3d2c3e171bcbac3d
https://doi.org/10.21203/rs.3.rs-1721449/v1
https://doi.org/10.21203/rs.3.rs-1721449/v1
Autor:
M. R. Perelshtein, O. V. Misochko, N. S. Kirsanov, Danylo Lykov, M. V. Lebedev, Valerii M. Vinokur, Gordey B. Lesovik, V. V. Zemlyanov
Publikováno v:
Scientific Reports, Vol 10, Iss 1, Pp 1-8 (2020)
Scientific Reports
Scientific Reports
Unitary Fourier transform lies at the core of the multitudinous computational and metrological algorithms. Here we show experimentally how the unitary Fourier transform-based phase estimation protocol, used namely in quantum metrology, can be transla
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a4d0b2d0bc72a9bcc83ccc815012711c
http://link.springer.com/article/10.1038/s41598-020-65466-3
http://link.springer.com/article/10.1038/s41598-020-65466-3
Autor:
Danylo Lykov, Angela Chen, Huaxuan Chen, Kristopher Keipert, Zheng Zhang, Tom Gibbs, Yuri Alexeev
This work studies the porting and optimization of the tensor network simulator QTensor on GPUs, with the ultimate goal of simulating quantum circuits efficiently at scale on large GPU supercomputers. We implement NumPy, PyTorch, and CuPy backends and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1996d0dec674d85120bc2c91665a65de
Autor:
Jonathan Wurtz, Danylo Lykov
Publikováno v:
Physical Review A. 104
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b6a89eb2117bd003d23cfa5bd80df280
https://doi.org/10.1103/physreva.104.052419
https://doi.org/10.1103/physreva.104.052419
Publikováno v:
QCE
The Quantum approximate optimization algorithm (QAOA) is one of the most promising candidates for achieving quantum advantage through quantum-enhanced combinatorial optimization. In a typical QAOA setup, a set of quantum circuit parameters is optimiz
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::88c3d5de4634ec2a2a96f62aa9b2ed53
https://doi.org/10.1109/qce52317.2021.00034
https://doi.org/10.1109/qce52317.2021.00034