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Gabriel P. L. M. Fernandes

Publications and source records attributed to Gabriel P. L. M. Fernandes.

6 recordsLinked to original sources

Engineering Quantum Interactions: From Effective Models to Validated Physical Predictions

Effective models make complex quantum dynamics tractable by retaining only the states, processes, and timescales relevant to a given physical question. Their predictive power, however, depends on treating the retained manifold, initial state, observables, micromotion, dissipative channels, and eliminated degrees of freedom consistently. We develop a unified framework for constructing and validating effective descriptions using projection methods, Schrieffer--Wolff transformations, Magnus expansions, adiabatic elimination, small rotations, time coarse graining, and Floquet theory. The quantum Rabi model and its dispersive limits serve as recurring benchmarks for identifying control parameters, virtual processes, spectral corrections, observable dressing, and breakdown. Higher-order resonances and applications to collective light--matter coupling, Raman processes, spin-chain buses, lossy mediators, and driven lattices illustrate how effective interactions can be interpreted and designed. A physical prediction is organized into three order-consistent steps: encoding into the retained description, evolution under the effective dynamics, and reconstruction of the physical observable. This viewpoint exposes failures caused by leakage, small denominators, neglected micromotion, inconsistent observables, memory effects, or incorrectly transformed dissipation, while linking derivation, physical interpretation, and mechanism-guided discovery. Companion notebooks provide QuTiP implementations, convergence tests, and microscopic-to-effective comparisons.

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Optimization Algorithm for Inventory Allocation in Gravity-Flow Racks with Classical and Quantum-Hybrid Computing

Warehouses play a central role in industrial logistics, functioning as critical hubs for storing and organizing inventory to support efficient production. Optimizing item allocation within these facilities is essential for reducing operational costs and improving delivery times. In this work, we address the optimization of inventory allocation in warehouses equipped with gravity-flow racks, which are designed for First In, First Out (FIFO) logistics, a configuration that inherently requires item reinsertions during retrieval operations to maintain flow continuity. These reinsertions, however, are time-consuming and costly, so minimizing their occurrence is crucial for operational efficiency. We propose an optimization strategy that simultaneously allocates multiple items, determining their placement across available shelves in a single decision step, explicitly accounting for every item and every shelf in the warehouse. By jointly evaluating multiple items, our approach enables globally optimized placement decisions, minimizing conflicts that arise in sequential methods. The problem is formulated as a QUBO, allowing implementation on both classical metaheuristics and quantum-hybrid solvers. We assess performance by comparing three classical optimization approaches - two variants of Simulated Annealing and the commercial solver Gurobi - with D-Wave's hybrid solver, which uniquely combines quantum annealing with classical metaheuristics. Complementing these benchmarks, a factory-scale simulation based on real operational data shows that considering larger batches of items in the allocation step can significantly reduce reinsertions, highlighting the practical potential of the proposed approach for industrial logistics.

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Warehouse optimization using a trapped-ion quantum processor

Warehouse optimization stands as a critical component for enhancing operational efficiency within the industrial sector. By strategically streamlining warehouse operations, organizations can achieve significant reductions in logistical costs such as the necessary footprint or traveled path, and markedly improve overall workflow efficiency including retrieval times or storage time. Despite the availability of numerous algorithms designed to identify optimal solutions for such optimization challenges, certain scenarios demand computational resources that exceed the capacities of conventional computing systems. In this context, we adapt a formulation of a warehouse optimization problem specifically tailored as a binary optimization problem and implement it in a trapped-ion quantum computer.

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Trapped Ions as an Architecture for Quantum Computing

In this paper we describe one of the most promising platforms for the construction of a universal quantum computer, which consists of a chain of $N$ ions trapped in a harmonic potential, whose internal states work out as qubits, and are coupled to collective vibrational modes of the chain. From such coupling, it is possible to build interactions between different ions of the chain, that is, qubit-qubit interactions that, together with individual operations on the ions, allow building a quantum computer as first proposed by Cirac and Zoller in the 1990s [Phys. Rev. Lett. 74, 4091 (1995)]. Here we discuss from the physics involved in trapping ions in electromagnetic potentials to the Hamiltonian engineering needed to generate a universal set of logic gates, fundamental for the execution of more complex quantum algorithms. Finally, we present the current state of the art of quantum computing in trapped ion systems, highlighting recent advances made by companies and government projects that use such architecture, such as IonQ and AQTION.

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Alternatives to a nonhomogeneous partial differential equation quantum algorithm

Recently J. M. Arrazola et al. [Phys. Rev. A 100, 032306 (2019)] proposed a quantum algorithm for solving nonhomogeneous linear partial differential equations of the form $Aψ(\textbf{r})=f(\textbf{r})$. Its nonhomogeneous solution is obtained by inverting the operator $A$ along with the preparation and measurement of special ancillary modes. In this work we suggest modifications in its structure to reduce the costs of preparing the initial ancillary states and improve the precision of the algorithm for a specific set of inputs. These achievements enable easier experimental implementation of the quantum algorithm based on nowadays technology.

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Superposition of two-mode squeezed states for quantum information processing and quantum sensing

We investigate superpositions of two-mode squeezed states (TMSSs), which have potential applications to quantum information processing and quantum sensing. Firstly we study some properties of these nonclassical states such as the statistics of each mode and the degree of entanglement between the two modes, which can be higher than that of a TMSS with the same degree of squeezing. The states we consider can be prepared by inducing two-mode Jaynes-Cummings and anti-Jaynes-Cummings interactions in a system of two modes and a spin-$\tfrac{1}{2}$ particle, for instance in the trapped ion domain, as described here. We show that when two harmonic oscillators are prepared in a superposition of two TMSSs, each reduced single-mode state can be advantageously employed to sense arbitrary displacements of the mode in phase space. The Wigner function of this reduced state exhibits a symmetrical peak centered at the phase-space origin, which has the convenient peculiarity of getting narrower in both quadratures simultaneously as the average photon number increases. This narrow peakcan be used as the pointer of our quantum sensor, with its position in phase space indicating the displacement undergone by the oscillator.

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