Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Thomas E O'Brien"'
Publikováno v:
New Journal of Physics, Vol 21, Iss 2, p 023022 (2019)
Quantum phase estimation (QPE) is the workhorse behind any quantum algorithm and a promising method for determining ground state energies of strongly correlated quantum systems. Low-cost QPE techniques make use of circuits which only use a single anc
Externí odkaz:
https://doaj.org/article/0900f20a1cff469789b6b48f427a09aa
Publikováno v:
Quantum, Vol 8, p 1537 (2024)
We study the problem of learning the Hamiltonian of a many-body quantum system from experimental data. We show that the rate of learning depends on the amount of control available during the experiment. We consider three control models: one where tim
Externí odkaz:
https://doaj.org/article/0544dd0a16f94259a7afb1541d8f315b
Autor:
Stasja Stanisic, Jan Lukas Bosse, Filippo Maria Gambetta, Raul A. Santos, Wojciech Mruczkiewicz, Thomas E. O’Brien, Eric Ostby, Ashley Montanaro
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-11 (2022)
The Fermi-Hubbard model represents one of the benchmarks for testing quantum computational methods for condensed matter. Here, the authors are able to reproduce qualitative properties of the model on 1 × 8 and 2 × 4 lattices, by running a VQE-based
Externí odkaz:
https://doaj.org/article/e10e12d04cb040bf9091f0a5dfa70b42
Simulating groundstate and dynamical quantum phase transitions on a superconducting quantum computer
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-9 (2022)
Strongly correlated condensed matter systems are among those for which quantum simulation should be able to give an advantage. Here, the authors use a translationally invariant tensor network technique to simulate a quantum critical system on a super
Externí odkaz:
https://doaj.org/article/1b4a3bdf4c9c4590a092d0bd656057f3
Publikováno v:
Quantum, Vol 6, p 830 (2022)
Quantum phase estimation is a cornerstone in quantum algorithm design, allowing for the inference of eigenvalues of exponentially-large sparse matrices.The maximum rate at which these eigenvalues may be learned, –known as the Heisenberg limit–, i
Externí odkaz:
https://doaj.org/article/1ceee52ca34e4036b352c69a4c78471f
Autor:
Thomas E. O’Brien, Lev B. Ioffe, Yuan Su, David Fushman, Hartmut Neven, Ryan Babbush, Vadim Smelyanskiy
Publikováno v:
PRX Quantum, Vol 3, Iss 3, p 030345 (2022)
We propose a quantum algorithm for inferring the molecular nuclear spin Hamiltonian from time-resolved measurements of spin-spin correlators, which can be obtained via nuclear magnetic resonance (NMR). We focus on learning the anisotropic dipolar ter
Externí odkaz:
https://doaj.org/article/04c264882a874556a8cb38bd24c832bb
Autor:
William J. Huggins, Sam McArdle, Thomas E. O’Brien, Joonho Lee, Nicholas C. Rubin, Sergio Boixo, K. Birgitta Whaley, Ryan Babbush, Jarrod R. McClean
Publikováno v:
Physical Review X, Vol 11, Iss 4, p 041036 (2021)
Contemporary quantum computers have relatively high levels of noise, making it difficult to use them to perform useful calculations, even with a large number of qubits. Quantum error correction is expected to eventually enable fault-tolerant quantum
Externí odkaz:
https://doaj.org/article/47f0aaf515cc48a5a32bfac53fdebaaf
Autor:
Thomas E. O’Brien, Stefano Polla, Nicholas C. Rubin, William J. Huggins, Sam McArdle, Sergio Boixo, Jarrod R. McClean, Ryan Babbush
Publikováno v:
PRX Quantum, Vol 2, Iss 2, p 020317 (2021)
The accumulation of noise in quantum computers is the dominant issue stymieing the push of quantum algorithms beyond their classical counterparts. We do not expect to be able to afford the overhead required for quantum error correction in the next de
Externí odkaz:
https://doaj.org/article/3f6c18736bfe4d4c8322a24f4c8983a7
Autor:
Xavier Bonet-Monroig, Hao Wang, Diederick Vermetten, Bruno Senjean, Charles Moussa, Thomas Bäck, Vedran Dunjko, Thomas E. O'Brien
Publikováno v:
Physical Review A, 107(3):032407
Variational quantum algorithms (VQAs) offer a promising path toward using near-term quantum hardware for applications in academic and industrial research. These algorithms aim to find approximate solutions to quantum problems by optimizing a parametr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c19d608b38e2814f0b1b30275e3e52e
http://hdl.handle.net/1887/3570441
http://hdl.handle.net/1887/3570441
Publikováno v:
Physical Review X, Vol 10, Iss 3, p 031064 (2020)
Many applications of quantum simulation require one to prepare and then characterize quantum states by efficiently estimating k-body reduced density matrices (k-RDMs), from which observables of interest may be obtained. For instance, the fermionic 2-
Externí odkaz:
https://doaj.org/article/556a9bb6081b44219382e683abbcf8a0