A Monte Carlo approach for finding optimally controlled quantum gates with differential geometry

• URL: http://arxiv.org/abs/2503.09593v1
• Date: Wed, 12 Mar 2025 17:58:01 GMT
• Title: A Monte Carlo approach for finding optimally controlled quantum gates with differential geometry
• Authors: Adonai Hilário da Silva, Leonardo Kleber Castelano, Reginaldo de Jesus Napolitano,
• Abstract summary: We employ sub-Riemannian geometry on the manifold of the unitary group to obtain optimally designed Hamiltonians for generating single-qubit gates.<n>We compare the optimized control fields obtained from the solutions of the geodesic equation with those extracted from the well-known Krotov method.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A unitary evolution in time may be treated as a curve in the manifold of the special unitary group. The length of such a curve can be related to the energetic cost of the associated computation, meaning a geodesic curve identifies an energetically optimal path. In this work, we employ sub-Riemannian geometry on the manifold of the unitary group to obtain optimally designed Hamiltonians for generating single-qubit gates in an environment with the presence of dephasing noise as well as a two-qubit gate under a time-constant crosstalk interaction. The resulting geodesic equation involves knowing the initial conditions of the parameters that cannot be obtained analytically. We then introduce a random sampling method combined with a minimization function and a cost function to find initial conditions that lead to optimal control fields. We also compare the optimized control fields obtained from the solutions of the geodesic equation with those extracted from the well-known Krotov method. Both approaches provide high fidelity values for the desired quantum gate implementation, but the geodesic method has the advantage of minimizing the required energy to execute the same task. These findings bring new insights for the design of more efficient fields in the arsenal of optimal control theory.

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