Optimization using quantum mechanics: quantum annealing through adiabatic evolution
By G. Santoro, E. Tosatti. 2006
326 citations recorded by the source corpus.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/dbf063ec3a27a4df25db216610937839d261dcb0
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In Artificial Intelligence
- Daniel A. Lidar
- Peter Leonard McMahon
- Ryan Hamerly
- Hiroki Takesue
- Toshimori Honjo
- Göran Wendin
- Dirk Englund
- Ken-ichi Kawarabayashi
Citations in the corpus
- Evidence for quantum annealing with more than one hundred qubits (2013, background): “[7, 9], “Quantum Monte Carlo Annealing” versus “QA with real time adiabatic evolution” in Ref.”
- Quantum Chaos and Thermalization in Isolated Systems of Interacting Particles (2016, background): “the limitation to short times of the time-dependent DMRG met hod [205]; the manipulation of the dynamics of interacting quantum systems [206, 191, 207] that emerged from the need to transfer and store information for quantum communication; and the further advances of quantum simulat ors for adiabati”
- Experimental investigation of performance differences between coherent Ising machines and a quantum annealer (2018, background): “gital CMOS annealers [6, 7], as well as analog devices such as nano-magnet arrays [8], electronic oscillators [9, 10] and laser networks [11]. Quantum adiabatic computation [12] and quantum annealing [13, 14, 15, 16] are also prominent examples, and may oer the possibility of quantum speedup [17, 18”