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One research paper could help make the algorithms that used on DWave's adiabatic quantum annealing system faster.
Arxiv – Diff erent Strategies for Optimization Using the Quantum Adiabatic Algorithm
We present the results of a numerical study, with 20 qubits, of the performance of the Quantum Adiabatic Algorithm on randomly generated instances of MAX 2-SAT with a unique assignment that maximizes the number of satis ed clauses. The probability of obtaining this assignment at the end of the quantum evolution measures the success of the algorithm. Here we report three strategies which consistently increase the success probability for the hardest instances in our ensemble: decreasing the overall evolution time, initializing the system in excited states, and adding a random local Hamiltonian to the middle of the evolution
Dwave implements the Ising model. A researcher has created Ising formulations of many NP problems.
Arxiv – Ising formulations of many NP problems
Andrew Lucas, Harvard, provides Ising formulations for many NP-complete and NP-hard problems, including all of Karp’s 21 NP-complete problems. This collects and extends mappings to the Ising model from partitioning, covering and satisfiability. In each case, the required number of spins is at most cubic in the size of the problem. This work may be useful in designing adiabatic quantum optimization algorithms.
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