5. Construction of Energy Functions for Lattice Heteropolymer Models: Efficient Encodings for Constraint Satisfaction Programming and Quantum Annealing
- Stuart A. Rice and
- Aaron R. Dinner
Published Online: 4 APR 2014
DOI: 10.1002/9781118755815.ch05
Copyright 2014 by John Wiley & Sons, Inc. All rights reserved.
Book Title

Advances in Chemical Physics: Volume 155
Additional Information
How to Cite
Babbush, R., Perdomo-Ortiz, A., O'Gorman, B., Macready, W. and Aspuru-Guzik, A. (2014) Construction of Energy Functions for Lattice Heteropolymer Models: Efficient Encodings for Constraint Satisfaction Programming and Quantum Annealing, in Advances in Chemical Physics: Volume 155 (eds S. A. Rice and A. R. Dinner), John Wiley & Sons, Inc., Hoboken, New Jersey. doi: 10.1002/9781118755815.ch05
Editor Information
Department of Chemistry and The James Franck Institute, The University of Chicago, Chicago, Illinois
Publication History
- Published Online: 4 APR 2014
- Published Print: 11 APR 2014
Book Series:
ISBN Information
Print ISBN: 9781118755778
Online ISBN: 9781118755815
- Summary
- Chapter
- References
Keywords:
- constraint satisfaction programming;
- integer'linear programming;
- lattice heteropolymer models;
- locality reductions;
- pseudo'Boolean function;
- quadratic unconstrained binary optimization (QUBO)
Summary
This chapter presents a general construction of the free energy function for the two-dimensional lattice heteropolymer model widely used to study the dynamics of proteins. It demonstrates how to map the lattice heteropolymer problem into forms that can be solved by using different types of technology and algorithms. The embedding strategies presented here apply to many discrete optimization problems. Mapping these problems to a constraint programming problem is a three-step process. The chapter provides a brief description of the process and expands upon each step as it applies to lattice folding. It explains the “circuit” construction that provides optimal efficiency at the cost of introducing high-ordered many-body terms. Finally, the chapter demonstrates the final steps required to embed a small instance of a particular lattice protein problem into a quadratic unconstrained binary optimization (QUBO) Hamiltonian.