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The aim of the present text is twofold: to provide a compendium of Lagrangian and Hamiltonian geometries and to introduce and investigate new analytical Mechanics: Finslerian, Lagrangian and Hamiltonian. The fundamental equations (or…

Differential Geometry · Mathematics 2012-03-20 Radu Miron

Frustration-free Hamiltonians provide pivotal models for understanding quantum many-body systems. In this paper, we establish a general framework for frustration-free fermionic systems. First, we derive a necessary and sufficient condition…

Strongly Correlated Electrons · Physics 2026-01-13 Rintaro Masaoka , Seishiro Ono , Hoi Chun Po , Haruki Watanabe

The experimental realization of increasingly complex quantum states underscores the pressing need for new methods of state learning and verification. In one such framework, quantum state tomography, the aim is to learn the full quantum…

Quantum Physics · Physics 2025-01-30 Antonio Anna Mele , Yaroslav Herasymenko

We show how fermionic statistics can be naturally incorporated in tensor networks on arbitrary graphs through the use of graded Hilbert spaces. This formalism allows to use tensor network methods for fermionic lattice systems in a local…

We study evolution of open quadratic fermion systems in the framework of the quantum Markovian semigroup approach. We show that the algebra concerning commutators of Liouvillians for systems of quadratic interacting fermions of finite…

Mathematical Physics · Physics 2023-01-18 Hiroshi Tamura

This paper provides a stabilizing preparation method for quantum Gaussian states by utilizing continuous measurement. The stochastic evolution of the open quantum system is described in terms of the quantum stochastic master equation. We…

Quantum Physics · Physics 2021-09-28 Liying Bao , Bo Qi , Daoyi Dong

We build an exact framework to evaluate heat, energy, and particle transport between Gaussian reservoirs mediated by a quadratic quantum system. By combining full counting statistics with newly developed non-Markovian master equation…

Quantum Physics · Physics 2026-02-25 Guglielmo Pellitteri , Vittorio Giovannetti , Vasco Cavina

The formulation of classical mechanics applicable to fermionic degrees of freedom is presented in mathematically rigorous terms, including a description of how the mathematical structure relates to the quantization of the theory. Canonical…

Mathematical Physics · Physics 2015-06-05 Luther Rinehart

In recent years, analog quantum simulators have reached unprecedented quality, both in qubit numbers and coherence times. Most of these simulators natively implement Ising-type Hamiltonians, which limits the class of models that can be…

Quantum Physics · Physics 2025-05-06 Matthias Werner , Artur García-Sáez , Marta P. Estarellas

We present some properties of measures (q-Gaussian) that orthogonalize the set of q-Hermite polynomials. We also present an algorithm for simulating i.i.d. sequences of random variables having q-Gaussian distribution.

Probability · Mathematics 2012-08-13 Paweł J. Szabłowki

We study the trace of the exponentials of general fermion bi-linears, including pairing terms, and including non Hermitian forms. In particular, we give elementary derivations for determinant and pfaffian formulae for such traces, and use…

Mesoscale and Nanoscale Physics · Physics 2015-06-19 Israel Klich

Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. Classical solutions such that Kalman filter and Particle filter are introduced in this report. Gaussian processes have been introduced as…

Information Theory · Computer Science 2010-11-04 Mr. Chong Han , Dr. Ido Nevat , Dr. Gareth Peters , Prof. Jinhong Yuan

Stochastic inference on Lie groups plays a key role in state estimation problems such as; inertial navigation, visual inertial odometry, pose estimation in virtual reality, etc. A key problem is fusing independent concentrated Gaussian…

Systems and Control · Electrical Eng. & Systems 2025-11-26 Yixiao Ge , Pieter van Goor , Robert Mahony

A class of fermionic quantum field theories with interactions is shown to be equivalent to probabilistic cellular automata, namely cellular automata with a probability distribution for the initial states. Probabilistic cellular automata on…

High Energy Physics - Lattice · Physics 2022-04-20 C. Wetterich

These notes aim at presenting an overview of Bayesian statistics, the underlying concepts and application methodology that will be useful to astronomers seeking to analyse and interpret a wide variety of data about the Universe. The level…

Cosmology and Nongalactic Astrophysics · Physics 2017-01-09 Roberto Trotta

We have provided a concise introduction to the Ising model as one of the most important models in statistical mechanics and in studying the phenomenon of phase transition. The required theoretical background and derivation of the…

Statistical Mechanics · Physics 2021-05-04 Ashkan Shekaari , Mahmoud Jafari

This paper offers examples of concrete numerical applications of Bayesian quantum-state-assignment methods to a three-level quantum system. The statistical operator assigned on the evidence of various measurement data and kinds of prior…

Quantum Physics · Physics 2007-05-23 A. Månsson , P. G. L. Porta Mana , G. Björk

We present an approach to generalised fermionic statistics which relates the existence of a generalised statistical behaviour to non-invariant ground states. Considering the thermal average of an operatorial generalization of the Heisenberg…

Statistical Mechanics · Physics 2009-10-31 Roberto Mignani , Roberto Scipioni

In a series of recent scientific contributions the role of bosonic and fermionic ladder operators in a macroscopic realm has been investigated. Creation, annihilation and number operators have been used in very different contexts, all…

Mathematical Physics · Physics 2024-11-06 Fabio Bagarello

Gaussian process state-space models (GPSSMs) provide a principled and flexible approach to modeling the dynamics of a latent state, which is observed at discrete-time points via a likelihood model. However, inference in GPSSMs is…

Machine Learning · Computer Science 2023-07-18 Xuhui Fan , Edwin V. Bonilla , Terence J. O'Kane , Scott A. Sisson
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