Imposing Constraints on Driver Hamiltonians and Mixing Operators: From Theory to Practical Implementation

• URL: http://arxiv.org/abs/2407.01975v1
• Date: Tue, 2 Jul 2024 06:23:02 GMT
• Title: Imposing Constraints on Driver Hamiltonians and Mixing Operators: From Theory to Practical Implementation
• Authors: Hannes Leipold, Federico M. Spedalieri, Stuart Hadfield, Eleanor Rieffel,
• Abstract summary: We give general expressions for finding Hamiltonian terms that satisfy constraint subsets and use these to give complexity characterizations of the related problems. We then give algorithmic procedures to turn these primitives into Hamiltonian drivers and unitary mixers that can be used for Constrained Quantum Anneal Operator Ansatz (CQA) and Quantum Alternating Operator Ansatz (QAOA) We empirically benchmark these approaches on instances sized between 12 and 22, showing the best relative performance for the tailored ansaetze and that exponential curve fits on the results are consistent with a quadratic speedup by utilizing alternative ansaetze to the
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Constructing Driver Hamiltonians and Mixing Operators such that they satisfy constraints is an important ansatz construction for quantum algorithms. We give general algebraic expressions for finding Hamiltonian terms and analogously unitary primitives, that satisfy constraint embeddings and use these to give complexity characterizations of the related problems. Finding operators that enforce classical constraints is proven to be NP-Complete in the general case; algorithmic procedures with worse-case polynomial runtime to find any operators with a constant locality bound, applicable for many constraints. We then give algorithmic procedures to turn these algebraic primitives into Hamiltonian drivers and unitary mixers that can be used for Constrained Quantum Annealing (CQA) and Quantum Alternating Operator Ansatz (QAOA) constructions by tackling practical problems related to finding an appropriate set of reduced generators and defining corresponding drivers and mixers accordingly. We then apply these concepts to the construction of ansaetze for 1-in-3 SAT instances. We consider the ordinary x-mixer QAOA, a novel QAOA approach based on the maximally disjoint subset, and a QAOA approach based on the disjoint subset as well as higher order constraint satisfaction terms. We empirically benchmark these approaches on instances sized between 12 and 22, showing the best relative performance for the tailored ansaetze and that exponential curve fits on the results are consistent with a quadratic speedup by utilizing alternative ansaetze to the x-mixer. We provide very general algorithmic prescriptions for finding driver or mixing terms that satisfy embedded constraints that can be utilized to probe quantum speedups for constraints problems with linear, quadratic, or even higher order polynomial constraints.

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