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Understanding domain-wall encoding theoretically and experimentally

Jesse Berwald, Nicholas Chancellor, Raouf Dridi

2022Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences23 citationsDOIOpen Access PDF

Abstract

We analyse 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 with 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. This article is part of the theme issue 'Quantum annealing and computation: challenges and perspectives'.

Topics & Concepts

Quantum annealingQuadratic unconstrained binary optimizationBinary numberIsing modelComputer scienceQuadratic equationEncoding (memory)Quantum computerQuantumQubitPairwise comparisonTheoretical computer scienceComputationAlgorithmStatistical physicsDomain (mathematical analysis)MathematicsPhysicsQuantum mechanicsArtificial intelligenceArithmeticMathematical analysisGeometryQuantum Computing Algorithms and ArchitectureQuantum and electron transport phenomenaQuantum Information and Cryptography
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