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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

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7152229 · Sep 202519922001200920172026
48 results for quantum R-matrices

This paper describes a method to obtain state model parameters for an infinite series of Links-Gould link invariants LG^{m,n}, based on quantum R matrices associated with the (\dot{0}_m | \dotα_n) representations of the quantum superalgebras U_q[gl(m|n)]. Explicit details of the state models for the cases n=1 and m=1,2…

2000-04-27abs ↗pdf ↗

In this paper, we reconstruct Kuperberg's G2G_2 web space. We introduce a new web (a trivalent diagram) and new relations between Kuperberg's web diagrams and the new diagram. Using the G2G_2 webs, we define crossing formulas corresponding to R-matrices associated to some G2G_2 irreducible representations and calculate…

2015-03-29abs ↗pdf ↗

The purpose of this paper is to establish a connection between various subjects such as dynamical r-matrices, Lie bialgebroids, and Lagrangian subalgebras. Our method relies on the theory of Dirac structures developed in dg-ga/9508013 and dg-ga/9611001. In particular, we give a new method of classifying dynamical r-mat…

1999-03-19abs ↗pdf ↗

We define invariants for a framed link equipped with a SL2 local system in its complement and additional combinatorial data based on the theory of representations of stated skein algebras at roots of unity of punctured bigons and the geometric interpretation of their centers. The gauge invariance of the link invariant …

2019-07-03abs ↗pdf ↗

We review the Reshetikhin-Turaev approach to construction of non-compact knot invariants involving R-matrices associated with infinite-dimensional representations, primarily those made from Faddeev's quantum dilogarithm. The corresponding formulas can be obtained from modular transformations of conformal blocks as thei…

2015-10-19abs ↗pdf ↗

In the present paper we discuss the cabling procedure for the colored HOMFLY polynomial. We describe how it can be used and how one can find all the quantities such as projectors and R\mathcal{R}-matrices, which are needed in this procedure. The constructed matrix forms of the projectors and the fundamental $\mathcal{…

2013-07-08abs ↗pdf ↗

The paper defines projective structures for Lie bialgebras and Poisson-Lie groups.

problem Defining projective analogues of Lie bialgebras and Poisson-Lie groups.
method Introducing projective tensor products and adapting classical notions to these structures.
result Every quasi-triangular projective r-matrix gives rise to a projective Banach Lie bialgebra.

Given a rack Q and a ring A, one can construct a Yang-Baxter operator c_Q: V tensor V --> V tensor V on the free A-module V = AQ by setting c_Q(x tensor y) = y tensor x^y for all x,y in Q. In answer to a question initiated by D.N. Yetter and P.J. Freyd, this article classifies formal deformations of c_Q in the space of…

2004-09-13abs ↗pdf ↗

We construct a Poisson isomorphism between the formal Poisson manifolds g^* and G^*, where g is a finite dimensional quasitriangular Lie bialgebra. Here g^* is equipped with its Lie-Poisson (or Kostant-Kirillov-Souriau) structure, and G^* with its Poisson-Lie structure. We also quantize Poisson-Lie dynamical r-matrices…

2004-12-17abs ↗pdf ↗

In Levin-Wen (LW) models, a wide class of exactly solvable discrete models, for two dimensional topological phases, it is relatively easy to describe only single fluxon excitations, but not the charge and dyonic as well as many-fluxon excitations. To incorporate charged and dyonic excitations in (doubled) topological p…

2015-02-11abs ↗pdf ↗

Direct proof of Alexander polynomial scaling for L-shaped representations.

problem Proving scaling property of Alexander polynomials for specific representations.
method Direct use of Reshetikhin-Turaev formalism to compute R-matrices.
result Normalized Alexander polynomial for one-hook representations scales with qRq^{|R|}.

Quantizes Chern-Simons invariant for tangle exteriors.

problem Geometric quantization of Chern-Simons invariant for tangles.
method Defining a sequence of invariants ZNψ\mathcal{Z}_{N}^ψ using modules over quantum sl2\mathfrak{sl}_{2} and holonomy RR-matrices.
result Directly recovers Chern-Simons invariant when N=1N = 1.

We study the local structure of Lie bialgebroids at regular points. In particular, we classify all transitive Lie bialgebroids. In special cases, they are connected to classical dynamical rr-matrices and matched pairs induced by Poisson group actions

2002-10-07abs ↗pdf ↗

Several authors have recently studied virtual knots and links because they admit invariants arising from R-matrices. We prove that every virtual link is uniquely represented by a link L in S X I, a thickened, compact, oriented surface S, such that the link complement (S X I) - L has no essential vertical cylinder.

2002-08-05abs ↗pdf ↗

We show that the exterior powers of the matrix valued random walk invariant of string links, introduced by Lin, Tian, and Wang, are isomorphic to the graded components of the tangle functor associated to the Alexander Polynomial by Ohtsuki divided by the zero graded invariant of the functor. Several resulting propertie…

2014-06-10abs ↗pdf ↗

The paper generalizes knot invariants and their connections to quivers and ideals.

problem Understanding knot complements and their invariants.
method Generalizing FKF_K invariants, knots-quivers correspondence, and AA-polynomials; associating FKF_K to branch of AA-polynomial; quiver generating series; RR-matrices; quantum aa-deformed AA-polynomial; 3d-5d theory.
result Explicit expressions for FKF_K invariants and their quiver representations for several simple knots.

For an arbitrary identity L=R between compositions of maps L and R on tensors of vector spaces V, a general construction of a 2-cocycle condition is given. These 2-cocycles correspond to those obtained in deformation theories of algebras. The construction is applied to a canceling pairings and copairings, with explicit…

2008-02-15abs ↗pdf ↗

Somewhat unexpectedly, the study of the family of twisted knots revealed a hidden structure behind exclusive Racah matrices Sˉ\bar S, which control non-associativity of the representation product in a peculiar channel RRˉRRR\otimes \bar R \otimes R \longrightarrow R. These Sˉ\bar S are simultaneously symmetric and orthogo…

2019-06-24abs ↗pdf ↗

Construction of (colored) knot polynomials for double-fat graphs is further generalized to the case when "fingers" and "propagators" are substituting R-matrices in arbitrary closed braids with m-strands. Original version of arXiv:1504.00371 corresponds to the case m=2, and our generalizations sheds additional light on …

2015-06-01abs ↗pdf ↗

Classifies Lie bialgebras using Darboux families.

problem Classifying real four-dimensional indecomposable coboundary Lie bialgebras.
method Introducing Darboux families to classify Lie bialgebras geometrically.
result Classification of coboundary Lie bialgebras on real four-dimensional indecomposable Lie algebras.

Quantum ML promises faster data analysis but faces trainability challenges.

problem Challenges in training quantum machine learning models.
method Review of current methods and applications of quantum neural networks and quantum deep learning.
result Opportunities for quantum advantage in quantum machine learning.

QGAA learns latent quantum states, reducing errors in quantum data generation.

problem Learning latent representations for quantum data generation.
method Quantum Generative Adversarial Autoencoder (QGAA) combining QAE and QGAN.
result Average errors in energies for H2 and LiH are 0.02 Ha and 0.06 Ha respectively, demonstrating QGAA's potential.

Quantum machine learning uses quantum cross entropy to minimize loss, but measurement loss affects this process.

problem Quantum machine learning's loss minimization through cross entropy is affected by measurement outcomes.
method Defined quantum cross entropy, proved its lower bounds, and investigated its relation to quantum fidelity and likelihood.
result Quantum cross entropy is lower-bounded by negative log-likelihood when derived from quantum data, but measurement outcomes can cause loss.

Quantum Earth Mover's distance improves stability and efficiency in quantum learning.

problem Quantum learning's loss landscapes often lead to poor local minima and gradients.
method Introduced the quantum Earth Mover's (EM) distance and proposed a quantum Wasserstein generative adversarial network (qWGAN).
result The quantum EM distance makes quantum learning more stable and efficient.

Quantum machine learning models can approximate any continuous function.

problem Theoretical understanding of quantum feature maps in machine learning.
method Proving universal approximation property of quantum machine learning models in quantum-enhanced feature spaces.
result Quantum machine learning models are universal approximators of continuous functions.

Quantum autoencoders allow for reducing the amount of resources in a quantum computation by mapping the original Hilbert space onto a reduced space with the relevant information. Recently, it was proposed to employ approximate quantum adders to implement quantum autoencoders in quantum technologies. Here, we carry out …

2018-07-27abs ↗pdf ↗

We define quantum exterior product wedge_h and quantum exterior differential d_h on Poisson manifolds, of which symplectic manifolds are an important class of examples. Quantum de Rham cohomology is defined as the cohomology of d_h. We also define quantum Dolbeault cohomology. Quantum hard Lefschetz theorem is proved. …

1998-06-30abs ↗pdf ↗

VQAs use classical optimization to train quantum circuits, promising quantum advantage.

problem High computational cost of quantum simulations and solving large-scale problems.
method Variational Quantum Algorithms (VQAs) use classical optimizers to train parametrized quantum circuits.
result VQAs are a promising strategy for obtaining quantum advantage.

Quantum machine learning tackles large datasets with randomized measurements.

problem Efficiently process large, high-dimensional datasets on quantum computers.
method Randomized measurements to scale linearly with dataset size and quadratic for post-processing.
result Substantial speed-up for noisy quantum computers, enabling image classification.

Quantum states can be learned efficiently using gentle measurements.

problem Efficiently learning quantum states with minimal measurements.
method Introducing α-LGM measurements and proving strong quantum DPI.
result The number of states needed for accurate learning is of order 1/(ε^2 α^2).