Degeneracy engineering for classical and quantum annealing: A case study of sparse linear regression in collider physics
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Classical and quantum annealing are computing paradigms that have been proposed to solve a wide range of optimization problems. In this paper, we aim to enhance the performance of annealing algorithms by introducing the technique of degeneracy engineering, through which the relative degeneracy of the ground state is increased by modifying a subset of terms in the objective Hamiltonian. We illustrate this novel approach by applying it to the example of ℓ0-norm regularization for sparse linear regression, which is, in general, an NP-hard optimization problem. Specifically, we show how to cast ℓ0-norm regularization as a quadratic unconstrained binary optimization (QUBO) problem, suitable for implementation on annealing platforms. As a case study, we apply this QUBO formulation to energy flow polynomials in high-energy collider physics, finding that degeneracy engineering substantially improves the annealing performance. Furthermore, our results motivate the application of degeneracy engineering to a variety of regularized optimization problems.
- Research Organization:
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP); National Science Foundation (NSF)
- Grant/Contract Number:
- SC0012567; SC0011090; PHY-2019786; SC0021006; SC0012704
- OSTI ID:
- 1907990
- Alternate ID(s):
- OSTI ID: 1909587
- Journal Information:
- Physical Review. D., Vol. 106, Issue 5; ISSN 2470-0010
- Publisher:
- American Physical Society (APS)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
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