Applications of Quantum Annealing in Statistics
arXiv:1904.06819
Abstract
Quantum computation offers exciting new possibilities for statistics. This paper explores the use of the D-Wave machine, a specialized type of quantum computer, which performs quantum annealing. A general description of quantum annealing through the use of the D-Wave is given, along with technical issues to be encountered. Quantum annealing is used to perform maximum likelihood estimation, generate an experimental design, and perform matrix inversion. Though the results show that quantum computing is still at an early stage which is not yet superior to classical computation, there is promise for quantum computation in the future.
arXiv commentCorrected a few typos, added a few references, clarified some points, added information about potential polynomial speedup of quantum annealing and effect of difference between ground and excited energy states
References in corpus (10)
- Quantum Computing in the NISQ era and beyond
- Defining and detecting quantum speedup
- A case study in programming a quantum annealer for hard operational planning problems
- Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning
- Quantum Optimization of Fully-Connected Spin Glasses
- Application of Quantum Annealing to Training of Deep Neural Networks
- Nonnegative/binary matrix factorization with a D-Wave quantum annealer
- A NASA Perspective on Quantum Computing: Opportunities and Challenges
- How "Quantum" is the D-Wave Machine?
- Temperature scaling law for quantum annealing optimizers