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20242026
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quant-ph2026

Performance of Krotov, PRONTO and PINN for optimal control of quantum gates

Martín D. Jiménez, Murilo D. Forlevesi, Emanuel F. de Lima +1

Achieving scalable quantum computing demands high-fidelity operations capable of mitigating population leakage into non-computational states. Physics-Informed Neural Networks (PINN…

quant-ph2026

Reverse engineering of single-qubit quantum gates

Gustavo Fernandes da Costa, Leonardo K. Castelano, Emanuel Fernandes de Lima

In this work, we address the problem of designing single-qubit quantum gates by means of a linearly-polarized field. We show that any desired one-qubit gate corresponding to a spec…

quant-ph2026

Minimal-Energy Optimal Control of Tunable Two-Qubit Gates in Superconducting Platforms Using Continuous Dynamical Decoupling

Adonai Hilário da Silva, Octávio da Motta, Leonardo Kleber Castelano +1

We present a unified scheme for generating high-fidelity entangling gates in superconducting platforms by continuous dynamical decoupling (CDD) combined with variational minimal-en…

quant-ph2025

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

Adonai Hilário da Silva, Leonardo Kleber Castelano, Reginaldo de Jesus Napolitano

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 associate…

quant-ph2025

Inverse Physics-informed neural networks procedure for detecting noise in open quantum systems

Gubio G. de Lima, Iann Cunha, Leonardo Kleber Castelano

Accurate characterization of quantum systems is essential for the development of quantum technologies, particularly in the noisy intermediate-scale quantum (NISQ) era. While tradit…