arXiv subjects

Daniel Leykam

Publications and source records attributed to Daniel Leykam.

At least 19 recordsLinked to original sources

arXiv2026

Quantum Topological Data Encoding

Many datasets encountered across a wide range of domains possess rich geometric and topological structure that is difficult to capture using conventional vector-based representations. Quantum machine learning offers the possibility of processing high-dimensional data in Hilbert spaces, but its practical success depends critically on how classical data is encoded into quantum states. We introduce \emph{quantum topological data encoding} (QTDE), a general framework for encoding topological information into quantum states via topology-driven quantum evolution. Our method generalises an existing topology-driven quantum encoding framework to higher-dimensional data. We test the proposed method on clique-complexes classification tasks, and provide preliminary evidence that topology-driven quantum representations can capture discriminative information beyond that available through direct comparisons of classical topological descriptors. The proposed quantum representations consistently outperform a baseline based on direct comparisons of the combinatorial Laplacians describing the underlying topological structure. We indicate several areas of application where the framework can be used to provide a more efficient and reliable data representation.

quant-ph↗

arXiv2026

Interference-Protected Subradiance and Bound States in Nested Atomic Arrays

Collective subradiant states in waveguide QED are highly sensitive to disorder, limiting their scalability and robustness. We propose a deterministic approach to engineering atom arrays based on a Minkowski sum construction, generating quasi-disordered structures with built-in correlations. This leads to mode-selective radiative coupling: interactions between dark modes are parametrically suppressed, while bright modes can hybridize. We study the stability of these subradiant and bound-state-like modes against moderate positional disorder. Our work provides a route to robust, analytically controllable subradiance through engineered quasi-disorder, with direct relevance to atom-waveguide and circuit QED experiments.

quant-ph↗

arXiv2025

Quantum Metamorphosis: Programmable Emergence and the Breakdown of Bulk-Edge Dichotomy in Multiscale Systems

Multiscale synergy -- the interplay of a system's distinct characteristic length, time, and energy scales -- is becoming a unifying thread across many contemporary branches of science. Ranging from moir\'e and super-moir\'e materials and cold atoms to DNA-templated superlattices and nested photonic networks, multiscale synergy produces behaviors not obtainable at any single scale alone. Yet a general framework that programs cross-scale interplay to steer spectra, transport, and topology has been missing. Here, we elevate multiscale synergy from a byproduct to a general design principle for emergent phenomena. Specifically, we introduce a scale-programmable framework for hierarchically nested lattices (HNLs) that can host quantum metamorphosis (QuMorph) -- a continuous evolution between system-dependent features governed by a dimensionless tunable parameter $\alpha$ (the relative hopping). To exemplify, we show an HNL, in which as $\alpha$ changes, the spectrum metamorphoses from integer quantum Hall-like to anomalous quantum Hall-like, passing through a cocoon regime with proliferating mini-gaps. This multiscale mixing yields multiple novel phenomena, including hybrid edge-bulk states, scale-dependent topology, topologically embedded flat bands, and isolated edge bands. We propose a feasible photonic implementation using commercially available coupled-resonator arrays, outline spatial-spectral signatures to map QuMorph, and explore applications for multi-timescale nonlinear optics. Our work establishes a scalable and programmable paradigm for engineering multiscale emergent phenomena.

physics.optics↗

arXiv2025

Mie-tronics supermodes and symmetry breaking in nonlocal metasurfaces

It is usually believed that symmetry breaking in photonic systems leads to weaker optical confinement, such as in the case of metasurfaces when bound states in the continuum are replaced by quasi-bound states with lower quality factors (Q factors). Here we show that symmetry breaking can instead enhance light trapping by strengthening in-plane nonlocal coupling pathways. We consider finite-size arrays of optical resonators supporting Mie resonances (a Mie-tronics platform) and employ diffraction and multiple-scattering analyses. We demonstrate that diffractive bands and Mie-tronics supermodes originate from the same underlying Mie resonances but differ fundamentally in their physical nature. Finite arrays exhibit Q-factor enhancement driven by redistributed radiation channels, and reversing the trends predicted by infinite-lattice theories. We reveal that controlled symmetry breaking opens new electromagnetic coupling channels, enabling polarization conversion in nonlocal metasurfaces. These novel findings establish a unified wave-physics platform linking both scattering and diffraction theories. Also, they outline the design principles for multi-functional metasurfaces that exploit nonlocality for advanced light manipulation, computation, and emission control.

physics.optics↗

arXiv2025

Cusp solitons mediated by a topological nonlinearity

Nonlinearity in the Schr\"odinger equation gives rise to rich phenomena such as soliton formation, modulational instability, and self-organization in diverse physical systems. Motivated by recent advances in engineering nonlinear gauge fields in Bose-Einstein condensates, we introduce a nonlinear Schr\"odinger model whose dynamics are dependent on the curvature of the wavefunction intensity and show that this has a direct link to a topological quantity from persistent homology. Our model energetically penalizes or favours the formation of local extrema and we demonstrate through numerical simulations that this topological nonlinearity leads to the emergence of robust, cusp-like soliton structures and supports flat-top beams which do not suffer from conventional modulational instability. These findings suggest that topological nonlinearities could serve as a versatile tool for controlling nonlinear waves in optics and Bose-Einstein condensates.

nlin.PS↗

arXiv2025

Unconventional localization of light with Mie-tronics

Localization of light requires high-Q cavities or spatial disorder, yet the wave nature of light may open novel opportunities. Here we suggest to employ Mie-tronics as a powerful approach to achieve the hybridization of different resonances for the enhanced confinement of light via interference effects. Contrary to a conventional approach, we employ the symmetry breaking in finite arrays of resonators to boost the Q factors by in-plane multiple scattering. Being applied to photonic moire structures, our approach yields a giant enhancement of the Purcell factor via twist-induced coupling between degenerate collective modes. Our findings reveal how finely tuned cooperative scattering can surpass conventional limits, advancing the control of wave localization in many subwavelength systems.

physics.optics↗

arXiv2024

Bulk-hole correspondence and inner robust boundary modes in singular flatband lattices

Topological entities based on bulk-boundary correspondence are ubiquitous, from conventional to higher-order topological insulators, where the protected states are typically localized at the outer boundaries (edges or corners). A less explored scenario involves protected states that are localized at the inner boundaries, sharing the same energy as the bulk states. Here, we propose and demonstrate what we refer to as the bulk-hole correspondence - a relation between the inner robust boundary modes (RBMs) and the existence of multiple "holes" in singular flatband lattices, mediated by the immovable discontinuity of the bulk Bloch wavefunctions. We find that the number of independent flatband states always equals the sum of the number of independent compact localized states and the number of nontrivial inner RBMs, as captured by the Betti number that also counts the hole number from topological data analysis. This correspondence is universal for singular flatband lattices, regardless of the lattice shape and the hole shape. Using laser-written Kagome lattices as a platform, we experimentally observe such inner RBMs, demonstrating their real-space topological nature and robustness. Our results may extend to other singular flatband systems beyond photonics, including non-Euclidean lattices, providing a new approach for understanding nontrivial flatband states and topology in hole-bearing lattice systems.

physics.optics↗

arXiv2024

Data-driven model reconstruction for nonlinear wave dynamics

The use of machine learning to predict wave dynamics is a topic of growing interest, but commonly-used deep learning approaches suffer from a lack of interpretability of the trained models. Here we present an interpretable machine learning framework for analyzing the nonlinear evolution dynamics of optical wavepackets in complex wave media. We use sparse regression to reduce microscopic discrete lattice models to simpler effective continuum models which can accurately describe the dynamics of the wavepacket envelope. We apply our approach to valley-Hall domain walls in honeycomb photonic lattices of laser-written waveguides with Kerr-type nonlinearity and different boundary shapes. The reconstructed equations accurately reproduce the linear dispersion and nonlinear effects including self-steepening and self-focusing. This scheme is proven free of the a priori limitations imposed by the underlying hierarchy of scales traditionally employed in asymptotic analytical methods. It represents a powerful interpretable machine learning technique of interest for advancing design capabilities in photonics and framing the complex interaction-driven dynamics in various topological materials.

physics.optics↗

arXiv2024

Resource-Efficient Hybrid Quantum-Classical Simulation Algorithm

Digital quantum computers promise exponential speedups in performing quantum time-evolution, providing an opportunity to simulate quantum dynamics of complex systems in physics and chemistry. However, the task of extracting desired quantum properties at intermediate time steps remains a computational bottleneck due to wavefunction collapse and no-fast-forwarding theorem. Despite significant progress towards building a Fault-Tolerant Quantum Computer (FTQC), there is still a need for resource-efficient quantum simulators. Here, we propose a hybrid simulator that enables classical computers to leverage FTQC devices and quantum time propagators to overcome this bottleneck, so as to efficiently simulate the quantum dynamics of large systems initialized in an unknown superposition of a few system eigenstates. It features no optimization subroutines and avoids barren plateau issues, while consuming fewer quantum resources compared to standard methods when many time steps are required.

quant-ph↗

arXiv2024

Collective nature of high-Q resonances in finite-size photonic metastructures

We study high quality-factor (high Q) resonances supported by periodic arrays of Mie resonators from the perspectives of both Bloch wave theory and multiple scattering theory. We reveal that, unlike a common belief, the bound states in the continuum (BICs) derived by the Bloch-wave theory do not directly determine the resonance with the highest Q value in large but finite arrays. Higher Q factors appear to be associated with collective resonances formed by nominally guided modes below the light line associated with strong effect of both electric and magnetic multipoles. Our findings offer valuable insights into accessing the modes with higher Q resonances via bonding modes within finite metastructures. Our results underpin the pivotal significance of magnetic and electric multipoles in the design of resonant metadevices and nonlocal flat-band optics. Moreover, our demonstrations reveal that coupled arrays of high-Q microcavities do not inherently result in a stronger light-matter interaction when compared to coupled low-Q nanoresonators. This result emphasizes the critical importance of the study of multiple light-scattering effects in cavity-based systems.

physics.optics↗

arXiv2024

Res-U2Net: Untrained Deep Learning for Phase Retrieval and Image Reconstruction

Conventional deep learning-based image reconstruction methods require a large amount of training data which can be hard to obtain in practice. Untrained deep learning methods overcome this limitation by training a network to invert a physical model of the image formation process. Here we present a novel untrained Res-U2Net model for phase retrieval. We use the extracted phase information to determine changes in an object's surface and generate a mesh representation of its 3D structure. We compare the performance of Res-U2Net phase retrieval against UNet and U2Net using images from the GDXRAY dataset.

eess.IV↗

arXiv2024

Flat band fine-tuning and its photonic applications

Flat bands - single-particle energy bands - in tight-binding networks have attracted attention due to the presence of macroscopic degeneracies and their extreme sensitivity to perturbations. This makes them natural candidates for emerging exotic phases and unconventional orders. The challenging part however is to construct flat band networks, whose existence relies on symmetries and fine-tuning. In this review we consider the recently proposed systematic ways to construct flat band networks based on symmetries or fine-tuning. We then discuss how the fine-tuning constructions can be further extended, adapted or exploited in presence of perturbations, both single-particle and many-body. This strategy has lead to the discovery of non-perturbative metal-insulator transitions, fractal phases, nonlinear and quantum caging and many-body nonergodic quantum models. We discuss what implications these results may have for the design of fine-tuned nanophotonic systems including photonic crystals, nanocavities, and metasurfaces.

physics.optics↗

arXiv2024

Unsupervised learning of quantum many-body scars using intrinsic dimension

Quantum many-body scarred systems contain both thermal and non-thermal scar eigenstates in their spectra. When these systems are quenched from special initial states which share high overlap with scar eigenstates, the system undergoes dynamics with atypically slow relaxation and periodic revival. This scarring phenomenon poses a potential avenue for circumventing decoherence in various quantum engineering applications. Given access to an unknown scar system, current approaches for identification of special states leading to non-thermal dynamics rely on costly measures such as entanglement entropy. In this work, we show how two dimensionality reduction techniques, multidimensional scaling and intrinsic dimension estimation, can be used to learn structural properties of dynamics in the PXP model and distinguish between thermal and scar initial states. The latter method is shown to be robust against limited sample sizes and experimental measurement errors.

quant-ph↗

arXiv2023

A Unified Framework for Trace-induced Quantum Kernels

Quantum kernel methods are promising candidates for achieving a practical quantum advantage for certain machine learning tasks. Similar to classical machine learning, an exact form of a quantum kernel is expected to have a great impact on the model performance. In this work we combine all trace-induced quantum kernels, including the commonly-used global fidelity and local projected quantum kernels, into a common framework. We show how generalized trace-induced quantum kernels can be constructed as combinations of the fundamental building blocks we coin "Lego" kernels, which impose an inductive bias on the resulting quantum models. We relate the expressive power and generalization ability to the number of non-zero weight Lego kernels and propose a systematic approach to increase the complexity of a quantum kernel model, leading to a new form of the local projected kernels that require fewer quantum resources in terms of the number of quantum gates and measurement shots. We show numerically that models based on local projected kernels can achieve comparable performance to the global fidelity quantum kernel. Our work unifies existing quantum kernels and provides a systematic framework to compare their properties.

quant-ph↗

arXiv2023

Identifying topology of leaky photonic lattices with machine learning

We show how machine learning techniques can be applied for the classification of topological phases in leaky photonic lattices using limited measurement data. We propose an approach based solely on bulk intensity measurements, thus exempt from the need for complicated phase retrieval procedures. In particular, we design a fully connected neural network that accurately determines topological properties from the output intensity distribution in dimerized waveguide arrays with leaky channels, after propagation of a spatially localized initial excitation at a finite distance, in a setting that closely emulates realistic experimental conditions.

physics.optics↗

arXiv2023

Shallow quantum circuits for efficient preparation of Slater determinants and correlated states on a quantum computer

Fermionic ansatz state preparation is a critical subroutine in many quantum algorithms such as Variational Quantum Eigensolver for quantum chemistry and condensed matter applications. The shallowest circuit depth needed to prepare Slater determinants and correlated states to date scale at least linearly with respect to the system size $N$. Inspired by data-loading circuits developed for quantum machine learning, we propose an alternate paradigm that provides shallower, yet scalable ${\mathcal{O}}(d \log_2^2N)$ two-qubit gate depth circuits to prepare such states with d-fermions, offering a subexponential reduction in $N$ over existing approaches in second quantization, enabling high-accuracy studies of $d{\ll}{\mathcal{O}}{\left(N / \log_2^2 N\right)}$ fermionic systems with larger basis sets on near-term quantum devices.

quant-ph↗

arXiv2023

Topological data analysis and machine learning

Topological data analysis refers to approaches for systematically and reliably computing abstract ``shapes'' of complex data sets. There are various applications of topological data analysis in life and data sciences, with growing interest among physicists. We present a concise yet (we hope) comprehensive review of applications of topological data analysis to physics and machine learning problems in physics including the detection of phase transitions. We finish with a preview of anticipated directions for future research.

cond-mat.mes-hall↗

arXiv2023

Landscape approximation of low energy solutions to binary optimization problems

We show how the localization landscape, originally introduced to bound low energy eigenstates of disordered wave media and many-body quantum systems, can form the basis for hardware-efficient quantum algorithms for solving binary optimization problems. Many binary optimization problems can be cast as finding low-energy eigenstates of Ising Hamiltonians. First, we apply specific perturbations to the Ising Hamiltonian such that the low energy modes are bounded by the localization landscape. Next, we demonstrate how a variational method can be used to prepare and sample from the peaks of the localization landscape. Numerical simulations of problems of up to $10$ binary variables show that the localization landscape-based sampling can outperform QAOA circuits of similar depth, as measured in terms of the probability of sampling the exact ground state.

quant-ph↗