A Quantum Algorithm for the Hamiltonian NAND Tree
By Edward Farhi, Jeffrey Goldstone, Sam Gutmann. Theory of Computing, 2007
299 citations recorded by the source corpus.
We give a quantum algorithm for the binary NAND tree problem in the Hamil- tonian oracle model. The algorithm uses a continuous time quantum walk with a running time proportional to p N. We also show a lower bound of W( p N) for the NAND tree problem in the Hamiltonian oracle model.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/6acaf5610ab9080932a70908daf6aecfcce2b91e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Andrew M. Childs
- Salvador Elías Venegas-Andraca
- Nathan Wiebe
- Dominic W. Berry
- Robin Kothari
- Guang Hao Low
- Rolando D. Somma
- Miklos Santha
In Computational Theory and Mathematics
- Andrew M. Childs
- Salvador Elías Venegas-Andraca
- Rolando D. Somma
- Miklos Santha
- Robin Kothari
- Dominic W. Berry
- Vedran Dunjko
- Hans J. Briegel
Citations in the corpus
- Search via quantum walk (2006, background): “In a break-through, Farhi, Goldstone, and Gutmann [20] designed an O( √ n) continuous-time quantum walk algorithm for balanced formulae.”
- Quantum walks: a comprehensive review (2012, methodology): “Moreover, Farhi et al. [146] have produced a O( √ N ) quantum algorithm for solving the NAND tree problem (which consists of evaluating the root node of a perfectly bifurcating tree whose N leaves are either ‘0’ or ‘1’ and the value of any other node is the NAND of corresponding children leaves) and”
- Hamiltonian Simulation with Nearly Optimal Dependence on all Parameters (2015, methodology): “For example, it was used to convert the algorithm for evaluating a balanced binary NAND tree with n leaves [13] to the discrete-query model [10].”
- Exponential improvement in precision for simulating sparse Hamiltonians (2013, background): “[19] E. Farhi and S. Gutmann.”
- Universal Computation by Multiparticle Quantum Walk (2012, methodology): “Notable previous applications of such scattering include the quantum algorithm for the Hamiltonian NAND tree [8] and the single-particle universality construction [5].”
- Machine learning \& artificial intelligence in the quantum domain (2017, methodology): “, 2014), algorithms for quadratic and super-polynomial improvements in NANDand boolean-tree evaluations, which are important in evaluation of optimal strategies in two-player games 130 (Childs et al., 2009; Zhan et al., 2012; Farhi et al., 2008).”
- On the Relationship Between Continuous- and Discrete-Time Quantum Walk (2008, methodology): “Finally, I discuss limitations of the method for simulating Hamiltonians with negative matrix elements, and present two problems that motivate attempting to circumvent these limitations.”