Graphical evolution: an introduction to the theory of random graphs
Graphical evolution: an introduction to the theory of random graphs
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
The hardest constraint problems: a double phase transition
Artificial Intelligence
Exploiting the deep structure of constraint problems
Artificial Intelligence
Phase transitions and the search problem
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Nuclear magnetic resonance spectroscopy: an experimentally accessible paradigm for quantum computing
PhysComp96 Proceedings of the fourth workshop on Physics and computation
A framework for structured quantum search
PhysComp96 Proceedings of the fourth workshop on Physics and computation
Feynman Lectures on Computation
Feynman Lectures on Computation
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Combinatorial Algorithms: For Computers and Hard Calculators
Combinatorial Algorithms: For Computers and Hard Calculators
Algorithms for quantum computation: discrete logarithms and factoring
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Quantum computing and phase transitions in combinatorial search
Journal of Artificial Intelligence Research
IEEE Intelligent Systems
A review of procedures to evolve quantum algorithms
Genetic Programming and Evolvable Machines
Evolving Hogg's quantum algorithm using linear-tree GP
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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A previously developed quantum search algorithm for solving 1-SAT problems in a single step is generalized to apply to a range of highly constrained k-SAT problems. We identify a bound on the number of clauses in satisfiahility problems for which the generalized algorithm can find a solution in a constant number of steps as the number of variables increases. This performance contrasts with the linear growth in the number of steps required by the best classical algorithms, and the exponential number required by classical and quantum methods that ignore the problem structure. In some cases, the algorithm can also guarantee that insoluble problems in fact have no solutions, unlike previously proposed quantum search algorithms.