arXiv subjects

Sunkyu Yu

Publications and source records attributed to Sunkyu Yu.

At least 19 recordsLinked to original sources

arXiv2026

Learning Nonlocal Interactions in Large-Scale Heterogeneous Metamaterials through Scientific Machine Learning

In multiscale systems, nonlocal interactions among spatially distributed constituents can unlock collective, potentially emergent behavior irreducible to individual constituent responses. Beyond characterization, nonlocality in multiscale systems is increasingly viewed as a design element across fracture mechanics, wave physics, and disordered systems. Existing nonlocality theories provide rigorous descriptions and mechanistic insight, but often require prescribed analytic forms, range, symmetries, or periodicity. Machine learning offers a data-driven route to learn interactions, yet principled approaches that tie the network design to physical theory remain nascent. Here we present domain-decomposition attention neural operator (DANO), which learns configuration-dependent interactions in heterogeneous systems governed by linear field equations from field data alone. Through a case study in large-scale optical metasurfaces, we demonstrate DANO's full-field prediction under geometric heterogeneity and across drive conditions, small-to-large domain transfer, and multifunctional inverse design, with learned interactions validated through physics-based interrogation. As an AI-for-Science tool, it supports interaction discovery and scalable, customizable design of nonlocal multiscale systems, including virtual-reality optics.

cs.CE↗

arXiv2026

Characterizing universal topological phases beyond spatial embedding

Although Hamiltonians govern wave-matter interactions, conventional characterizations of topological phases of matter require more than the Hamiltonian alone, relying on global invariants defined on closed reciprocal-space manifolds or local commutation relations with position operators. This spatial dependence precludes a unique characterization of topological phases on an abstract platform, whose Hamiltonian can be defined independently of its spatial embedding. Here we establish a framework for characterizing universal topological phases, grounded in the invariance of topological features under arbitrary Hamiltonian-preserving deformations. We construct a Hamiltonian-based spatial embedding that groups a system's constituents according to their connectivity. This embedding, which reproduces the results of conventional topological diagnostics in established systems, enables a coherent extension of topological phases to complex networks using the Hamiltonian alone, further highlighting exceptional robustness in small-world networks. Our results reshape the conceptual foundations of topological physics by decoupling topological characterization from prescribed physical platforms.

cond-mat.mes-hall↗

arXiv2026

Observation of a topological defect state in the quantum Rabi model

Synthetic lattices built from hybrid qubit-oscillator systems provide a platform for exploring topology, with multiple physical degrees of freedom enabling control over lattice geometry. A single spin-oscillator pair, described by the quantum Rabi model, provides a minimal system supporting a topological defect state on a lattice formed from the oscillator's infinite ladder of number states. However, the realization of the defect state and its robustness remain experimentally unexplored. Here we realize a topological defect state of the quantum Rabi model in a single trapped $^{171}\mathrm{Yb}^{+}$ ion. As a coupling phase varies, reconstructed phase-space distributions yield centroid trajectories with windings of one and zero in two regimes, linking discrete-lattice topology to the geometry of the oscillator's continuous position-momentum space. The measured spin response to drive-amplitude modulation relates the stability of spin polarization to energy gaps and allowed transitions. Together, these results establish a minimal quantum system for realizing and probing topological defect states.

quant-ph↗

arXiv2026

Topological insulators on complex networks

The landscape of topological insulators has expanded beyond its traditional domain of periodic lattices with short-range hopping. Related studies have explored topological phenomena under non-Euclidean geometries, disordered structures, and long-range hopping, progressively narrowing the gap between topological phases of matter and complex networks. Here we demonstrate that genuine complex networks, far beyond periodic lattices and their conventional variants, can themselves host topological insulating phases. By developing a nonconflicting design framework for multiple topological objectives, we realize topological insulators in high-degree Hofstadter lattices and across regular, small-world, and random networks. This generalization uncovers distinct network-specific features, including an expanded range of attainable topological characteristics, a degree-dependent transition from Hofstadter-type to Haldane-type Chern insulators, and, most notably, the superior robustness and reconfigurability of small-world topological insulators. Our graph-theoretic framework establishes network complexity as a resource for realizing high-capacity and reconfigurable topological insulators.

cond-mat.mes-hall↗

arXiv2026

Mapping open quantum dynamics onto graphs

Graph-theoretic frameworks have been widely employed in quantum physics to address the high-dimensional complexity of quantum systems. Although open quantum dynamics incorporates system-bath coupling via numerous interacting operators, it has been formulated algebraically with a partial set of jump operators or statistically universal reservoirs, leaving the underlying connectivity structure largely unexplored. Here, we propose a universal graph-theoretic framework for Markovian quantum dynamics. The framework maps open quantum dynamics onto two uniquely defined graphs, where the quantum master equation is rigorously interpreted as the average wave characteristic of operator-valued signals across the graphs. Applying this framework to the open quantum Rabi model, we demonstrate an open-system generalization of Fock-state lattices, characterize graph-topological signatures of dissipation, and classify the weak-to-ultrastrong coupling transition. Building on these representations, graph pruning reveals the backbone of open quantum dynamics, which enables superior graph neural-network learning. Our results bridge graph theory and open quantum dynamics, achieving efficient data-driven analysis of high-dimensional complexity.

quant-ph↗

arXiv2026

Magnetic graphs for cavity quantum electrodynamics

Strengthening light-matter coupling has become a central challenge in cavity quantum electrodynamics (QED), enabling ultrafast gate operations, qubit protection, and deterministic nonlinear optics. As the coupling increases, even the simplest configuration, a two-level atom interacting with a quantized field, requires careful treatment, as exemplified by the gauge-invariant quantum Rabi model (QRM). Here we propose a magnetic graph model for single-atom cavity QED, which enables the interpretation of quantum dynamics across the ultrastrong coupling regime through graph connectivity. We demonstrate that the generalized QRM maps onto a complex bipartite graph of identical sites under Floquet boundary conditions. This framework captures the crossover from weak to deep-strong coupling via a single metric: the cost of disconnecting a nonmagnetic subgraph. We examine the mechanism underlying this connectivity transition, establishing phase frustration induced by subgraph topology as the primary driver. Scalable to many-body systems, this approach bridges graph theory and cavity QED, revealing highly complex-graph dynamics even in the simplest setting.

quant-ph↗

arXiv2026

Multi-objective design of photon blockade for bright single-photon sources

High-quality single-photon sources, realized through saturable emitters, photon blockade, or heralded pair generation, are indispensable building blocks for photonic quantum platforms. Although these mechanisms suppress multiphoton emission through distinct principles typically captured by analytical models, their practical implementation is constrained by conflicting requirements for purity, brightness, and indistinguishability, which must be balanced within high-dimensional design landscapes. Here, we propose a computational framework for optimizing competing metrics of single-photon sources. Building on a Liouville-space adjoint formulation that efficiently evaluates multiple objectives in Markovian open quantum systems, we develop a Jacobian-based update, which ensures first-order monotonic reduction of multi-objective costs. By incorporating simulated annealing to escape gradient-vanishing plateaus, our framework achieves a design success rate of nearly 60 % for photon blockade with g2(0) smaller than 0.1 and theoretically bounded brightness across a broad parameter space, without any analytical guidance. This framework provides a general recipe for multi-objective design of open quantum systems.

quant-ph↗

arXiv2026

Tailoring Defects in Photonic Time Crystals for Coherent Energy Control

Recent advances in time-varying photonics have revealed new degrees of freedom for manipulating optical states, arising from the distinctive nature of the temporal axis: causality and open-system dynamics. A representative example is photonic time crystals (PTCs) characterized by discrete time-translational symmetry, which exhibit space-analogous yet distinct phenomena, such as momentum gaps and amplifying-decaying Floquet-mode pairs. Although PTCs enable optical-energy amplification beyond conventional gain media, their application as programmable energy-functional devices remains challenging. Here, we propose a design framework for tailoring optical energy via defective PTCs. By optimizing defect permittivity and duration using analytic gradients of time transfer matrices, we realize prescribed coherent energy amplification and suppression. We show that a single defect enables continuous energy tailoring, while revealing an intrinsic asymmetry between amplification and suppression due to the inherently amplifying nature of the momentum gap. Extending the framework to coupled defects expands the design space and markedly improves suppression, establishing temporal-defect engineering as a route to programmable coherent energy control.

physics.optics↗

arXiv2026

Task-specific programming of chaos in neural circuits

Chaotic dynamics have emerged as a versatile resource for neuromorphic and probabilistic computing, enabling high-dimensional nonlinear processing and classical analogues of quantum randomness. Exploiting chaos for computation requires task-dependent control over complexity, as demonstrated in reservoir computing, random-number generation, and probabilistic inference. Existing approaches have focused on tuning element-level parameters, leaving the collective, many-body origin of chaos largely unexplored as a design freedom. Here, we demonstrate programmable chaotic dynamics for task-specific reservoir computing. Using a continuous-time neural-circuit model, we show that tuning network topology drives an ordered-to-chaotic transition, accompanied by transitions in correlation timescales, stability characteristics, and signal propagation. By jointly controlling element-level properties and network topology, we establish a unified chaos-latency phase diagram, revealing that small-world connectivity enables low-latency on-off switching of chaos via edge rewiring. Supported by distinct reservoir-computing benchmarks across various topological regimes, our results demonstrate that network topology serves as a reconfigurable parameter for task-specific computation and tunable randomness.

nlin.CD↗

arXiv2026

Topological heavy-tailed networks

Although two-dimensional periodic structures have functioned as the primary platform for exploring topological phenomena, recent advances have substantially expanded this research boundary to include more intricate, aperiodic structures: quasicrystals, fractals, non-Euclidean lattices, and disordered materials. A network-based perspective not only offers a unified framework for classifying these diverse platforms based on their network connectivity but also unveils unexplored regimes of topological phenomena in complex networks. Here, we implement topological heavy-tailed networks, as an example of high-degree complex networks exhibiting topological phases. By developing a tight-binding model for the Apollonian network and a deterministic algorithm to assign nontrivial gauge fields to this aperiodic geometry, we compute the magnetic-flux-dependent energy spectrum: the Apollonian butterfly. Using spectral localizers, we characterize the topological features of the Apollonian butterfly, whose sensitivity is governed by lower-degree nodes, analogous to the controllability of complex networks. Our framework bridges topological physics and network science, introducing a connectivity-driven paradigm for the control of topological waves.

cond-mat.mes-hall↗

arXiv2026

Fully programmable slow light based on a spinor representation of generalized coupled-resonator-induced transparency

Electromagnetically induced transparency (EIT), arising from quantum interference in coherently driven atomic systems, has inspired a variety of photonic analogues, such as coupled-resonator-induced transparency (CRIT) built on the quantum-state modelling using resonators. Although CRIT serves as a building block for slow light in photonic integrated circuits, recent advances in topological photonics motivate a further generalization of both EIT and CRIT using gauge-field degrees of freedom. Here, we propose generalized CRIT via a spinor representation with dual-channel gauge fields, enabling fully programmable CRIT featuring dynamical spectral engineering. We generalize the traditional EIT framework by introducing a spinor representation of bright- and dark-mode resonances, yielding a unified description of design parameters through universal unitary operations. Implementing a coupled-resonator building block that accesses the entire design space through dual-channel gauge fields, we demonstrate a programmable slow-light band in a one-dimensional CRIT lattice. These results address urgent needs in optical interconnects, such as tunable delay lines, reconfigurable synchronization, and linear frequency conversion.

physics.optics↗

arXiv2026

Non-Hermitian stealthy hyperuniformity

Symmetry-driven wave physics in open systems, exemplified by parity-time (PT) symmetry, has extended the landscape of crystalline phases in materials science to include gain-loss media. Given the growing interest in engineering disorder for wave manipulation, such non-Hermitian crystals motivate the extension of non-Hermitian frameworks into the realm of correlated disorder. Here, we propose hyperuniformity and stealthiness in non-Hermitian systems as a generalization of PT-symmetric crystals to correlated disorder. We extend the scattering-microstructure correspondence to open systems, formulating non-Hermitian hyperuniformity and stealthiness that encompass their Hermitian counterparts. This approach, incorporating a statistical crystallography framework for non-Hermitian materials, demonstrates that real-imaginary cross-correlations of the material potential are irrelevant for achieving hyperuniformity but are essential for characterizing stealthiness, revealing unidirectional scattering phases that are inaccessible in Hermitian materials and in non-Hermitian crystals. By analysing the microstructural statistics of the resulting materials, our results, building on non-Hermitian wave physics, establish a connection to materials science, encompassing conventional descriptors of correlated disorder.

physics.optics↗

arXiv2026

Scalable Memory Sharing in Photonic Quantum Memristors for Reservoir Computing

Although photons are robust, room-temperature carriers well suited to quantum machine learning, the absence of photon-photon interactions hinder the realization of memory functionalities that are critical for capturing long-range context. Recently, measurement-based implementations of photonic quantum memristors (PQMRs) have enabled tunable non-Markovian responses. However, their memory remains confined to local elements, in contrast to biological or artificial networks where memory is shared across the system. Here, we propose a scalable PQMR network that enables measurement-based memory sharing. Each memristive node updates its internal state using the history of its own and neighbouring quantum states, thereby realizing distributed memory. By modelling each node as a photonic quantum memtransistor, we demonstrate pronounced enhancements in both classical and quantum hysteresis at the device level, as well as enhanced network-level quantum hysteresis. Implemented as a quantum reservoir, the architecture achieves improved Fashion-MNIST classification accuracy and confidence via increased data separability. Our approach paves the way toward high-capacity quantum machine learning using memristive devices compatible with linear-optical quantum computing.

quant-ph↗

arXiv2026

Phase-Space Topology in a Single-Atom Synthetic Dimension

We investigate topological features in the synthetic Fock-state lattice (FSL) of a single-atom system described by the quantum Rabi model. By diagonalizing the Hamiltonian, we identify a zero-energy defect state localized at a domain wall of the FSL, whose spin polarization is topologically protected. To address the challenge of applying band topology to the FSL, we introduce a physically motivated and directly measurable topological invariant based on phase-space geometry-the phase-space winding number. We show that the Zak phase, computed using a phase-space parameter, is related to the invariant. This quantized geometric phase reflects the spin polarization of the defect state, demonstrating a bulk-boundary correspondence. The resulting phase-space topology reveals the emergence of single-atom dressed states with contrasting properties-topologically protected spin states and driving-tunable bosonic states. Our results establish phase-space topology as a novel framework for exploring topological physics in single-atom synthetic dimensions, uncovering quantum-unique topological protection distinct from classical analogs.

quant-ph↗

arXiv2025

Heterogeneous networks for phase-sensitive engineering of optical disordered materials

Heterogeneous networks provide a universal framework for extracting subsystem-level features of a complex system, which are critical in graph colouring, pattern classification, and motif identification. When abstracting physical systems into networks, distinct groups of nodes and links in heterogeneous networks can be decomposed into different modes of multipartite networks, allowing for a deeper understanding of both intra- and inter-group relationships. Here, we develop heterogeneous network modelling of wave scattering to engineer multiphase random heterogeneous materials. We devise multipartite network decomposition determined by material phases, which is examined using uni- and bi-partite network examples for two-phase multiparticle systems. We show that the directionality of the bipartite network governs the phase-sensitive alteration of microstructures. The proposed modelling enables a network-based design to achieve phase-sensitive microstructural features, while almost preserving the overall scattering response. With examples of designing quasi-isoscattering stealthy hyperuniform materials, our results provide a general recipe for engineering multiphase materials for wave functionalities.

physics.optics↗

arXiv2025

Hypergraph modelling of wave scattering to speed-up material design

Hypergraphs offer a generalized framework for understanding complex systems, covering group interactions of different orders beyond traditional pairwise interactions. This modelling allows for the simplified description of simultaneous interactions among multiple elements in coupled oscillators, graph neural networks, and entangled qubits. Here, we employ this generalized framework to describe wave-matter interactions for material design acceleration. By devising the set operations for multiparticle systems, we develop the hypergraph model, which compactly describes wave interferences among multiparticles in scattering events by hyperedges of different orders. This compactness enables an evolutionary algorithm with O(N1/2) time complexity and approximated accuracy for designing stealthy hyperuniform materials, which is superior to traditional methods of O(N) scaling. By hybridizing our hypergraph evolutions to the conventional collective-coordinate method, we preserve the original accuracy, while achieving substantial speed-up in approaching near the optimum. Our result paves the way toward scalable material design and compact interpretations of large-scale multiparticle systems.

physics.optics↗

arXiv2025

Heavy-tailed open quantum systems reveal long-lived and ultrasensitive coherence

Understanding random open quantum systems is critical for characterizing the performance of large-scale quantum devices and exploring macroscopic quantum phenomena. Various features in these systems, including spectral distributions, gap scaling, and decoherence, have been examined by modelling randomness under the central limit theorem. Here, we investigate random open quantum systems beyond the central limit theorem, focusing on heavy-tailed system-environment interactions. By extending the Ginibre unitary ensemble, we model system-environment interactions to exhibit a continuous transition from light-tailed to heavy-tailed distributions. This generalized configuration reveals unique properties-gapless spectra, Pareto principle governing dissipation, orthogonalization, and quasi-degeneracies-all linked to the violation of the central limit theorem. The synergy of these features challenges the common belief-the tradeoff between stability and sensitivity-through the emergence of long-lived and ultrasensitive quantum coherences that exhibit an enhancement of two orders of magnitude compared to predictions under the central limit theorem. The result, which is based on heavy-tailedness of open quantum systems, provides highly desirable platforms for quantum sensing applications.

quant-ph↗

arXiv2024

Nonlinear unitary circuits for photonic neural networks

Photonics has unlocked the potential for energy-efficient acceleration of deep learning. Most approaches toward photonic deep learning have diligently reproduced traditional deep learning architectures using photonic platforms, separately implementing linear-optical matrix calculations and nonlinear activations via electro-optical conversion, optical nonlinearities, and signal-encoded materials. Here we propose a concept of nonlinear unitary photonic circuits to achieve the integration of linear and nonlinear expressivity essential for deep neural networks. We devise a building block for two-dimensional nonlinear unitary operations, featuring norm-preserving mappings with nonconservative inner products, which enables the construction of high-dimensional nonlinear unitary circuits. Using deep nonlinear unitary circuits, we demonstrate exponential growth in trajectory length and near-complete coverage of the output space, both of which are essential for deep learning. Along with neuroevolutionary learning examples for the regression of a nonconvex function, our results pave the way to photonic neural networks with highly expressive inference and stable training.

physics.optics↗