Generalised Geometric Phase: Mathematical Aspects
- URL: http://arxiv.org/abs/2312.14522v1
- Date: Fri, 22 Dec 2023 08:42:11 GMT
- Title: Generalised Geometric Phase: Mathematical Aspects
- Authors: Vivek M. Vyas
- Abstract summary: An operator generalisation of the notion of geometric phase has been recently proposed purely based on physical grounds.
We provide a mathematical foundation for its existence, while uncovering new geometrical structures in quantum systems.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: An operator generalisation of the notion of geometric phase has been recently
proposed purely based on physical grounds. Here we provide a mathematical
foundation for its existence, while uncovering new geometrical structures in
quantum systems. While probing the average of any observable it is found that a
quantum system exhibits different ray spaces and associated fibre bundle
structures. The generalised geometric phase is understood as (an)holonomy of a
connection over these fibre bundles. The underlying ray spaces in general are
found to be pseudo-Kahler manifolds, and its symplectic structure gets
manifests as the generalised geometric phase.
Related papers
- Gaussian Entanglement Measure: Applications to Multipartite Entanglement
of Graph States and Bosonic Field Theory [50.24983453990065]
An entanglement measure based on the Fubini-Study metric has been recently introduced by Cocchiarella and co-workers.
We present the Gaussian Entanglement Measure (GEM), a generalization of geometric entanglement measure for multimode Gaussian states.
By providing a computable multipartite entanglement measure for systems with a large number of degrees of freedom, we show that our definition can be used to obtain insights into a free bosonic field theory.
arXiv Detail & Related papers (2024-01-31T15:50:50Z) - A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems [87.30652640973317]
Recent advances in computational modelling of atomic systems represent them as geometric graphs with atoms embedded as nodes in 3D Euclidean space.
Geometric Graph Neural Networks have emerged as the preferred machine learning architecture powering applications ranging from protein structure prediction to molecular simulations and material generation.
This paper provides a comprehensive and self-contained overview of the field of Geometric GNNs for 3D atomic systems.
arXiv Detail & Related papers (2023-12-12T18:44:19Z) - Geometric Phases Characterise Operator Algebras and Missing Information [0.6749750044497732]
We show how geometric phases may be used to fully describe quantum systems, with or without gravity.
We find a direct relation between geometric phases and von Neumann algebras.
arXiv Detail & Related papers (2023-05-31T18:00:01Z) - Generalised Geometric Phase [0.0]
A generalised notion of geometric phase for pure states is proposed and its physical manifestations are shown.
The generalised geometric phase is found to contribute to the shift in the energy spectrum due perturbation, and to the forward scattering amplitude in a scattering problem.
arXiv Detail & Related papers (2023-01-26T04:27:53Z) - Topological transitions of the generalized Pancharatnam-Berry phase [55.41644538483948]
We show that geometric phases can be induced by a sequence of generalized measurements implemented on a single qubit.
We demonstrate and study this transition experimentally employing an optical platform.
Our protocol can be interpreted in terms of environment-induced geometric phases.
arXiv Detail & Related papers (2022-11-15T21:31:29Z) - Pointillisme \`a la Signac and Construction of a Pseudo Quantum Phase
Space [0.0]
We construct a quantum-mechanical substitute for the symplectic phase space.
The total space of this fiber bundle consists of geometric quantum states.
We show that the set of equivalence classes of unitarily related geometric quantum states is in a one-to-one correspondence with the set of all Gaussian wavepackets.
arXiv Detail & Related papers (2022-07-31T16:43:06Z) - A singular Riemannian geometry approach to Deep Neural Networks I.
Theoretical foundations [77.86290991564829]
Deep Neural Networks are widely used for solving complex problems in several scientific areas, such as speech recognition, machine translation, image analysis.
We study a particular sequence of maps between manifold, with the last manifold of the sequence equipped with a Riemannian metric.
We investigate the theoretical properties of the maps of such sequence, eventually we focus on the case of maps between implementing neural networks of practical interest.
arXiv Detail & Related papers (2021-12-17T11:43:30Z) - Geometric phase in a dissipative Jaynes-Cummings model: theoretical
explanation for resonance robustness [68.8204255655161]
We compute the geometric phases acquired in both unitary and dissipative Jaynes-Cummings models.
In the dissipative model, the non-unitary effects arise from the outflow of photons through the cavity walls.
We show the geometric phase is robust, exhibiting a vanishing correction under a non-unitary evolution.
arXiv Detail & Related papers (2021-10-27T15:27:54Z) - A Unified View on Geometric Phases and Exceptional Points in Adiabatic
Quantum Mechanics [0.0]
We present a formal geometric framework for the study of adiabatic quantum mechanics for arbitrary finite-dimensional non-degenerate Hamiltonians.
This framework generalizes earlier holonomy interpretations of the geometric phase to non-cyclic states appearing for non-Hermitian Hamiltonians.
arXiv Detail & Related papers (2021-07-06T09:27:26Z) - A Unifying and Canonical Description of Measure-Preserving Diffusions [60.59592461429012]
A complete recipe of measure-preserving diffusions in Euclidean space was recently derived unifying several MCMC algorithms into a single framework.
We develop a geometric theory that improves and generalises this construction to any manifold.
arXiv Detail & Related papers (2021-05-06T17:36:55Z)
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.