Quantum annealing showing the exponentially small success probability despite a constant energy gap

• URL: http://arxiv.org/abs/2212.09995v1
• Date: Tue, 20 Dec 2022 04:43:40 GMT
• Title: Quantum annealing showing the exponentially small success probability despite a constant energy gap
• Authors: Hiroshi Hayasaka, Takashi Imoto, Yuichiro Matsuzaki, Shiro Kawabata
• Abstract summary: Adiabatic condition consists of two parts: an energy gap and a transition matrix. It is believed that the computational time mainly depends on the energy gap during QA. In our formalism, we choose a known model exhibiting an exponentially small energy gap during QA, and we modify the model by adding a specific penalty term to the Hamiltonian.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum annealing (QA) is one of the methods to solve combinatorial optimization problems. We can estimate a computational time of QA by using the so-called adiabatic condition derived from the adiabatic theorem. The adiabatic condition consists of two parts: an energy gap and a transition matrix. It is believed that the computational time mainly depends on the energy gap during QA and is inversely proportional to a polynomial of the minimal energy gap. In this paper, we challenge this common wisdom. We propose a general method to construct counterintuitive models with a constant energy gap during QA where QA with a constant annealing time fails despite a constant energy gap. In our formalism, we choose a known model exhibiting an exponentially small energy gap during QA, and we modify the model by adding a specific penalty term to the Hamiltonian. In the modified model, the transition matrix in the adiabatic condition becomes exponentially large with the number of qubits, while the energy gap remains constant. As concrete examples we consider the adiabatic Grover search and the ferromagnetic p-spin model. In these cases, by adding the penalty term, the success probability of QA in the modified models become exponentially small despite a constant energy gap. Our results paves a way for better understanding of the QA performance.

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