Circumventing superexponential runtimes for hard instances of quantum adiabatic optimization

• URL: http://arxiv.org/abs/2306.13131v1
• Date: Thu, 22 Jun 2023 18:00:02 GMT
• Title: Circumventing superexponential runtimes for hard instances of quantum adiabatic optimization
• Authors: Benjamin F. Schiffer, Dominik S. Wild, Nishad Maskara, Madelyn Cain, Mikhail D. Lukin, Rhine Samajdar
• Abstract summary: We present a general construction of instances of the problem for which the minimum gap decays superexponentially with system size. The small gap arises from locally independent choices, which cause the system to initially evolve and localize into a configuration far from the solution. We show that quantum quenches in these models can exhibit signatures of quantum many-body scars, which in turn, can circumvent the superexponential gaps.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Classical optimization problems can be solved by adiabatically preparing the ground state of a quantum Hamiltonian that encodes the problem. The performance of this approach is determined by the smallest gap encountered during the evolution. Here, we consider the maximum independent set problem, which can be efficiently encoded in the Hamiltonian describing a Rydberg atom array. We present a general construction of instances of the problem for which the minimum gap decays superexponentially with system size, implying a superexponentially large time to solution via adiabatic evolution. The small gap arises from locally independent choices, which cause the system to initially evolve and localize into a configuration far from the solution in terms of Hamming distance. We investigate remedies to this problem. Specifically, we show that quantum quenches in these models can exhibit signatures of quantum many-body scars, which in turn, can circumvent the superexponential gaps. By quenching from a suboptimal configuration, states with a larger ground state overlap can be prepared, illustrating the utility of quantum quenches as an algorithmic tool.

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