Quantum Annealing and Analog Quantum Computation

By Arnab Das, Bikas K. Chakrabarti. 2008

617 citations recorded by the source corpus.

We review here the recent success in quantum annealing, i.e., optimization of the cost or energy functions of complex systems utilizing quantum fluctuations. The concept is introduced in successive steps through the studies of mapping of such computationally hard problems to the classical spin glass problems. The quantum spin glass problems arise with the introduction of quantum fluctuations, and the annealing behavior of the systems as these fluctuations are reduced slowly to zero. This provides a general framework for realizing analog quantum computation.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/6c1b70b494f156521c4e157cb43d11b36e217548

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In Artificial Intelligence

In Atomic and Molecular Physics, and Optics

Citations in the corpus

Quantum annealing for industry applications: introduction and review (2021, neutral): “An approach closely related to AQC is QA [44, 81, 122, 126, 172], proposed in 1988 by Apolloni et al [10].”
Observing the Space- and Time-Dependent Growth of Correlations in Dynamically Tuned Synthetic Ising Models with Antiferromagnetic Interactions (2017, background): “In order for this approach to succeed, the duration of the sweep, which scales as the inverse of the square of the energy gap Δ above the instantaneous ground states [38], should be smaller than the coherence time of the system.”
Engineered dissipation for quantum information science (2022, methodology): “These same tools are also useful for annealing-based computational strategies 179 .”
Ising formulations of many NP problems (2013, background): “QUANTUM ADIABATIC OPTIMIZATION Recently, there has been much interest in the possibility of using adiabatic quantum optimization (AQO) to solve NP-complete and NP-hard problems [1, 2] 1.”
Evidence for quantum annealing with more than one hundred qubits (2013, neutral): “[8], and “Simulated Quantum Annealing” versus “Quantum Annealing” 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”
Qudits and High-Dimensional Quantum Computing (2020, background): “The advantage of adiabatic quantum computing is evident in its natural correspondence to quantizing satisfiability problems [55]; and current efforts to exploit adiabatic quantum computing focus on quantum annealing, which is a quantum generalization of the simulated annealing metaheuristic used for”
Combinatorial optimization by simulating adiabatic bifurcations in nonlinear Hamiltonian systems (2019, methodology): “These machines have been developed by various approaches: quantum computers based on quantum annealing (8, 9) or quantum adiabatic optimization (10–12) implemented with superconducting circuits (13, 14), coherent Isingmachines (CIMs) implemented with laser pulses (15–20), and Ising machines based on”
Minor-embedding in adiabatic quantum computation: II. Minor-universal graph design (2010, background): “See [ 8 ] for the closed related quantum annealing, and [19] for the recently introduced general Hamiltonian interpolation for AQO.”
Adiabatic Quantum Computing (2016, background): “This agrees with the intuition that a StoqAQC advantage over SA is associated with tall and thin barriers (Das and Chakrabarti, 2008; Ray 41 et al., 1989).”