Quantum algorithm for approximating partition functions

Physical Review A - Atomic, Molecular, and Optical Physics

Volumn 80, Issue 2, 2009, Pages

  Wocjan, Pawel ^a   Chiang, Chen Fu ^a   Nagaj, Daniel ^b,c   Abeyesinghe, Anura ^a  

^a University of Central Florida   (United States)

^b INSTITUTE OF PHYSICS   (Slovakia)

^c Quniverse   (Slovakia)

Author keywords

[No Author keywords available]

Indexed keywords

COOLING SCHEDULE; ENCODINGS; EXPECTED VALUES; FULLY POLYNOMIAL RANDOMIZED APPROXIMATION SCHEMES; MARKOV CHAIN; MONTE CARLO MARKOV CHAIN; PARAMETER CHARACTERIZING; PARTITION FUNCTIONS; PHASE ESTIMATION; QUANTUM ALGORITHMS; QUANTUM COUNTING; QUANTUM OBSERVABLES; QUANTUM WALK; SPECTRAL GAP; SPEED-UPS; STATIONARY DISTRIBUTION; TIME COMPLEXITY;

APPROXIMATION ALGORITHMS; MARKOV PROCESSES; POLYNOMIAL APPROXIMATION; QUANTUM THEORY;

SIMULATED ANNEALING;

EID: 69549116762     PISSN: 10502947     EISSN: 10941622     Source Type: Journal    
DOI: 10.1103/PhysRevA.80.022340     Document Type: Article

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17. This relationship is used, e.g., in the algorithm for approximating the permanent of a non-negative matrix-one can find the value of the permanent by counting the number of perfect matchings of a particular bipartite graph, which in turn is equal to the zero-temperature partition function of a certain spin system.
18. When the classical Markov chains can be implemented efficiently, each step of the corresponding quantum walks can also be applied efficiently and precisely, as shown, e.g.,.