arXiv subjects

Leonardo Massai

Publications and source records attributed to Leonardo Massai.

At least 19 recordsLinked to original sources

arXiv2026

Coherent and ultra-low-power EDSR with a flopping-mode spin qubit in germanium

Hole spin qubits in semiconductor quantum dots (QDs) enable high-fidelity all-electric control, but conventional electric dipole spin resonance (EDSR) can require substantial rf drive power at the low magnetic fields that are favorable for qubit coherence and readout. In planar Ge hole spin qubits, this can reach -27 dBm at the device, posing challenges for scalable architectures due to heating and crosstalk. Here, we demonstrate a flopping-mode (FM) qubit in Ge, where a single spin is delocalized in a double QD, combining first-order protection against charge noise with exceptionally efficient electric driving. By mapping out coherence sweet-spots as a function of magnetic field orientation we achieve $T_2^*= 1.4μ\mathrm{s}$, $T_2^{\mathrm{Hahn}}= 11.5 μ\mathrm{s}$, $T^{ϕ, \mathrm{CPMG32}}_2= 130 μ\mathrm{s}$, and $T_1= 226 μ\mathrm{s}$, and a single-qubit gate fidelity of up to 99.76$\%$ for a gate time $t_{Xπ} = 88$ ns. Importantly, these results are obtained at a nearly in-plane magnetic field of 5 mT using only -52 dBm drive power at the device. We further find that qubit relaxation in this regime is consistent with a two-photon Orbach process, providing a route for further optimization. Our results demonstrate that FM-EDSR supports ultra-low-power, high-fidelity single-qubit operations, improvements that could benefit scalable hole-spin-based architectures and hybrid spin-photon interfaces.

cond-mat.mes-hall↗

arXiv2026

Context-Enriched Performance Boosting via Operator Decomposition

Performance Boosting (PB) is a control framework that, for a pre-stabilized system subject to $\mathcal L_p$ process disturbances, parametrizes the controllers that preserve closed-loop $\mathcal L_p$-stability through a causal $\mathcal L_p$-stable operator mapping reconstructed disturbances to corrective control actions. Although this permits optimization over expressive stability-preserving controllers, learning a desired policy from disturbance information alone can be difficult. We introduce a structured factorization for context-enriched, multi-input PB operators. The proposed architecture combines an $\mathcal L_p$-stable dynamical module that processes reconstructed disturbances with a uniformly bounded matrix-valued mixer depending on disturbances and contextual signals. Under the standard PB assumptions, this factorization preserves closed-loop $\mathcal L_p$-stability by construction. Moreover, on a weighted-envelope disturbance domain, we prove that the factorization is necessary and sufficient for causal operators satisfying a context-uniform envelope-preservation property. A numerical moving-gate navigation experiment demonstrates the advantages of the proposed architecture over context-agnostic PB, MAD, and reference-aware PB baselines.

eess.SY↗

arXiv2026

Equilibria in Network Constrained Markets with System Operator

We study a networked economic system composed of $n$ producers supplying a single homogeneous good to a number of geographically separated markets and of a centralized authority, called the market maker. Producers compete à la Cournot, by choosing the quantities of good to supply to each market they have access to in order to maximize their profit. Every market is characterized by its inverse demand functions returning the unit price of the considered good as a function of the total available quantity. Markets are interconnected by a dispatch network through which quantities of the considered good can flow within finite capacity constraints and possibly satisfying additional linear physical constraints. Such flows are determined by the action of a system operator, who aims at maximizing a designated welfare function. We model such competition as a strategic game with $n+1$ players: the producers and the system operator. For this game, we first establish the existence of pure-strategy Nash equilibria under standard concavity assumptions. We then identify sufficient conditions for the game to be exact potential with an essentially unique Nash equilibrium. Next, we present a general result that connects the optimal action of the system operator with the capacity constraints imposed on the network. For the commonly used Walrasian welfare, our finding proves a connection between capacity bottlenecks in the market network and the emergence of price differences between markets separated by saturated lines. This phenomenon is frequently observed in real-world scenarios, for instance in power networks. Finally, we validate the model with data from the Italian day-ahead electricity market.

cs.GT↗

arXiv2026

Engineering two-qubit gates via anisotropic exchange in germanium spin qubits

Germanium hole spin qubits are a promising and versatile platform for quantum computation and simulation. In this system, strong spin-orbit interaction (SOI) renders the single-qubit $g$-tensor anisotropic and electrically tunable, enabling operational sweet spots with reduced noise sensitivity. SOI also transforms the isotropic two-qubit exchange coupling into an anisotropic tensor whose geometry is inherited from the single-qubit $g$-tensors and spin-flip tunnelling. Here, using two hole spin qubits in a strained-germanium quantum well and full vector control of the magnetic field, we map this exchange tensor, separate it into longitudinal and transverse components, and show that they govern controlled-phase and SWAP-like dynamics, respectively. We find that the longitudinal exchange can be tuned via the magnetic field orientation from a conventional positive value, through zero, to an effectively negative one, as measured by inverted exchange-split spin transitions. The magnetic field direction thus provides continuous control over the interaction Hamiltonian: at a point of purely transverse exchange, we engineer a single-pulse baseband iSWAP, unattainable under isotropic exchange. Linking $g$-tensor geometry to exchange anisotropy establishes native Hamiltonian engineering, enabling spin-based quantum simulation and gate sets selected by the global field orientation alone.

cond-mat.mes-hall↗

arXiv2026

Free Parametrization of L_2-Bounded Structured State-Space Controllers for Nonlinear Control with Stability Guarantees

Designing stabilizing control policies for nonlinear systems while optimizing complex objectives remains a formidable challenge. Neural networks (NNs), despite their expressive power, can be highly sensitive to small input perturbations and can easily destabilize the closed-loop system. Existing approaches often impose explicit constraints on the controller's parameters to ensure stability, but this typically leads to additional computational overhead. To address this issue, we leverage recently proposed structured state-space models (SSMs) to parametrize discrete-time control policies for nonlinear systems. Our key contribution is a new free parametrization of linear time-invariant (LTI) systems with a prescribed L2 gain. We use this result to construct the L2-Recurrent Unit (L2RU), an SSM layer that enforces the desired L2 bound by design. The resulting architecture can be used to guarantee closed-loop stability via the small-gain theorem or the so-called performance-boosting framework, independently of the controller's optimization parameters, thereby enabling fully unconstrained optimization of general nonlinear objectives. Furthermore, the structure induced by the proposed parametrization enables the efficient processing of long input sequences, as it is highly parallelizable through algorithms such as parallel scan. We demonstrate the effectiveness of this approach on a formation-control task for mobile robots, where the L2RU-based controller ensures collision and obstacle avoidance while maintaining stability and performance.

eess.SY↗

arXiv2026

L2RU: a Structured State Space Model with prescribed L2-bound

Structured state-space models (SSMs) have recently emerged as a powerful architecture at the intersection of machine learning and control, featuring layers composed of discrete-time linear time-invariant (LTI) systems followed by pointwise nonlinearities. These models combine the expressiveness of deep neural networks with the interpretability and inductive bias of dynamical systems, offering strong performance on long-sequence tasks with favorable computational complexity. However, their adoption in applications such as system identification and optimal control remains limited by the difficulty of enforcing stability and robustness in a principled and tractable manner. We introduce L2RU, a class of SSMs endowed with a prescribed $\mathcal{L}_2$-gain bound, guaranteeing input--output stability and robustness for all parameter values. The L2RU architecture is derived from free parametrizations of LTI systems satisfying an $\mathcal{L}_2$ constraint, enabling unconstrained optimization via standard gradient-based methods while preserving rigorous stability guarantees. Specifically, we develop two complementary parametrizations: a non-conservative formulation that provides a complete characterization of square LTI systems with a given $\mathcal{L}_2$-bound, and a conservative formulation that extends the approach to general (possibly non-square) systems while improving computational efficiency through a structured representation of the system matrices. Both parametrizations admit efficient initialization schemes that facilitate training long-memory models. We demonstrate the effectiveness of the proposed framework on a nonlinear system identification benchmark, where L2RU achieves improved performance and training stability compared to existing SSM architectures, highlighting its potential as a principled and robust building block for learning and control.

eess.SY↗

arXiv2026

U-centrality: A Network Centrality Measure Based on Minimum Energy Control for Laplacian Dynamics

Network centrality is a foundational concept for quantifying the importance of nodes within a network. Many traditional centrality measures--such as degree and betweenness centrality--are purely structural and often overlook the dynamics that unfold across the network. However, the notion of a node's importance is inherently context-dependent and must reflect both the system's dynamics and the specific objectives guiding its operation. Motivated by this perspective, we propose a dynamic, task-aware centrality framework rooted in optimal control theory. By formulating a problem on minimum energy control of average opinion based on Laplacian dynamics and focusing on the variance of terminal state, we introduce a novel centrality measure--termed U-centrality--that quantifies a node's ability to unify the agents' state. We demonstrate that U-centrality interpolates between known measures: it aligns with degree centrality in the short-time horizon and converges to a new centrality over longer time scales which is closely related to current-flow closeness centrality. This work bridges structural and dynamical approaches to centrality, offering a principled, versatile tool for network analysis in dynamic environments.

cs.SI↗

arXiv2026

A dressed singlet-triplet qubit in germanium

In semiconductor hole spin qubits, low magnetic field ($B$) operation extends the coherence time ($T_\mathrm{2}^*$) but proportionally reduces the gate speed. In contrast, singlet-triplet (ST) qubits are primarily controlled by the exchange interaction ($J$) and can thus maintain high gate speeds even at low $B$. However, a large $J$ introduces a significant charge component to the qubit, rendering ST qubits more vulnerable to charge noise when driven. Here, we demonstrate a highly coherent ST hole spin qubit in germanium, operating at both low $B$ and low $J$. By modulating $J$, we achieve resonant driving of the ST qubit, obtaining an average gate fidelity of $99.68\%$ and a coherence time of $T_\mathrm{2}^*=1.9\,μ$s. Moreover, by applying the resonant drive continuously, we realize a dressed ST qubit with a tenfold increase in coherence time ($T_\mathrm{2ρ}^*=20.3\,μ$s). Frequency modulation of the driving signal enables universal control, with an average gate fidelity of $99.63\%$. Our results demonstrate the potential for extending coherence times while preserving high-fidelity control of germanium-based ST qubits, paving the way for more efficient operations in semiconductor-based quantum processors.

cond-mat.mes-hall↗

arXiv2026

Boosting the transient performance of reference tracking controllers with neural networks

Reference tracking is a key objective in many control systems, including those characterized by complex nonlinear dynamics. In these settings, traditional control approaches can effectively ensure steady-state accuracy but often struggle to explicitly optimize transient performance. Neural network controllers have gained popularity due to their adaptability to nonlinearities and disturbances; however, they often lack formal closed-loop stability and performance guarantees. To address these challenges, a recently proposed neural-network control framework known as Performance Boosting (PB) has demonstrated the ability to maintain $\mathcal{L}_p$ stability properties of nonlinear systems while optimizing generic transient costs. This paper extends the PB approach to reference tracking problems. First, we characterize the complete set of nonlinear controllers that preserve desired tracking properties for nonlinear systems equipped with base reference-tracking controllers. Then, we show how to optimize transient costs while searching within subsets of tracking controllers that incorporate expressive neural network models. Furthermore, we analyze the robustness of our method to uncertainties in the underlying system dynamics. Numerical simulations on a robotic system demonstrate the advantages of our approach over the standard PB framework.

eess.SY↗

arXiv2025

Neural Identification of Feedback-Stabilized Nonlinear Systems

Neural networks have demonstrated remarkable success in modeling nonlinear dynamical systems. However, identifying these systems from closed-loop experimental data remains a challenge due to the correlations induced by the feedback loop. Traditional nonlinear closed-loop system identification methods struggle with reliance on precise noise models, robustness to data variations, or computational feasibility. Additionally, it is essential to ensure that the identified model is stabilized by the same controller used during data collection, ensuring alignment with the true system's closed-loop behavior. The dual Youla parameterization provides a promising solution for linear systems, offering statistical guarantees and closed-loop stability. However, extending this approach to nonlinear systems presents additional complexities. In this work, we propose a computationally tractable framework for identifying complex, potentially unstable systems while ensuring closed-loop stability using a complete parameterization of systems stabilized by a given controller. We establish asymptotic consistency in the linear case and validate our method through numerical comparisons, demonstrating superior accuracy over direct identification baselines and compatibility with the true system in stability properties.

eess.SY↗

arXiv2025

Spatial uniformity of g-tensor and spin-orbit interaction in germanium hole spin qubits

Holes in Ge/SiGe heterostructures are now a leading platform for semiconductor spin qubits, thanks to the high confinement quality, two-dimensional arrays, high tunability, and larger gate structure dimensions. One limiting factor for the operation of large arrays of qubits is the considerable variation in qubit frequencies or properties resulting from the strongly anisotropic $g$-tensor. We study the $g$-tensors of six and seven qubits in an array with a Y geometry across two devices. We report a mean distribution of the tilts of the $g$-tensor's out-of-plane principal axis of around $1.1 °$, where nearby quantum dots are more likely to have a similar tilt. Independently of this tilt, and unlike simple theoretical predictions, we find a strong in-plane $g$-tensor anisotropy with strong correlations between neighboring quantum dots. Additionally, in one device where the principal axes of all g-tensors are aligned along the [100] crystal direction, we extract the spin-flip tunneling vector from adjacent dot pairs and find a pattern that is consistent with a uniform Dresselhaus-like spin-orbit field. The Y arrangement of the gate layout and quantum dots allows us to rule out local factors like electrostatic confinement shape or local strain as the origin of the preferential direction. Our results reveal long-range correlations in the spin-orbit interaction and $g$-tensors that were not previously predicted or observed, and could prove critical to reliably understand $g$-tensors in germanium quantum dots.

cond-mat.mes-hall↗

arXiv2025

Resonant two-qubit gates for fermionic simulations with spin qubits

In gate-defined semiconductor spin qubits, the highly tunable Heisenberg exchange interaction is leveraged to implement fermionic two-qubit gates such as CZ and SWAP. However, the broader family of fermionic simulation (fSim) gates remains unexplored, and has the potential to enhance the performance of near-term quantum simulation algorithms. Here, we demonstrate a method to implement the fSim gate set in spin qubits using a single pulse combining baseband and resonant exchange drives. This approach minimizes gate duration and drive amplitude, mitigating decoherence and crosstalk. We validate its effectiveness by realizing a resonant iSWAP gate between two hole spins in germanium, achieving a fidelity of 93.8(5)% extracted with interleaved randomized benchmarking. Quantum process tomography confirms accurate gate calibration and identifies qubit decoherence as the dominant error source. Our results establish a practical route toward a versatile and efficient two-qubit gate set for spin-based quantum processors.

cond-mat.mes-hall↗

arXiv2025

HfO$_2$-based platform for high-index-contrast visible/UV integrated photonics

Ultraviolet and visible integrated photonics are enabling for applications in quantum information, sensing, and spectroscopy, among others. Few materials support low-loss photonics into the UV, and the relatively low refractive index of known depositable materials limits the achievable functionality. Here we present a high-index integrated photonics platform based on HfO$_2$ and Al$_2$O$_3$ composites deposited via Atomic Layer Deposition (ALD) with low loss in the visible and near-UV. We show that Al$_2$O$_3$ incorporation dramatically decreases bulk loss compared to pure HfO$_2$, consistent with inhibited crystallization due to the admixture of Al$_2$O$_3$. Composites exhibit refractive index $n$ following the average of that of HfO$_2$ and Al$_2$O$_3$, weighted by the HfO$_2$ fractional composition $x$. At $λ=375$ nm, composites with $x=0.67$ exhibit $n=2.08$ preserving most of HfO$_2$'s significantly higher index, and $3.8(7) $ dB/cm material loss. We further present fully etched and cladded waveguides, grating couplers, and ring resonators, realizing single-mode waveguide loss of $0.25(2)$ dB/cm inferred from resonators of 2.6 million intrinsic quality factor at $λ=729$ nm, $2.6(2)$ dB/cm at $λ=405$ nm, and $7.7(6)$ dB/cm at $λ=375$ nm. We measure the composite's thermo-optic coefficient (TOC) to be $2.44(3) \times 10^{-5}$ RIU/$^\circ$C near $λ=397$ nm. This work establishes (HfO$_2$)$_x$(Al$_2$O$_3$)$_{1-x}$ composites as a platform amenable to integration for low-loss, high-index photonics spanning the UV to NIR.

physics.optics↗

arXiv2025

Identifying and mitigating errors in hole spin qubit readout

High-fidelity readout of spin qubits in semiconductor quantum dots can be achieved by combining a radio-frequency (RF) charge sensor together with spin-to-charge conversion and Pauli spin blockade. However, reaching high readout fidelities in hole spin qubits remains elusive and is complicated by a combination of site-dependent spin anisotropies and short spin relaxation times. Here, we analyze the different error processes that arise during readout using a double-latched scheme in a germanium double quantum dot hole spin qubit system. We first investigate the spin-to-charge conversion process as a function of magnetic field orientation, and configure the system to adiabatically map the $\lvert \downarrow\downarrow \rangle$ state to the only non-blockaded state. We reveal a strong dependence of the spin relaxation rates on magnetic field strength and minimize this relaxation by operating at low fields. We further characterize and mitigate the error processes that arise during the double-latching process. By combining an RF charge sensor, a double-latching process, and optimized magnetic field parameters, we achieve a single-shot single-qubit state-preparation-and-measurement fidelity of 97.0%, the highest reported fidelity for hole spin qubits. Unlike prior works and vital to usability, we simultaneously maintain universal control of both spins. These findings lay the foundation for the reproducible achievement of high-fidelity readout in hole-based spin quantum processors.

cond-mat.mes-hall↗

arXiv2024

Optimal distributed control with stability guarantees by training a network of neural closed-loop maps

This paper proposes a novel approach to improve the performance of distributed nonlinear control systems while preserving stability by leveraging Deep Neural Networks (DNNs). We build upon the Neural System Level Synthesis (Neur-SLS) framework and introduce a method to parameterize stabilizing control policies that are distributed across a network topology. A distinctive feature is that we iteratively minimize an arbitrary control cost function through an unconstrained optimization algorithm, all while preserving the stability of the overall network architecture by design. This is achieved through two key steps. First, we establish a method to parameterize interconnected Recurrent Equilibrium Networks (RENs) that guarantees a bounded $\mathcal{L}_2$ gain at the network level. This ensures stability. Second, we demonstrate how the information flow within the network is preserved, enabling a fully distributed implementation where each subsystem only communicates with its neighbors. To showcase the effectiveness of our approach, we present a simulation of a distributed formation control problem for a fleet of vehicles. The simulation demonstrates how the proposed neural controller enables the vehicles to maintain a desired formation while navigating obstacles and avoiding collisions, all while guaranteeing network stability.

math.OC↗

arXiv2024

Unconstrained learning of networked nonlinear systems via free parametrization of stable interconnected operators

This paper characterizes a new parametrization of nonlinear networked incrementally $L_2$-bounded operators in discrete time. The distinctive novelty is that our parametrization is \emph{free} -- that is, a sparse large-scale operator with bounded incremental $L_2$ gain is obtained for any choice of the real values of our parameters. This property allows one to freely search over optimal parameters via unconstrained gradient descent, enabling direct applications in large-scale optimal control and system identification. Further, we can embed prior knowledge about the interconnection topology and stability properties of the system directly into the large-scale distributed operator we design. Our approach is extremely general in that it can seamlessly encapsulate and interconnect state-of-the-art Neural Network (NN) parametrizations of stable dynamical systems. To demonstrate the effectiveness of this approach, we provide a simulation example showcasing the identification of a networked nonlinear system. The results underscore the superiority of our free parametrizations over standard NN-based identification methods where a prior over the system topology and local stability properties are not enforced.

eess.SY↗

arXiv2022

Equilibria in Network Constrained Energy Markets

We study an energy market composed of producers who compete to supply energy to different markets and want to maximize their profits. The energy market is modeled by a graph representing a constrained power network where nodes represent the markets and links are the physical lines with a finite capacity connecting them. Producers play a networked Cournot game on such a network together with a centralized authority, called market maker, that facilitates the trade between geographically separate markets via the constrained power network and aims to maximize a certain welfare function. We first prove a general result that links the optimal action of the market maker with the capacity constraint enforced on the power network. Under mild assumptions, we study the existence and uniqueness of Nash equilibria and exploit our general result to prove a connection between capacity bottlenecks in the power network and the emergence of price differences between different markets that are separated by saturated lines, a phenomenon that is often observed in real power networks.

econ.GN↗

arXiv2022

Pure circularly polarized light emission from waveguide microring resonators

Circularly polarized light plays a key role in many applications including spectroscopy, microscopy, and control of atomic systems. Particularly in the latter, high polarization purity is often required. Integrated technologies for atomic control are progressing rapidly, but while integrated photonics can generate fields with pure linear polarization, integrated generation of highly pure circular polarization states has not been addressed. Here, we show that waveguide microring resonators, perturbed with azimuthal gratings and thereby emitting beams carrying optical orbital angular momentum, can generate radiated fields of high circular polarization purity. We achieve this in a passive device by taking advantage of symmetries of the structure and radiated modes, and directly utilizing both transverse and longitudinal field components of the guided modes. On the axis of emission and at maximum intensity, we measure an average polarization impurity of $1.0 \times 10^{-3}$ in relative intensity across the resonance FWHM, and observe impurities below $10^{-4}$ in this range. This constitutes a significant improvement over the ${\sim}10^{-2}$ impurity demonstrated in previous work on integrated devices. Photonic structures allowing high circular polarization purity may assist in realizing high-fidelity control and measurement in atomic quantum systems.

physics.optics↗