SOAR: an architecture for general intelligence
Artificial Intelligence
A fast quantum mechanical algorithm for database search
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Quantum vs. classical communication and computation
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
A framework for structured quantum search
PhysComp96 Proceedings of the fourth workshop on Physics and computation
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Introduction to algorithms
Quantum lower bounds by polynomials
Journal of the ACM (JACM)
Introduction to the Theory of Computation
Introduction to the Theory of Computation
Behind Deep Blue: Building the Computer that Defeated the World Chess Champion
Behind Deep Blue: Building the Computer that Defeated the World Chess Champion
Exponential algorithmic speedup by a quantum walk
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Quantum Walk on the Line
An Introduction to Quantum Computing
An Introduction to Quantum Computing
Any AND-OR Formula of Size N can be Evaluated in time N^{1/2 + o(1)} on a Quantum Computer
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Quantum Algorithms for Evaluating Min-Max Trees
Theory of Quantum Computation, Communication, and Cryptography
Tree search and quantum computation
Quantum Information Processing
How significant are the known collision and element distinctness quantum algorithms
Quantum Information & Computation
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Graph search represents a cornerstone in computer science and is employed when the best algorithmic solution to a problem consists in performing an analysis of a search space representing computational possibilities. Typically, in such problems it is crucial to determine the sequence of transitions performed that led to certain states. In this work we discuss how to adapt generic quantum search procedures, namely quantum random walks and Grover's algorithm, in order to obtain computational paths. We then compare these approaches in the context of tree graphs. In addition we demonstrate that in a best-case scenario both approaches differ, performance-wise, by a constant factor speedup of two, whilst still providing a quadratic speedup relatively to their classical equivalents. We discuss the different scenarios that are better suited for each approach.