Hamiltonian simulation with explicit formulas for Digital-Analog Quantum Computing

• URL: http://arxiv.org/abs/2511.11404v1
• Date: Fri, 14 Nov 2025 15:32:05 GMT
• Title: Hamiltonian simulation with explicit formulas for Digital-Analog Quantum Computing
• Authors: Mikel Garcia-de-Andoin, Thorge Müller, Gonzalo Camacho,
• Abstract summary: Digital-analog is a quantum computational paradigm that employs the natural interaction Hamiltonian of a system as the entangling resource, combined with single qubit gates, to implement universal quantum operations.<n>As in the case of its digital gate-based counterpart, designing digital-analog circuits that employ optimal quantum resources often requires an exceedingly large classical computational time.<n>We provide an exact solution for the problem of expressing arbitrary two-body Hamiltonians as the sum of local unitary transformations of an arbitrary Ising Hamiltonian, with the total number of required terms being at most quadratic in system size.<n>This allows us to design a digital-
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• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Digital-analog is a quantum computational paradigm that employs the natural interaction Hamiltonian of a system as the entangling resource, combined with single qubit gates, to implement universal quantum operations. As in the case of its digital gate-based counterpart, designing digital-analog circuits that employ optimal quantum resources often requires an exceedingly large classical computational time. In this work we find a suboptimal solution to this exponentially large problem, showing that it can be solved within polynomial computational time. In particular, we provide an exact solution for the problem of expressing arbitrary two-body Hamiltonians as the sum of local unitary transformations of an arbitrary Ising Hamiltonian, with the total number of required terms being at most quadratic in system size. This allows us to design a digital-analog simulation protocol that avoids employing numerical optimization over a large parameter space at the preprocessing stage, minimizing computational resources and allowing for further scaling.

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