Comprehensive Validation of Replica Symmetry Breaking via Quantum Annealing: From Ground States to Topological Collapse
- URL: http://arxiv.org/abs/2511.06403v1
- Date: Sun, 09 Nov 2025 14:33:22 GMT
- Title: Comprehensive Validation of Replica Symmetry Breaking via Quantum Annealing: From Ground States to Topological Collapse
- Abstract summary: We extend Giorgio Parisi's exact solution of the Sherrington-Kirkpatrick spin glass to 4000 spins.<n>We probe both the emergence and breakdown of replica symmetry breaking.<n>This comprehensive validation establishes quantum advantage for probing fundamental statistical mechanics in complex systems.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Giorgio Parisi's exact solution of the Sherrington-Kirkpatrick spin glass, recognized with the 2021 Nobel Prize in Physics, revealed revolutionary hierarchical organization in disordered systems, yet systematic validation has remained computationally intractable beyond $N \sim 100$ spins, and the topological limits of this complexity remain unexplored. Here we leverage quantum annealing to extend ground-state computations to 4000 spins and systematically probe both the emergence and breakdown of replica symmetry breaking. Three independent measurements validate core RSB predictions: ground-state energies converge to Parisi's value $E_\infty/N = -0.7633$ with predicted $N^{-2/3}$ finite-size corrections; chaos exponent $\theta = 0.51 \pm 0.02$ confirms mean-field square-root scaling ($R^2 = 0.989$); and state-space overlap distribution exhibits broad continuous structure ($\sigma_q = 0.19$) characteristic of hierarchical landscape organization. We then investigate RSB robustness by introducing controlled network dilution via the Blume-Capel model with vacancy formation. Remarkably, hierarchical complexity remains invariant under 36\% dilution, proving RSB is a topological property of network connectivity rather than spin density. Beyond a critical threshold in the range $0.8 < D_c < 0.9$, the hierarchy collapses discontinuously as the system undergoes complete conversion to the all-vacancy state within a narrow parameter window an abrupt avalanche-driven transition where independent-vacancy mean-field theory correctly predicts the energy scale but fails to capture the cooperative dynamics. This comprehensive validation across thermodynamics, universality, landscape geometry, and topological limits establishes quantum advantage for probing fundamental statistical mechanics in complex systems relevant to neural networks, optimization, and materials science.
Related papers
- Kibble-Zurek Dynamics in Two-dimensional Frustrated Systems with a Neural Foundation-state Subspace Method [0.6524460254566904]
We introduce a Neural Foundation-state Subspace (NFS) method for near-adiabatic dynamics.<n>A foundation neural-network quantum state represents the ground-state manifold along the driving path.<n>We validate the method on the two-dimensional transverse-field Ising model.
arXiv Detail & Related papers (2026-09-03T16:58:54Z) - Spectral Dynamics of Semantic Drift in Clinical Multi-Agent Language Model Networks [0.0]
We show that localized dense cliques confine hallucinated data, preventing global consensus and forcing the system toward a permanent entropy saturation threshold.<n>By introducing dynamic spectral monitoring operating at an $mathcalO(N3)$ time complexity, we present a mathematically rigorous technique to ensure global state diffusion.
arXiv Detail & Related papers (2026-07-23T20:20:45Z) - Quantum tunneling Mpemba effect [0.0]
Quantum tunneling Mpemba effect is investigated within a continuous one-dimensional symmetric double-well potential open to external environmental sinks.<n>We mathematically prove that the non-monotonic behavior of the first non-trivial even-parity spectral coefficient, $a_2(T_i)$, is a universal topological property born from quantum statistical mechanics.
arXiv Detail & Related papers (2026-07-04T12:23:59Z) - How to Scale Mixture-of-Experts: From muP to the Maximally Scale-Stable Parameterization [45.69980208532521]
We take a principled step toward resolving this gap by analyzing three different scaling regimes.<n>For each regime, we develop a novel Dynamical Mean Field Theory (DMFT) description of the limiting training dynamics of MoEs.<n>We show that the resulting $$P prescription does not reliably induce monotonic improvement with scale or robust learning-rate transfer.
arXiv Detail & Related papers (2026-05-13T23:32:00Z) - Hysteretic squashed entanglement in many-body quantum systems [42.085941481155295]
Entanglement in many-body quantum systems is distributed across spatial regions.<n>We propose hysteretic entanglement $T_sq$, a conditional entanglement that measures the genuine quantum correlations between two subregions.<n>We show that $T_sq$ can detect genuine quantum correlations across both adjacent and long-range subsystems.
arXiv Detail & Related papers (2026-03-10T17:00:49Z) - The Inductive Bias of Convolutional Neural Networks: Locality and Weight Sharing Reshape Implicit Regularization [57.37943479039033]
We study how architectural inductive bias reshapes the implicit regularization induced by the edge-of-stability phenomenon in gradient descent.<n>We show that locality and weight sharing fundamentally change this picture.
arXiv Detail & Related papers (2026-03-05T04:50:51Z) - Unsupervised Discovery of Intermediate Phase Order in the Frustrated $J_1$-$J_2$ Heisenberg Model via Prometheus Framework [0.0]
We apply the Prometheus variational autoencoder framework to explore the $J_1$-$J$ phase diagram.<n>We identify the structure factor $S(,)$ and $S(,)$ as the dominant order parameters.<n>This work establishes a scalable pathway for applying machine learning to frustrated quantum systems.
arXiv Detail & Related papers (2026-02-25T00:44:51Z) - Phase Transition for Budgeted Multi-Agent Synergy [41.486076708302456]
Multi-agent systems can improve reliability, yet under a fixed inference budget they often help, saturate, or even collapse.<n>We develop a minimal and calibratable theory that predicts these regimes from three binding constraints of modern agent stacks.
arXiv Detail & Related papers (2026-01-24T05:32:50Z) - Multi-Scale Negative Coupled Information Systems (MNCIS): A Unified Spectral Topology Framework for Stability in Turbulence, AI, and Biology [1.4213973379473657]
This work generalizes the Multi-Scale Negative Coupled Information System (MNCIS) framework.<n>Global stability requires an active topological operator -- Adaptive Spectral Negative Coupling (ASNC) -- functioning as a state-dependent high-pass filter.<n>ASNC acts as a global-enstrophy adaptive sub-grid scale (SGS) model, stabilizing the inviscid limit and preserving the Kolmogorov $-5/3$ inertial range without artificial hyper-viscosity.<n>Our results suggest that the MNCIS framework provides a base-independent topological condition for distinguishing viable complex systems from those collapsing into thermal equilibrium
arXiv Detail & Related papers (2026-01-06T21:11:33Z) - Balancing Centralized Learning and Distributed Self-Organization: A Hybrid Model for Embodied Morphogenesis [0.0]
We investigate how to couple a learnable brain-like'' controller to a cell-like'' Gray--Scott substrate to steer pattern formation with minimal effort.<n>A compact convolutional policy is embedded in a differentiable PyTorch reaction--diffusion simulator.
arXiv Detail & Related papers (2025-11-13T09:05:27Z) - Pauli Propagation: Simulating Quantum Spin Dynamics via Operator Complexity [1.299941371793082]
We introduce a scalable Pauli propagation approach that evolves local observables directly in the Heisenberg picture.<n>We prove the number of non-zero Pauli coefficients scales quadratically in Trotter steps, establishing the compressibility of Heisenberg-evolved operators.<n>Results establish an observable-centric simulator whose cost is governed by operator complexity rather than entanglement.
arXiv Detail & Related papers (2025-10-25T14:24:12Z) - Theta-term in Russian Doll Model: phase structure, quantum metric and BPS multifractality [45.88028371034407]
We investigate the phase structure of the deterministic and disordered versions of the Russian Doll Model (RDM)<n>We find the pattern of phase transitions in the global charge $Q(theta,gamma)$, which arises from the BA equation.<n>We conjecture that the Hamiltonian of the RDM model describes the mixing in particular 2d-4d BPS sector of the Hilbert space.
arXiv Detail & Related papers (2025-10-23T17:25:01Z) - Constructive Lyapunov Functions via Topology-Preserving Neural Networks [0.0]
ONN achieves order-optimal performance on convergence rate, edge efficiency, and computational complexity.<n> Empirical validation on 3M-node semantic networks demonstrates 99.75% improvement over baseline methods.<n>ORTSF integration into transformers achieves 14.7% perplexity reduction and 2.3 faster convergence.
arXiv Detail & Related papers (2025-10-10T17:46:52Z) - Non-Hermitian Quantum Metrology Enhancement and Skin Effect Suppression in PT-Symmetric Bardeen-Cooper-Schrieffer Chains [0.0]
We outline a theoretical framework for quantum metrology in non-Hermitian systems.<n>Through biorthogonal quantum Fisher information analysis, we identify two distinct regimes.<n>NHSE suppresses sensitivity exponentially, while $mathcalPT$-symmetry enables Heisenberg-limited enhancement.
arXiv Detail & Related papers (2025-08-06T18:54:45Z) - Quantum sensor network metrology with bright solitons [39.58317527488534]
General Heisenberg Limit (GHL) characterizes fundamental limitations for unknown parameter measurement and estimation accuracy.<n>Three-mode soliton Josephson junction (TMSJJ) system as a three mode extension for the soliton Josephson junction (SJJ) bosonic model.<n>Our findings open new prospects for quantum network sensorics with atomtronic circuits.
arXiv Detail & Related papers (2025-07-21T08:01:36Z) - Non-stabilizerness of Neural Quantum States [41.94295877935867]
We introduce a methodology to estimate non-stabilizerness or "magic", a key resource for quantum complexity, with Neural Quantum States (NQS)<n>We study the magic content in an ensemble of random NQS, demonstrating that neural network parametrizations of the wave function capture finite non-stabilizerness besides large entanglement.
arXiv Detail & Related papers (2025-02-13T19:14:15Z) - KPZ scaling from the Krylov space [83.88591755871734]
Recently, a superdiffusion exhibiting the Kardar-Parisi-Zhang scaling in late-time correlators and autocorrelators has been reported.
Inspired by these results, we explore the KPZ scaling in correlation functions using their realization in the Krylov operator basis.
arXiv Detail & Related papers (2024-06-04T20:57:59Z) - Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks [54.177130905659155]
Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks.
In this paper, we study a suitable function space for over- parameterized two-layer neural networks with bounded norms.
arXiv Detail & Related papers (2024-04-29T15:04:07Z) - Programmable order by disorder effect and underlying phases through dipolar quantum simulators [0.0874967598360817]
We study two different quantum simulators composed of molecules with dipole-dipole interaction.
First result provides knowledge upon the quantum order by disorder effect of the $S=1/2$ system.
Next, we study the dipolar bosonic model with tilted polar angle which can be realized through a quantum simulator.
arXiv Detail & Related papers (2023-10-02T02:04:58Z) - Complexity of frustration: a new source of non-local non-stabilizerness [0.0]
We advance the characterization of complexity in quantum many-body systems by examining $W$-states embedded in a spin chain.
Our work reveals that $W$-states/frustrated ground states display a non-local degree of complexity that can be harvested as a quantum resource.
arXiv Detail & Related papers (2022-09-21T17:59:40Z) - Predicting the Stability of Hierarchical Triple Systems with
Convolutional Neural Networks [68.8204255655161]
We propose a convolutional neural network model to predict the stability of hierarchical triples.
All trained models are made publicly available, allowing to predict the stability of hierarchical triple systems $200$ times faster than pure $N$-body methods.
arXiv Detail & Related papers (2022-06-24T17:58:13Z) - Quantum anomalous Hall phase in synthetic bilayers via twistless
twistronics [58.720142291102135]
We propose quantum simulators of "twistronic-like" physics based on ultracold atoms and syntheticdimensions.
We show that our system exhibits topologicalband structures under appropriate conditions.
arXiv Detail & Related papers (2020-08-06T19:58:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.