arXiv subjects

Peiyuan Huang

Publications and source records attributed to Peiyuan Huang.

10 recordsLinked to original sources

arXiv2026

Geometric Superconducting Diode Effect in an NbN Nanoring

Superconducting diodes, which exhibit nonreciprocal critical currents, are promising building blocks for low-power cryogenic electronics and superconducting circuits. Existing superconducting diode platforms commonly rely on Josephson junctions, multilayer heterostructures, ferromagnetic elements, gate-difined structures. Here, we demonstrate a geometrically induced superconducting diode effect realized in a structurally minimal, single-materials NbN nanoring, where inversion-symmetry breaking is introduced solely by the asymmetric geometry. The device exhibits pronounced and polarity-switchable critical-current nonreciprocity. Systematic magnetic-field and temperature-dependent measurements reveal that, at low fields, the applied magnetic field redistributes the critical current asymmetrically between opposite bias directions without significantly reducing the overall superconducting current-carrying capability. Moreover, the maximal nonreciprocity and diode efficiency exhibit distinct temperature dependence: the maximal diode efficiency follows the evolution of the energy gap, whereas the maximal nonreciprocity is more closely associated with the superfluid density. These results establish asymmetric superconducting nanorings as a minimal geometric platform for studying nonreciprocal superconducting transport and provide a simple design principle for future superconducting electronics.

cond-mat.supr-con↗

arXiv2026

Sensing-Native Over-the-Air Federated Learning

Over-the-air federated learning (FL) leverages the superposition property of multiple-access channels to enable communication-efficient distributed model training. Existing integrated sensing, communication, and computation (ISCC)-enabled over-the-air FL systems typically require dedicated resources for the sensing module, inevitably compromising FL performance due to resource competition. In this paper, we propose a sensing-native over-the-air FL framework that explores built-in distributed wireless sensing capability with zero overhead per model aggregation. Specifically, the high-dimensional local gradient signals possessing favorable autocorrelation property are concurrently leveraged for target distance estimation, while the gradient statistics already required for over-the-air FL serve as a ready-made gateway to deliver locally-sensed results to the edge server for cooperative localization. To combat inter-device interference, channel fading, and communication noise, we put forth a robust trilateration-based target positioning method building upon an efficient matched-filtering-based distance estimation. Then, by explicitly characterizing the impact of imperfect model aggregation and noisy gradient-statistics transmission on the sensing-native over-the-air FL convergence, we develop a statistics-aware communication-learning co-design approach. We first derive the closed-form optimal power budgets allocated to local gradients and their statistics, based on which an efficient successive convex approximation method is proposed for receiver beamforming optimization. Simulation results show that the proposed framework simultaneously achieves superior learning and sensing performance compared to representative baselines.

eess.SP↗

arXiv2026

Scalable First-Order Interior Point Trust Region Algorithms for Linearly Constrained Optimization

Computing approximate Karush--Kuhn--Tucker (KKT) points for constrained nonconvex programs is a fundamental problem in mathematical programming. Interior-point trust-region (IPTR) methods are particularly attractive for such problems because they maintain strictly feasible iterates throughout the iterative process and converge to a first-order and second-order KKT solution. Their scalability, however, is limited by the repeated computation of trust-region search directions. In this paper, we propose an approximate first-order IPTR framework that addresses this bottleneck by replacing exact trust-region subproblem solves with an approximate projector maintained through low-rank updates. The resulting method preserves feasibility and the global convergence guarantees of standard IPTR schemes while substantially reducing the per-iteration cost. We further extend the framework to obtain approximate second-order KKT points using only first-order information by integrating a gradient-based negative-curvature routine, thus avoiding explicit Hessian computations. We conduct numerical experiments to demonstrate the scalability of our approximate first-order IPTR framework in large-scale settings, where it achieves up to a $2.48\times$ speedup over the existing first-order IPTR algorithm.

cs.DS↗

arXiv2026

Nanoscale electrothermal-switch superconducting diode for electrically programmable superconducting circuits

Superconducting diodes enable dissipationless directional transport, yet achieving electrical tunability and scalability remains a major challenge for circuit-level integration. Here, we demonstrate an electrothermal-switch superconducting diode in which a gate-controlled nanoscale hotspot dynamically breaks inversion symmetry in a superconducting nanowire. This mechanism gives rise to two coexisting nonreciprocal transport regimes-one associated with a nonreciprocal superconducting-to-normal transition and the other with ratchet-like vortex dynamics-both originating from the same electrothermal-switch process. The diode exhibits efficiencies up to 42% and 60% for the two regimes, respectively, and can be electrically switched on, off, or reversed in polarity in situ by applying a small gate current. These capabilities enable programmable superconducting circuits that realize electrically reconfigurable full-wave and half-wave rectification. The lithography-compatible design, high performance, and gate-controlled functionality establish a scalable platform for programmable superconducting electronics and hybrid quantum systems.

cond-mat.supr-con↗

arXiv2025

Quantum Algorithms for Bandits with Knapsacks with Improved Regret and Time Complexities

Bandits with knapsacks (BwK) constitute a fundamental model that combines aspects of stochastic integer programming with online learning. Classical algorithms for BwK with a time horizon $T$ achieve a problem-independent regret bound of ${O}(\sqrt{T})$ and a problem-dependent bound of ${O}(\log T)$. In this paper, we initiate the study of the BwK model in the setting of quantum computing, where both reward and resource consumption can be accessed via quantum oracles. We establish both problem-independent and problem-dependent regret bounds for quantum BwK algorithms. For the problem-independent case, we demonstrate that a quantum approach can improve the classical regret bound by a factor of $(1+\sqrt{B/\mathrm{OPT}_\mathrm{LP}})$, where $B$ is budget constraint in BwK and $\mathrm{OPT}_{\mathrm{LP}}$ denotes the optimal value of a linear programming relaxation of the BwK problem. For the problem-dependent setting, we develop a quantum algorithm using an inexact quantum linear programming solver. This algorithm achieves a quadratic improvement in terms of the problem-dependent parameters, as well as a polynomial speedup of time complexity on problem's dimensions compared to classical counterparts. Compared to previous works on quantum algorithms for multi-armed bandits, our study is the first to consider bandit models with resource constraints and hence shed light on operations research.

quant-ph↗

arXiv2024

Topological geometric frustration in a cube-surface artificial spin ice

Artificial spin ices provide a controlled platform for investigating diverse physical phenomena, such as geometric frustration, magnetic monopoles, and phase transitions, via deliberate design. Here, we introduce a novel approach by developing artificial spin ice on the surfaces of a three-dimensional cube, which leads to emergent geometric frustration mediated by topologically protected domain walls, distinct from its flat counterparts. These domain walls connect vertices at the corners of cube that acting as intrinsic topological defects. Utilizing Monte Carlo simulations, we observe robust, topologically protected correlations among the intrinsic topological defects, regardless of their spatial separation. Our findings demonstrate that three-dimensional surfaces can unveil emergent properties absent in flat architectures.

cond-mat.mes-hall↗

arXiv2024

Magnetic nonreciprocity in a hybrid device of asymmetric artificial spin-ice-superconductors

Controlling the size and distribution of potential barriers within a medium of interacting particles can unveil unique collective behaviors and innovative functionalities. In this study, we introduce a unique superconducting hybrid device using a novel artificial spin ice structure composed of asymmetric nanomagnets. This structure forms a distinctive superconducting pinning potential that steers unconventional motion of superconducting vortices, thereby inducing a magnetic nonreciprocal effect, in contrast to the electric nonreciprocal effect commonly observed in superconducting diodes. Furthermore, the polarity of the magnetic nonreciprocity is in-situ reversible through the tunable magnetic patterns of artificial spin ice. Our findings demonstrate that artificial spin ice not only precisely modulates superconducting characteristics but also opens the door to novel functionalities, offering a groundbreaking paradigm for superconducting electronics.

cond-mat.supr-con↗

arXiv2024

Toroidic phase transitions in a direct-kagome artificial spin ice

Ferrotoroidicity, the fourth form of primary ferroic order, breaks both space and time inversion symmetry. So far, direct observation of ferrotoroidicity in natural materials remains elusive, which impedes the exploration of ferrotoroidic phase transitions. Here, we overcome the limitations of natural materials using an artificial nanomagnet system that can be characterized at the constituent level and at different effective temperatures. We design a nanomagnet array as to realize a direct-kagome spin ice. This artificial spin ice exhibits robust toroidal moments and a quasi-degenerate ground state with two distinct low-temperature toroidal phases: ferrotoroidicity and paratoroidicity. Using magnetic force microscopy and Monte Carlo simulation, we demonstrate a phase transition between ferrotoroidicity and paratoroidicity, along with a crossover to a non-toroidal paramagnetic phase. Our quasi-degenerate artificial spin ice in a direct-kagome structure provides a model system for the investigation of magnetic states and phase transitions that are inaccessible in natural materials.

cond-mat.mes-hall↗

arXiv2024

Unconventional superconducting diode effects via antisymmetry and antisymmetry breaking

Symmetry-breaking plays a pivotal role in unlocking intriguing properties and functionalities in material systems. For example, the breaking of spatial and temporal symmetries leads to a fascinating phenomenon of superconducting diode effect. However, generating and precisely controlling the superconducting diode effect poses significant challenges. Here, we take a novel route with deliberate manipulation of magnetic charge potentials to realize unconventional superconducting flux-quantum diode effects. We achieve this through suitably tailored nanoengineered arrays of nanobar magnets on top of a superconducting thin film. We demonstrate the vital roles of inversion antisymmetry and its breaking in evoking unconventional superconducting effects-a magnetically symmetric diode effect and an odd-parity magnetotransport effect. These effects are non-volatilely controllable through in-situ magnetization switching of the nanobar magnets. Our findings promote the use of antisymmetry (breaking) for initiating unconventional superconducting properties, paving the way for exciting prospects and innovative functionalities in superconducting electronics.

cond-mat.supr-con↗

arXiv2022

Eigenmodes of twisted spin-waves in a thick ferromagnetic nanodisk

Magnetic vortex is topologically nontrivial and commonly found in ferromagnetic nanodisks. So far, three classes spin-wave eigenmodes, i.e., gyrotropic, azimuthal and radial modes, have been identified in ferromagnetic nanodisks. Here, using micromagnetic simulation and analytical calculation, we reveal twisted spin-wave modes in a thick permalloy (Ni0.8Fe0.2) nanodisk. The twisted spin-waves carry topological charges, which sign depends on the core polarity of the magnetic vortex in the nanodisk. By applying rotating magnetic fields at one end of the sample, we observe continuous generation of twisted spin-waves that have characteristic spiral phase front and carry topological charge l = 1, -1, 2 and -2. The dispersion relation of twisted spin-waves is derived analytically and the result is in good agreement with micromagnetic numerical calculations.

cond-mat.mes-hall↗