41,522 research outputs found
Adiabatic optimization without local minima
Several previous works have investigated the circumstances under which
quantum adiabatic optimization algorithms can tunnel out of local energy minima
that trap simulated annealing or other classical local search algorithms. Here
we investigate the even more basic question of whether adiabatic optimization
algorithms always succeed in polynomial time for trivial optimization problems
in which there are no local energy minima other than the global minimum.
Surprisingly, we find a counterexample in which the potential is a single basin
on a graph, but the eigenvalue gap is exponentially small as a function of the
number of vertices. In this counterexample, the ground state wavefunction
consists of two "lobes" separated by a region of exponentially small amplitude.
Conversely, we prove if the ground state wavefunction is single-peaked then the
eigenvalue gap scales at worst as one over the square of the number of
vertices.Comment: 20 pages, 1 figure. Journal versio
Parallel Deterministic and Stochastic Global Minimization of Functions with Very Many Minima
The optimization of three problems with high dimensionality and many local minima are investigated
under five different optimization algorithms: DIRECT, simulated annealing, Spallâs SPSA algorithm, the KNITRO
package, and QNSTOP, a new algorithm developed at Indiana University
What is the Computational Value of Finite Range Tunneling?
Quantum annealing (QA) has been proposed as a quantum enhanced optimization
heuristic exploiting tunneling. Here, we demonstrate how finite range tunneling
can provide considerable computational advantage. For a crafted problem
designed to have tall and narrow energy barriers separating local minima, the
D-Wave 2X quantum annealer achieves significant runtime advantages relative to
Simulated Annealing (SA). For instances with 945 variables, this results in a
time-to-99%-success-probability that is times faster than SA
running on a single processor core. We also compared physical QA with Quantum
Monte Carlo (QMC), an algorithm that emulates quantum tunneling on classical
processors. We observe a substantial constant overhead against physical QA:
D-Wave 2X again runs up to times faster than an optimized
implementation of QMC on a single core. We note that there exist heuristic
classical algorithms that can solve most instances of Chimera structured
problems in a timescale comparable to the D-Wave 2X. However, we believe that
such solvers will become ineffective for the next generation of annealers
currently being designed. To investigate whether finite range tunneling will
also confer an advantage for problems of practical interest, we conduct
numerical studies on binary optimization problems that cannot yet be
represented on quantum hardware. For random instances of the number
partitioning problem, we find numerically that QMC, as well as other algorithms
designed to simulate QA, scale better than SA. We discuss the implications of
these findings for the design of next generation quantum annealers.Comment: 17 pages, 13 figures. Edited for clarity, in part in response to
comments. Added link to benchmark instance
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