Understanding domain-wall encoding theoretically and experimentally

• URL: http://arxiv.org/abs/2108.12004v4
• Date: Wed, 14 Sep 2022 10:07:40 GMT
• Title: Understanding domain-wall encoding theoretically and experimentally
• Abstract summary: We analyze the method of encoding pairwise interactions of higher-than-binary discrete variables into binary variables based on domain walls on one dimensional Ising chains. We show that for problems of practical interest for quantum computing it is not possible to have a more efficient general encoding in terms of number of binary variables per discrete variable.
• Score: 1.2891210250935146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We analyze the method of encoding pairwise interactions of higher-than-binary discrete variables (these models are sometimes referred to as discrete quadratic models) into binary variables based on domain walls on one dimensional Ising chains. We discuss how this is relevant to quantum annealing, but also many gate model algorithms such as VQE and QAOA. We theoretically show that for problems of practical interest for quantum computing and assuming only quadratic interactions are available between the binary variables, it is not possible to have a more efficient general encoding in terms of number of binary variables per discrete variable. We furthermore use a D-Wave Advantage 1.1 flux qubit quantum annealing computer to show that the dynamics effectively freeze later for a domain-wall encoding compared to a traditional one-hot encoding. This second result could help explain the dramatic performance improvement of domain wall over one hot which has been seen in a recent experiment on D-Wave hardware. This is an important result because usually problem encoding and the underlying physics are considered separately, our work suggests that considering them together may be a more useful paradigm. We argue that this experimental result is also likely to carry over to a number of other settings, we discuss how this has implications for gate-model and quantum-inspired algorithms.

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