arXiv subjects

Yaron Oz

Publications and source records attributed to Yaron Oz.

At least 37 records · Page 2Linked to original sources

arXiv2025

Accurate Modeling of Directional Couplers with Oxide Cladding: Bridging Simulation and Experiment

Directional couplers are a fundamental building block in integrated photonics, particularly in quantum applications and optimization-based design where precision is critical. Accurate functionality is crucial to ensure reliable operation within classical and quantum circuits. However, discrepancies between simulations and measurements are frequently observed. These inaccuracies can compromise the performance and scalability of integrated photonic systems, underscoring the critical need for advanced, precise simulation methods that bridge the gap between design and implementation. In this work, we show that this discrepancy can be mainly attributed to density changes in the oxide cladding. We conduct a systematic study involving experimental optical measurements, numerical simulations, and direct electron microscopy imaging to investigate this discrepancy in directional couplers. We find that the impact of cladding density variations on performance increases as feature gaps shrink. By incorporating these effects into our simulations using a novel and physically motivated Effective Trench Medium Model (ETMM), we achieve highly accurate reproduction of experimental measurements. We quantify the effects of cladding density variations on the SU(2) symmetry parameters that govern light propagation in directional couplers. This insight is crucial for advancing the precision of compact device fabrication, enabling reliable simulation of photonic integrated devices.

physics.optics↗

arXiv2025

Robust photonic quantum gates with large number of waveguide segments

Realizing quantum information processors is challenged by errors and noise across all platforms. While composite segmentation schemes have been proposed in many systems, their application to photonic quantum gates in dual-rail configurations has only recently been demonstrated. However, prior research has been limited to a small number of segments, full noise correlation, and has overlooked the inherent power loss in such designs. Here, we study the fidelity and power loss of composite designs for photonic quantum gates with a high number of segments of varying geometrical widths. Using numerical simulations, we analyze the relationship between gate performance and the number of waveguide segments, accounting for statistical error correlations and variances. Beyond effectively reducing the errors, an asymptotic scaling pattern of quantum gate fidelity and power loss is observed as the number of segments increases. This analysis is examined in Silicon and Lithium Niobate platforms, addressing practical implementation challenges. Our findings demonstrate that optimized multi-segment waveguide geometrical designs significantly enhance the robustness and efficiency of photonic quantum gates, paving the way for more reliable quantum information processors.

quant-ph↗

arXiv2024

Robust Characterization of Integrated Photonics Directional Couplers

Directional couplers are essential components in integrated photonics. Given their widespread use, accurate characterization of directional couplers is crucial for ensuring optimal performance. However, it is challenging due to the coupling between fibers and waveguides, which is highly sensitive to alignment and fabrication imperfections. To address these challenges, we propose a novel direct measurement technique that offers greater robustness to variations in optical interfaces, while bypassing extinction ratio measurements. Our method enables a broadband and precise characterization of the directional couplers' splitting ratio. We experimentally validate this approach, demonstrate its robustness against intentional errors, and compare it to a naive direct measurement method. Furthermore, our technique is generalized to measure the amplitude of any general 2x2 unitary circuit, providing valuable insights for designing and testing a wide range of photonic integrated devices.

physics.optics↗

arXiv2024

Complexity Measure Diagnostics of Ergodic to Many-Body Localization Transition

We introduce new diagnostics of the transition between the ergodic and many-body localization phases, which are based on complexity measures defined via the probability distribution function of the Lanczos coefficients of the tri-diagonalized Hamiltonian. We use these complexity measures to analyze the power-law random banded matrix model as a function of the correlation strength and show that the moments and the entropy of the distribution diagnose the ergodic to many-body transition, as well as the distinctive feature of the phases concerning the memory of the initial conditions.

hep-th↗

arXiv2024

Gravitationally dominated instantons and instability of dS, AdS and Minkowski spaces

We study the decay of the false vacuum in the regime where the quantum field theory analysis is not valid, since gravitational effects become important. This happens when the height of the barrier separating the false and the true vacuum is large, and it has implications for the instability of de Sitter, Minkowski and anti-de Sitter vacua. We carry out the calculations for a scalar field with a potential coupled to gravity, and work within the thin-wall approximation, where the bubble wall is thin compared to the size of the bubble. We show that the false de Sitter vacuum is unstable, independently of the height of the potential and the relative depth of the true vacuum compared to the false vacuum. The false Minkowski and anti-de Sitter vacua can be stable despite the existence of a lower energy true vacuum. However, when the relative depth of the true and false vacua exceeds a critical value, which depends on the potential of the false vacuum and the height of the barrier, then the false Minkowski and anti-de Sitter vacua become unstable. We calculate the probability for the decay of the false de Sitter, Minkowski and anti-de Sitter vacua, as a function of the parameters characterizing the field potential.

hep-th↗

arXiv2024

Direct and inverse cascades scaling in real shell models of turbulence

Shell models provide a simplified mathematical framework that captures essential features of incompressible fluid turbulence, such as the energy cascade and scaling of the fluid observables. We perform a precision analysis of the direct and inverse cascades in shell models of turbulence, where the velocity field is a real-valued function. We calculate the leading hundred anomalous scaling exponents, the marginal probability distribution functions of the velocity field at different shells, as well as the correlations between different shells. We find that the structure functions in both cascades exhibit a linear Kolomogorov scaling in the inertial range. We argue that the underlying reason for having no intermittency, is the strong correlations between the velocity fields at different shells. We analyze the tails of velocity distribution functions, which offer new insights to the structure of fluid turbulence.

physics.flu-dyn↗

arXiv2024

Input Space Mode Connectivity in Deep Neural Networks

We extend the concept of loss landscape mode connectivity to the input space of deep neural networks. Mode connectivity was originally studied within parameter space, where it describes the existence of low-loss paths between different solutions (loss minimizers) obtained through gradient descent. We present theoretical and empirical evidence of its presence in the input space of deep networks, thereby highlighting the broader nature of the phenomenon. We observe that different input images with similar predictions are generally connected, and for trained models, the path tends to be simple, with only a small deviation from being a linear path. Our methodology utilizes real, interpolated, and synthetic inputs created using the input optimization technique for feature visualization. We conjecture that input space mode connectivity in high-dimensional spaces is a geometric effect that takes place even in untrained models and can be explained through percolation theory. We exploit mode connectivity to obtain new insights about adversarial examples and demonstrate its potential for adversarial detection. Additionally, we discuss applications for the interpretability of deep networks.

cs.LG↗

arXiv2024

Boundary Liouville Conformal Field Theory in Four Dimensions

Higher dimensional Euclidean Liouville conformal field theories (LCFTs) consist of a log-correlated real scalar field with a background charge and an exponential potential. We analyse the LCFT on a four-dimensional manifold with a boundary. We extend to the boundary, the conformally covariant GJMS operator and the ${\cal Q}$-curvature term in the LCFT action and classify the conformal boundary conditions. Working on a flat space with plate boundary, we calculate the dimensions of the boundary conformal primary operators, the two- and three-point functions of the displacement operator and the boundary conformal anomaly coefficients.

hep-th↗

arXiv2024

Extremal fixed points and Diophantine equations

The coupling constants of fixed points in the $ε$ expansion at one loop are known to satisfy a quadratic bound due to Rychkov and Stergiou. We refer to fixed points that saturate this bound as extremal fixed points. The theories which contain such fixed points are those which undergo a saddle-node bifurcation, entailing the presence of a marginal operator. Among bifundamental theories, a few examples of infinite families of such theories are known. A necessary condition for extremality is that the sizes of the factors of the symmetry group of a given theory satisfy a specific Diophantine equation, given in terms of what we call the extremality polynomial. In this work we study such Diophantine equations and employ a combination of rigorous and probabilistic estimates to argue that these infinite families constitute rare exceptions. The Pell equation, Falting's theorem, Siegel's theorem, and elliptic curves figure prominently in our analysis. In the cases we study here, more generic classes of multi-fundamental theories saturate the Rychkov-Stergiou bound only in sporadic cases or in limits where they degenerate into simpler known examples.

hep-th↗

arXiv2024

Turbulence Scaling from Deep Learning Diffusion Generative Models

Complex spatial and temporal structures are inherent characteristics of turbulent fluid flows and comprehending them poses a major challenge. This comprehesion necessitates an understanding of the space of turbulent fluid flow configurations. We employ a diffusion-based generative model to learn the distribution of turbulent vorticity profiles and generate snapshots of turbulent solutions to the incompressible Navier-Stokes equations. We consider the inverse cascade in two spatial dimensions and generate diverse turbulent solutions that differ from those in the training dataset. We analyze the statistical scaling properties of the new turbulent profiles, calculate their structure functions, energy power spectrum, velocity probability distribution function and moments of local energy dissipation. All the learnt scaling exponents are consistent with the expected Kolmogorov scaling. This agreement with established turbulence characteristics provides strong evidence of the model's capability to capture essential features of real-world turbulence.

physics.flu-dyn↗

arXiv2024

Classifying Overlapping Gaussian Mixtures in High Dimensions: From Optimal Classifiers to Neural Nets

We derive closed-form expressions for the Bayes optimal decision boundaries in binary classification of high dimensional overlapping Gaussian mixture model (GMM) data, and show how they depend on the eigenstructure of the class covariances, for particularly interesting structured data. We empirically demonstrate, through experiments on synthetic GMMs inspired by real-world data, that deep neural networks trained for classification, learn predictors which approximate the derived optimal classifiers. We further extend our study to networks trained on authentic data, observing that decision thresholds correlate with the covariance eigenvectors rather than the eigenvalues, mirroring our GMM analysis. This provides theoretical insights regarding neural networks' ability to perform probabilistic inference and distill statistical patterns from intricate distributions.

stat.ML↗

arXiv2024

The Underlying Scaling Laws and Universal Statistical Structure of Complex Datasets

We study universal traits which emerge both in real-world complex datasets, as well as in artificially generated ones. Our approach is to analogize data to a physical system and employ tools from statistical physics and Random Matrix Theory (RMT) to reveal their underlying structure. We focus on the feature-feature covariance matrix, analyzing both its local and global eigenvalue statistics. Our main observations are: (i) The power-law scalings that the bulk of its eigenvalues exhibit are vastly different for uncorrelated normally distributed data compared to real-world data, (ii) this scaling behavior can be completely modeled by generating Gaussian data with long range correlations, (iii) both generated and real-world datasets lie in the same universality class from the RMT perspective, as chaotic rather than integrable systems, (iv) the expected RMT statistical behavior already manifests for empirical covariance matrices at dataset sizes significantly smaller than those conventionally used for real-world training, and can be related to the number of samples required to approximate the population power-law scaling behavior, (v) the Shannon entropy is correlated with local RMT structure and eigenvalues scaling, is substantially smaller in strongly correlated datasets compared to uncorrelated ones, and requires fewer samples to reach the distribution entropy. These findings show that with sufficient sample size, the Gram matrix of natural image datasets can be well approximated by a Wishart random matrix with a simple covariance structure, opening the door to rigorous studies of neural network dynamics and generalization which rely on the data Gram matrix.

cs.LG↗

arXiv2024

Holographic Turbulence From a Random Gravitational Potential

We study the turbulent dynamics of a relativistic (2 + 1)-dimensional fluid placed in a stochastic gravitational potential. We demonstrate that the dynamics of the fluid can be obtained using a dual holographic description realized by an asymptotically Anti-de Sitter black brane driven by a random boundary metric. Using the holographic duality we study the inverse cascade energy power spectrum of the fluid and show that it is compatible with that of a compressible fluid flow. We calculate the local energy dissipation and the local fluid velocity distribution which provide other measures of the holographic fluid turbulence.

hep-th↗

arXiv2024

Anomalous scaling in shell model turbulence

Shell model turbulence is a simplified mathematical framework that captures essential features of incompressible fluid turbulence such as the energy cascade, intermittency and anomalous scaling of the fluid observables. We perform a precision analysis of shell model of a complex velocity field in the steady state turbulent regime, including a calculation of the leading hundred anomalous scaling exponents, the probability distribution function of the magnitude and phase of the velocity and the correlations among them at different shells. We analyze the tail of velocity distribution function and find that the high moments exhibit a linear scaling that differs from Kolomogorov's. We explain the origin of this asymptotic scaling that offers a new insight to the structure of fluid turbulence.

physics.flu-dyn↗

arXiv2023

RG Flows and Fixed Points of $O(N)^r$ Models

By means of $ε$ and large $N$ expansions, we study generalizations of the $O(N)$ model where the fundamental fields are tensors of rank $r$ rather than vectors, and where the global symmetry (up to additional discrete symmetries and quotients) is $O(N)^r$, focusing on the cases $r\leq 5$. Owing to the distinct ways of performing index contractions, these theories contain multiple quartic operators, which mix under the RG flow. At all large $N$ fixed points, melonic operators are absent and the leading Feynman diagrams are bubble diagrams, so that all perturbative fixed points can be readily matched to full large $N$ solutions obtained from Hubbard-Stratonovich transformations. The family of fixed points we uncover extend to arbitrary higher values of $r$, and as their number grows superexponentially with $r$, these theories offer a vast generalization of the critical $O(N)$ model. We also study sextic $O(N)^r$ theories, whose large $N$ limits are obscured by the fact that the dominant Feynman diagrams are not restricted to melonic or bubble diagrams. For these theories the large $N$ dynamics differ qualitatively across different values of $r$, and we demonstrate that the RG flows possess a numerous and diverse set of perturbative fixed points beginning at rank four.

hep-th↗

arXiv2023

The Universal Statistical Structure and Scaling Laws of Chaos and Turbulence

Turbulence is a complex spatial and temporal structure created by the strong non-linear dynamics of fluid flows at high Reynolds numbers. Despite being an ubiquitous phenomenon that has been studied for centuries, a full understanding of turbulence remained a formidable challenge. Here, we introduce tools from the fields of quantum chaos and Random Matrix Theory (RMT) and present a detailed analysis of image datasets generated from turbulence simulations of incompressible and compressible fluid flows. Focusing on two observables: the data Gram matrix and the single image distribution, we study both the local and global eigenvalue statistics and compare them to classical chaos, uncorrelated noise and natural images. We show that from the RMT perspective, the turbulence Gram matrices lie in the same universality class as quantum chaotic rather than integrable systems, and the data exhibits power-law scalings in the bulk of its eigenvalues which are vastly different from uncorrelated classical chaos, random data, natural images. Interestingly, we find that the single sample distribution only appears as fully RMT chaotic, but deviates from chaos at larger correlation lengths, as well as exhibiting different scaling properties.

cond-mat.stat-mech↗

arXiv2023

Segmented Composite Design of Robust Single-Qubit Quantum Gates

Error mitigation schemes and error-correcting codes have been the center of much effort in quantum information processing research over the last few decades. While most of the successful proposed schemes for error mitigation are perturbative in the noise and assume deterministic systematic errors, studies of the problem considering the full noise and errors distribution are still scarce. In this work, we introduce an error mitigation scheme for robust single-qubit unitary gates based on composite segmented design, which accounts for the full distribution of the physical noise and errors in the system. We provide two optimization approaches to construct these robust segmented gates: perturbative and non-perturbative, that addresses all orders of errors. We demonstrate our scheme in the photonics realm for the dual-rail directional couplers realization. We show that the 3-segmented composite design for the fundamental single-qubits unitary operations reduces the error by an order of magnitude for a realistic distribution of errors, and that the two approaches are compatible for small errors. This is shown to significantly reduce the overhead of modern error correction codes. Our methods are rather general and can be applied to other realizations of quantum information processing units.

quant-ph↗

arXiv2023

Correlation thresholds for effective composite pulse quantum error mitigation

Composite pulse segmentation has emerged as a promising error mitigation technique for a wide range of physical systems. In recent years, composite schemes were applied as mitigation strategies for quantum information processing and quantum computing. However, most of these strategies assume full error correlation between segments, which can result in gates with worse fidelity performance compared to non-composite gates. In our research, we investigate how error correlations impact the fidelity of quantum gates within the composite segmentation framework. In our study, we prove the existence of a critical correlation threshold, above which the composite pulse method significantly enhances both the mean value and variance of the fidelity. To gain deeper insights, we analyze various properties of the threshold in the realm of integrated photonics, including the effects of geometrical variations and the limit where the number of segments approaches infinity. We numerically explore diverse scenarios, showcasing different aspects of the critical threshold within the photonic quantum gates framework. These findings contribute open new pathways of error mitigation strategies and their implications in quantum information processing.

quant-ph↗