Joseph Dulny
Research Subjects
Research Interests
Joseph Dulny has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Casey Tomlin
Institute for Gravitation and the Cosmos, Pennsylvania State University, University Park, Pennsylvania 16802-6300, USA
1 shared publication - Gideon Bass
Booz Allen Hamilton, Washington, USA
1 shared publication - Pete Rihaczek
Booz Allen Hamilton 901 15th Street Washington DC, 20005
1 shared publication - Vaibhaw Kumar
Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
1 shared publication
Latest Publications
- 7 March 2018
- Published by IOP Publishing in Quantum Science and Technology
- Vol. 3 (2) , 024010
Heterogeneous quantum computing for satellite constellation optimization: solving the weighted k -clique problem
NP-hard optimization problems scale very rapidly with problem size, becoming unsolvable with brute force methods, even with supercomputing resources. Typically, such problems have been approximated with heuristics. However, these methods still take a long time and are not guaranteed to find an optimal solution. Quantum computing offers the possibility of producing significant speed-up and improved solution quality. Current quantum annealing (QA) devices are designed to solve difficult optimization problems, but they are limited by hardware size and qubit connectivity restrictions. We present a novel heterogeneous computing stack that combines QA and classical machine learning, allowing the use of QA on problems larger than the hardware limits of the quantum device. These results represent experiments on a real-world problem represented by the weighted k -clique problem. Through this experiment, we provide insight into the state of quantum machine learning.
- 7 March 2018
- Published by IOP Publishing in Quantum Science and Technology
- Vol. 3 (2) , 024010
Heterogeneous quantum computing for satellite constellation optimization: solving the weighted k -clique problem
NP-hard optimization problems scale very rapidly with problem size, becoming unsolvable with brute force methods, even with supercomputing resources. Typically, such problems have been approximated with heuristics. However, these methods still take a long time and are not guaranteed to find an optimal solution. Quantum computing offers the possibility of producing significant speed-up and improved solution quality. Current quantum annealing (QA) devices are designed to solve difficult optimization problems, but they are limited by hardware size and qubit connectivity restrictions. We present a novel heterogeneous computing stack that combines QA and classical machine learning, allowing the use of QA on problems larger than the hardware limits of the quantum device. These results represent experiments on a real-world problem represented by the weighted k -clique problem. Through this experiment, we provide insight into the state of quantum machine learning.