Faster quantum and classical SDP approximations for quadratic binary optimization
APA
Kueng, R. (2019). Faster quantum and classical SDP approximations for quadratic binary optimization. Perimeter Institute. https://pirsa.org/19100088
MLA
Kueng, Richard. Faster quantum and classical SDP approximations for quadratic binary optimization. Perimeter Institute, Oct. 28, 2019, https://pirsa.org/19100088
BibTex
@misc{ pirsa_PIRSA:19100088, doi = {10.48660/19100088}, url = {https://pirsa.org/19100088}, author = {Kueng, Richard}, keywords = {Other}, language = {en}, title = {Faster quantum and classical SDP approximations for quadratic binary optimization}, publisher = {Perimeter Institute}, year = {2019}, month = {oct}, note = {PIRSA:19100088 see, \url{https://pirsa.org}} }
We give a quantum speedup for solving the canonical semidefinite programming relaxation for binary quadratic optimization. The class of relaxations for combinatorial optimization has so far eluded quantum speedups. Our methods combine ideas from quantum Gibbs sampling and matrix exponent updates. A de-quantization of the algorithm also leads to a faster classical solver. For generic instances, our quantum solver gives a nearly quadratic speedup over state-of-the-art algorithms.
This is joint work with Fernando Brandao (Caltech) and Daniel Stilck Franca (QMATH, Copenhagen).