Design and Implementation of Quantum Optimization Methods
Abstract
The project explores the applications of quantum computing ideas to optimization problems in machine learning related to partially observable Markov decision process. The problem of approximating the optimal policy for the expected discounted reward in an infinite horizon is proved to be decidable for quantum observable Markov decision process. This provides a relevant example where the quantum optimization problem is tractable while other related problems are known to be undecidable. A generalization of this quantum Markov model has potential applications for quantum control problems under noisy communication channels. The project also includes an educational component to develop a quantum computing course and a software development component to develop a prototype for a graphical quantum circuit simulator.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 2020
- Accession Number
- AD1091449
Entities
People
- Christino Tamon
Organizations
- Clarkson University