arXiv subjects

Leonardo K. Castelano

Publications and source records attributed to Leonardo K. Castelano.

15 recordsLinked to original sources

arXiv2026

Beyond Impedance Additivity: A Systematic Nonlinear Perspective on Memristor Associations

We investigate the validity of the superposition principle and impedance additivity in AC circuits containing a memristive device connected in series with a resistor, capacitor, or inductor. While the series association of impedances is a cornerstone of linear circuit theory, its applicability to memory-bearing nonlinear systems remains largely unexplored. Using a state-dependent memristive model, we numerically analyze the stationary current response under sinusoidal excitation and characterize the resulting harmonic spectra, Bode diagrams, and Nyquist plots. To assess whether the fundamental response can still be interpreted through an equivalent-circuit framework, we introduce the concept of an apparent memristor, whose effective parameters are extracted directly from the composite impedance. We show that, although the fundamental harmonic can be accurately reproduced by an apparent equivalent circuit over selected parameter ranges, the effective parameters differ substantially from those of the isolated memristor, revealing a renormalization induced by the coupling to the passive element. More importantly, we identify parameter regimes in which the apparent-circuit description breaks down altogether, particularly for capacitor and inductor-coupled systems, demonstrating that the composite impedance cannot generally be expressed as the sum of independent impedances. These results establish boundaries for the use of equivalent-circuit models in memory-enabled electronic systems and provide practical guidelines for the interpretation of impedance spectroscopy in nonlinear devices exhibiting memory.

cond-mat.mtrl-sci↗

arXiv2026

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

Achieving scalable quantum computing demands high-fidelity operations capable of mitigating population leakage into non-computational states. Physics-Informed Neural Networks (PINNs) have recently emerged as a powerful paradigm to unify quantum hardware characterization (inverse problems) and pulse engineering (direct problems), laying the foundational architecture for autonomous quantum processors. However, standard PINN frameworks face severe numerical bottlenecks, such as spectral bias, when attempting to simultaneously solve highly oscillatory multi-level dynamics and optimize continuous control fields under strict global phase constraints. In this work, we propose an enhanced PINN scheme for quantum optimal control (PINNQOC) that circumvents these limitations by incorporating Fourier feature embeddings, dynamic epoch normalization, and an informed pre-training routine. To rigorously evaluate its performance, we systematically benchmark our framework against two premier continuous control solvers: the first-order Krotov method and the second-order Projection Operator Newton Method for Trajectory Optimization (PRONTO). These techniques are applied to implement multiple quantum gates on a truncated three-level fluxonium qubit and a four-level Nitrogen-Vacancy center coupled to a Carbon-13 nuclear spin. Our advanced PINNQOC approach successfully suppresses population leakage while achieving gate fidelities exceeding 99.9$\%$, matching the efficacy of traditional solvers. Finally, we provide a comprehensive analysis of computational times, iteration efficiency, and mean leakage, highlighting the distinct trade-offs and avenues for embedding physics-guided machine learning into automated quantum hardware pipelines.

quant-ph↗

arXiv2026

Reverse engineering of single-qubit quantum gates

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 special unitary matrix can be generated by a modulated sinusoidal field. The only approximation involved is the rotating-wave-approximation. The formula for the control field is obtained by inverting the equation of motion for the evolution operator and imposing the conditions for the desired gate. We give a simple procedure to obtain closed analytical formulas for the fields in terms of a priori chosen dynamical functions. Additionally, these dynamical functions can depend on tunable free parameters intentionally introduced to meet a desired performance criteria.

quant-ph↗

arXiv2026

Phase-Topology Classification of Memristor Hysteresis Loops via Self-Crossings

Memristive devices have revolutionized non-volatile memory and neuromorphic computing, yet the geometry of their hysteresis loops -- in particular, the occurrence and robustness of multiple self-crossings -- remains poorly understood. Here we introduce a topological and algebraic framework that treats the number of transverse self-intersections of a memristor hysteresis loop as a robust integer-valued invariant. Drawing on differential topology, singularity theory, and cusp catastrophe, we employ discriminants and resultants to stratify the six-dimensional parameter space. This approach partitions the parameter space into structurally stable regions separated by explicitly computable catastrophe surfaces. We demonstrate that the crossing number remains strictly invariant under continuous deformations and changes only at self-tangencies or cusp singularities, thereby providing a complete classification of all multi-lobed hysteresis behaviors. These insights bridge device physics with modern singularity theory and suggest a clear roadmap for exploiting higher-order memory effects in next-generation electronics and brain-inspired hardware.

cond-mat.other↗

arXiv2026

2D Canonical Approach for Beating the Boltzmann Tyranny Using Memory

The 60 mV$/$decade subthreshold limit at room temperature, coined as the Boltzmann tyranny, remains a fundamental obstacle to the continued down-scaling of conventional transistors. While several strategies have sought to overcome this constraint through non-thermal carrier injection, most rely on ferroelectric-based or otherwise material-specific mechanisms that require complex fabrication and stability control. Here, we develop a universal theoretical framework showing that intrinsic memory effects in nanometric field-effect transistors can naturally bypass this limit. Within the Landauer-Büttiker quantum transport formalism, we incorporate charge-trapping mechanisms that dynamically renormalize the conduction band edge. The resulting analytical expression for the subthreshold swing explicitly links memory dynamics to gate efficiency, revealing that a reduced carrier generation rate or enhanced trapping activity leads to sub-thermal switching, thus breaking the Boltzmann barrier. The model captures key experimental features and provides clear, generalizable design principles, establishing memory-assisted transistors as a robust pathway toward ultra-low-power and multifunctional electronic architectures.

physics.app-ph↗

arXiv2025

Gate-controlled analog memcapacitance in LaAlO3/SrTiO3 interface-based devices

Current memcapacitor implementations typically demand complex fabrication processes or depend on organic materials exhibiting poor environmental stability and reproducibility. Here, we demonstrate memcapacitor structures utilizing a quasi 2-dimensional electron gas, formed at the crystalline LaAlO3/SrTiO3 heterointerface, as electrodes and SiO2/SrTiO3 as dielectric layer. The observed memcapacitance originates from the charge localization in a lateral floating gate, while an applied gate voltage enables reversible tuning of the device capacitance. Furthermore, preprogrammed or erased gate biases enable controllable shifts of the capacitance hysteresis window toward positive or negative bias, leading to an enlarged capacitance gap at zero bias. A memcapacitor model developed for this system reproduces the main features of the experimental capacitance hysteresis, capturing the effects of charge fluctuations and dielectric frequency modulation within the oxide layer. The demonstrated low-voltage operation and gate tunability of oxide interface-based memcapacitors highlight their potential for power-efficient, capacitor-based neuromorphic and synaptic electronic architectures.

physics.app-ph↗

arXiv2024

Accuracy Bottlenecks in Impedance Spectroscopy due to Transient Effects

Impedance spectroscopy is vital for material characterization and assessing electrochemical device performance. It provides real-time analysis of dynamic processes such as electrode kinetics, electrons, holes or ion transport, and interfacial or defect driven phenomena. However, the technique is sensitive to experimental conditions, introducing potential variability in results. The intricate interplay of transient effects within the realm of spectral impedance analyses introduces a layer of complexity that may impede straightforward interpretations. This demands a nuanced approach for refining analytical methodologies and ensuring the fidelity of impedance characterization once the dynamic contributions of transient ingredients cannot be disentangled from the underlying steady-state characteristics. In our study, we experimentally identify that the transient effects in a memristor device are most pronounced near an optimal frequency related to intrinsic relaxation times, with these effects diminishing as the frequency varies beyond or below this range. While inherent systematic errors impose a practical limit (accuracy floor) on achievable measurement accuracy, this paper offers qualitative and quantitative insights into how specific procedures affect this limit and how to reduce it in orders of magnitude. Only by effectively addressing these errors we can push beyond this constraint.

cond-mat.mes-hall↗

arXiv2023

Physics informed neural networks learning a two-qubit Hamiltonian

Machine learning techniques are employed to perform the full characterization of a quantum system. The particular artificial intelligence technique used to learn the Hamiltonian is called physics informed neural network (PINN). The idea behind PINN is the universal approximation theorem, which claims that any function can be approximate by a neural network if it contains enough complexity. Consequently, a neural network can be a solution of a physical model. Moreover, by means of extra data provided by the user, intrinsic physical parameters can be extracted from the approach called inverse-PINN. Here, we apply inverse-PINN with the goal of extracting all the physical parameters that constitutes a two qubit Hamiltonian. We find that this approach is very efficient. To probe the robustness of the inverse-PINN to learn the Hamiltonian of a two-qubit system, we use the IBM quantum computers as experimental platforms to obtain the data that is plugged in the PINN. We found that our method is able to predict the two-qubit parameters with 5% of accuracy on average.

quant-ph↗

arXiv2023

Effectiveness of the Krotov method in controlling open quantum systems

We apply the Krotov method for open and closed quantum systems with the objective of finding optimized controls to manipulate qubit/qutrit systems in the presence of the external environment. In the case of unitary optimization, the Krotov method is first applied to a quantum system neglecting its interaction with the environment. The resulting controls from the unitary optimization are then used to drive the system along with the environmental noise. In the case of non-unitary optimization, the Krotov method already takes into account the noise during the optimization process. We consider two distinct computational task: target-state preparation and quantum gate implementation. These tasks are carried out in simple qubit/qutrit systems and also in systems presenting leakage states. For the state-preparation cases, the controls from the non-unitary optimization outperform the controls from the unitary optimization. However, as we show here, this is not always true for the implementation of quantum gates. There are some situations where the unitary optimization performs equally well compared to the non-unitary optimization. We verify that these situations corresponds to either the absence of leakage states or to the effects of dissipation being spread uniformly over the system, including non-computational levels. For such cases, the quantum gate implementation must cover the entire Hilbert space and there is no way to dodge dissipation. On the other hand, if the subspace containing the computational levels and its complement are differently affected by dissipation, the non-unitary optimization becomes effective.

quant-ph↗

arXiv2023

Optimizing control fields for adiabatic protocols in the presence of noise

Quantum control techniques are employed to perform adiabatic quantum computing in the presence of noise. First, we analyze the adiabatic entanglement protocol (AEP) for two qubits. In this case, we found that this protocol is very robust against noise. The reason behind this fact is related to the chosen Hamiltonians, where the ground state of the initial Hamiltonian is not affected by the noise. The optimal control solution, in this case, is to leave the system in its ground state and apply a fast pulse to entangle the qubits at the end of the time evolution. Secondly, we probe a system composed of three qubits, where the goal is to teleport the first qubit to the third qubit. In this case, the ground state of the system does not share the same robustness against noise as in the case of AEP. To improve the robustness against noise, we propose the inclusion of a local control field that can drive the system to an intermediate state, which is more robust against noise in comparison to other states. The target state is also achieved by a fast pulse at the final time. We found that this approach provides a significant gain in the fidelity and can improve the adiabatic quantum computing in the so-called Noisy Intermediate-Scale Quantum (NISQ) devices in a near future.

quant-ph↗

arXiv2021

Optimal solutions to quantum annealing using two independent control functions

We investigate the quantum computing paradigm consisted of obtaining a target state that encodes the solution of a certain computational task by evolving the system with a combination of the problem-Hamiltonian and the driving-Hamiltonian. We analyze this paradigm in the light of Optimal Control Theory considering each Hamiltonian modulated by an independent control function. In the case of short evolution times and bounded controls, we analytically demonstrate that an optimal solution consists of both controls tuned at their upper bound for the whole evolution time. This optimal solution is appealing because of its simplicity and experimental feasibility. To numerically solve the control problem, we propose the use of a quantum optimal control technique adapted to limit the amplitude of the controls. As an application, we consider a teleportation protocol and compare the fidelity of the teleported state obtained for the two-control functions with the usual single-control function scheme and with the quantum approximate optimization algorithm (QAOA). We also investigate the energetic cost and the robustness against systematic errors in the teleportation protocol, considering different time evolution schemes. We show that the scheme with two-control functions yields a higher fidelity than the other schemes for the same evolution time.

quant-ph↗

arXiv2017

Optimal control of universal quantum gates in a double quantum dot

We theoretically investigate electron spin operations driven by applied electric fields in a semiconductor double quantum dot (DQD). Our model describes a DQD formed in semiconductor nanowire with longitudinal potential modulated by local gating. The eigenstates for two electron occupation, including spin-orbit interaction, are calculated and then used to construct a model for the charge transport cycle in the DQD taking into account the spatial dependence and spin mixing of states. The dynamics of the system is simulated aiming at implementing protocols for qubit operations, that is, controlled transitions between the singlet and triplet states. In order to obtain fast spin manipulation, the dynamics is carried out taking advantage of the anticrossings of energy levels introduced by the spin-orbit and interdot couplings. The theory of optimal quantum control is invoked to find the specific electric-field driving that performs qubit logical operations. We demonstrate that it is possible to perform within high efficiency a universal set of quantum gates $\{$CNOT, H$\otimes$I, I$\otimes$H, T$\otimes$I, and T$\otimes$I$\}$, where H is the Hadamard gate, T is the $π/8$ gate, and I is the identity, even in the presence of a fast charge transport cycle and charge noise effects.

quant-ph↗

arXiv2013

Generation and control of spin-polarized photocurrents in GaMnAs heterostructures

Photocurrents are calculated for a specially designed GaMnAs semiconductor heterostructure. The results reveal regions in the infrared range of the energy spectrum in which the proposed structure is remarkably spin-selective. For such photon energies, the generated photocurrents are strongly spin-polarized. Application of a relatively small static bias in the growth direction of the structure is predicted to efficiently reverse the spin-polarization for some photon energies. This behavior suggests the possibility of conveniently simple switching mechanisms. The physics underlying the results is studied and understood in terms of the spin-dependent properties emerging from the particular potential profile of the structure.

cond-mat.mes-hall↗

arXiv2013

Probing the degree of non-Markovianity for independent and common environments

We study the non-Markovianity of the dynamics of open quantum systems focusing on the cases of independent and common environmental interactions. We investigate the degree of non-Markovianity quantified by two distinct measures proposed by Luo, Fu and Song (LFS) and Breuer, Laine and Pillo (BLP). We show that the amount of non-Markovianity, for a single and a pair of qubits, depends on the quantum process, the proposed measure and whether the environmental interaction is collective or independent. In particular, we demonstrate that while the degree of non-Markovianity generally increases with the number of the qubits in the system for independent environments, the same behavior is not always observed for common environments. In the latter case, our analysis suggests that the amount of non-Markovianity could increase or decrease depending on the properties of the considered quantum process.

quant-ph↗

arXiv2012

Shielding quantum discord through continuous dynamical decoupling

This work investigates the use of dynamical decoupling to shield quantum discord from errors introduced by the environment. Specifically, a two-qubits system interacting with independent baths of bosons is considered. The initial conditions of the system were chosen as pure and mixed states, while the dynamical decoupling has been achieved by means of continuous fields. The effects of the temperature on the shielding of quantum discord is also studied. It is shown that although the quantum discord for particular initial states may be perfectly preserved over some finite time window in the absence of any protective field, the effectiveness of the dynamical decoupling with continuous fields depends essentially on the timescale required to preserve quantum discord. It is also shown that for these particular initial states the time for which the shielding of the quantum discord becomes effective decreases as the temperature increases.

quant-ph↗