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Comparing planning problem compilation approaches for quantum annealing

Published online by Cambridge University Press:  22 February 2017

Bryan O’Gorman ,
Eleanor Gilbert Rieffel ,
Minh Do ,
Davide Venturelli  and
Jeremy Frank
Bryan O’Gorman
Affiliation:
QuAIL, NASA Ames Research Center, Moffett Field, CA, USA; e-mail: [email protected], [email protected] Stinger Ghaffarian Technologies, Inc. 7701 Greenbelt Road, Suite 400 Greenbelt, MD 20770, USA; e-mail: [email protected]
Eleanor Gilbert Rieffel
Affiliation:
QuAIL, NASA Ames Research Center, Moffett Field, CA, USA; e-mail: [email protected], [email protected]
Minh Do
Affiliation:
Intelligent Systems Division, NASA Ames Research Center, Moffett Field, CA, USA; e-mail: [email protected] Stinger Ghaffarian Technologies, Inc. 7701 Greenbelt Road, Suite 400 Greenbelt, MD 20770, USA; e-mail: [email protected]
Davide Venturelli
Affiliation:
QuAIL, NASA Ames Research Center, Moffett Field, CA, USA; e-mail: [email protected], [email protected] Universities Space Research Association 615 National Avenue, Suite 220 Mountain View, CA 94043, USA; e-mail: [email protected]
Jeremy Frank
Affiliation:
Intelligent Systems Division, NASA Ames Research Center, Moffett Field, CA, USA; e-mail: [email protected]
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Abstract

One approach to solving planning problems is to compile them to other problems for which powerful off-the-shelf solvers are available; common targets include SAT, CSP, and MILP. Recently, a novel optimization technique has become available: quantum annealing (QA). QA takes as input problem instances of quadratic unconstrained binary optimization (QUBO) problem. Early quantum annealers are now available, though their constraints restrict the types of QUBOs they can take as input. Here, we introduce the planning community to the key steps in compiling planning problems to QA hardware: a hardware-independent step, mapping, and a hardware-dependent step, embedding. After describing two approaches to mapping general planning problems to QUBO, we describe preliminary results from running an early quantum annealer on a parametrized family of hard planning problems. The results show that different mappings can substantially affect performance, even when many features of the resulting instances are similar. We conclude with insights gained from this early study that suggest directions for future work.

Type
Articles
Information
The Knowledge Engineering Review , Volume 31 , Special Issue 5: Constraint Satisfaction for Planning and Scheduling , November 2016 , pp. 465 - 474
Copyright
© Cambridge University Press, 2017 

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