Physics – Quantum Physics
Scientific paper
2008-04-28
Physics
Quantum Physics
7 pages, 3 figures
Scientific paper
Many artificial intelligence (AI) problems naturally map to NP-hard optimization problems. This has the interesting consequence that enabling human-level capability in machines often requires systems that can handle formally intractable problems. This issue can sometimes (but possibly not always) be resolved by building special-purpose heuristic algorithms, tailored to the problem in question. Because of the continued difficulties in automating certain tasks that are natural for humans, there remains a strong motivation for AI researchers to investigate and apply new algorithms and techniques to hard AI problems. Recently a novel class of relevant algorithms that require quantum mechanical hardware have been proposed. These algorithms, referred to as quantum adiabatic algorithms, represent a new approach to designing both complete and heuristic solvers for NP-hard optimization problems. In this work we describe how to formulate image recognition, which is a canonical NP-hard AI problem, as a Quadratic Unconstrained Binary Optimization (QUBO) problem. The QUBO format corresponds to the input format required for D-Wave superconducting adiabatic quantum computing (AQC) processors.
Macready William G.
Neven Hartmut
Rose Geordie
No associations
LandOfFree
Image recognition with an adiabatic quantum computer I. Mapping to quadratic unconstrained binary optimization does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Image recognition with an adiabatic quantum computer I. Mapping to quadratic unconstrained binary optimization, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Image recognition with an adiabatic quantum computer I. Mapping to quadratic unconstrained binary optimization will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-692262