arXiv research

Study connects Riemann-Finsler geometry to Lorentz-violating scalar fields.

A nonlinear wave alternative for the standard Black-Scholes option-pricing model is presented. The adaptive-wave model, representing 'controlled Brownian behavior' of financial markets, is formally defined by adaptive nonlinear Schrödinger (NLS) equations, defining the option-pricing wave function in terms of the stock…

In the current paper the Lagrangian of a classical, relativistic point particle is obtained whose conjugate momentum satisfies the dispersion relation of a quantum wave packet that is subject to Lorentz violation based on a particular coefficient of the nonminimal Standard-Model Extension (SME). The properties of this …

Develops support theorem for analytic transforms in tomography.

We develop a definitive physical-space scattering theory for the scalar wave equation on Kerr exterior backgrounds in the general subextremal case |a|<M. In particular, we prove results corresponding to "existence and uniqueness of scattering states" and "asymptotic completeness" and we show moreover that the resulting…

Global existence and boundedness proved for quasilinear wave equations on Kerr black holes.

Investigates quantum vs classical portfolio optimization of 60 stocks.

The abstract explores a new wave equation linking quantum mechanics and complex adaptive systems.

The commuting vector fields approach, devised for strichartz estimates in [13], was developed for proving the local well-posedness in the Sobolev spaces $H^s$ with $s>2+\frac{2-\sqrt{3}}{2}$ for general quasi-linear wave equation in ${\mathbb R}^{1+3}$ by Klainerman and Rodnianski. Via this approach they obtained the l…

Quantum computer optimizes investment portfolios, outperforming traditional methods.

D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest, but have been used for few real-world computations. Machine learning has been identified as an area where quantum annealing may be useful. Here, we show that the D-Wave 2X can be effectively used as part of an…

Study analyzes 3,171 stocks to pick efficient portfolios using quantum and classical solvers.

Quantum annealing solves matrix factorization for large datasets.

Quantum computer helps optimize stock portfolios.

Adaptive wave model for financial option pricing is proposed, as a high-complexity alternative to the standard Black--Scholes model. The new option-pricing model, representing a controlled Brownian motion, includes two wave-type approaches: nonlinear and quantum, both based on (adaptive form of) the Schrödinger equatio…

We consider the motion of small bodies in general relativity. The key result captures a sense in which such bodies follow timelike geodesics (or, in the case of charged bodies, Lorentz-force curves). This result clarifies the relationship between approaches that model such bodies as distributions supported on a curve, …

D-Wave computers struggle with sampling Boltzmann distributions efficiently.

D-Wave quantum annealing fails to improve sampling quality from RBMs compared to Gibbs sampling.

Deep learning wave function improves quantum chemistry calculations.

Regression algorithm uses quantum annealer for sparse inference in lattice QCD simulations.

Quantum knots and knotted zeros linked through complex plane mappings.

Quantum annealer improves classification of handwritten digits.

Recently, a novel adaptive wave model for financial option pricing has been proposed in the form of adaptive nonlinear Schrödinger (NLS) equation [Ivancevic a], as a high-complexity alternative to the linear Black-Scholes-Merton model [Black-Scholes-Merton]. Its quantum-mechanical basis has been elaborated in [Ivancevi…

Beginning with several basic hypotheses of quantum mechanics, we give a new quantum model in econophysics. In this model, we define wave functions and operators of the stock market to establish the Schrödinger equation for the stock price. Based on this theoretical framework, an example of a driven infinite quantum wel…

Deep neural network predicts molecular wave functions in minimal basis.

Federated Learning improves mobile data privacy by training classifiers without sharing raw data.

We design non-singular cloaks enabling objects to scatter waves like objects with smaller size and very different shapes. We consider the Schrodinger equation which is valid e.g. in the contexts of geometrical and quantum optics. More precisely, we introduce a generalized non-singular transformation for star domains, a…

Tensor networks and RNNs are equivalent, improving wave function encoding.

The mixture of Gaussian distributions, a soft version of k-means , is considered a state-of-the-art clustering algorithm. It is widely used in computer vision for selecting classes, e.g., color, texture, and shapes. In this algorithm, each class is described by a Gaussian distribution, defined by its mean and covarianc…

The Madelung transform connects quantum mechanics and hydrodynamics.

D-Wave hybrid quantum-classical portfolio optimization shows classical decomposition is key, not quantum sampling.

Estimates classical potential from stock price data using quantum mechanics.

Decentralized learning ensures stability in online queuing systems with packet rates above 1.

Quantum SVMs outperform classical ones on limited data.

Neural-Network Quantum States have been recently introduced as an Ansatz for describing the wave function of quantum many-body systems. We show that there are strong connections between Neural-Network Quantum States in the form of Restricted Boltzmann Machines and some classes of Tensor-Network states in arbitrary dime…

SparseIDS uses RL to significantly reduce packet inspection in IDSs.

Study explores D-Wave QA for RBM training, finds modest benefits but no significant improvements.

Quantum annealer speeds up RBM training for image classification.

Quantum computing techniques improve graph analysis and community detection.

TG-PSM morphs encrypted traffic to reduce traffic analysis accuracy.

Proposes a quantum-inspired algorithm for selecting representative data subsets.

Quantum model explains stock market price behavior.

This paper is the second part of a study of the quantum free particle on spherical and hyperbolic spaces by making use of a curvature-dependent formalism. Here we study the analogues, on the three-dimensional spherical and hyperbolic spaces, $S_\k^3$ ($κ>0$) and $H_\k^3$ ($κ<0$), to the standard {\itshape spherical wav…

Quantum time evolution exhibits rich physics, attributable to the interplay between the density and phase of a wave function. However, unlike classical heat diffusion, the wave nature of quantum mechanics has not yet been extensively explored in modern data analysis. We propose that the Laplace transform of quantum tra…

Paper tackles dynamic portfolio optimization using quantum and quantum-inspired methods.

Lossy compression of statistical data using quantum annealing.

Study wave functions in complex Chern-Simons theory, finding integrality and rational points.

In this paper we examine a new class of five dimensional (5D) exact solutions in extra dimension gravity possessing Lie algebroid symmetry. The constructions provide a motivation for the theory of Clifford nonholonomic algebroids elaborated in Ref. hep-th/0501217. Such Einstein-Dirac spacetimes are parametrized by gene…