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We have studied a three-level {\Lambda}-type atomic system with all the energy levels exhibiting decay. The system is described by a pseudo-Hermitian Hamiltonian and subject to certain conditions, the Hamiltonian shows parity-time (PT)…

Quantum Physics · Physics 2014-08-29 Amarendra K. Sarma , Balla Prannay

We study Krylov complexity of a one-dimensional Bosonic system, the celebrated Bose-Hubbard Model. The Bose-Hubbard Hamiltonian consists of interacting bosons on a lattice, describing ultra-cold atoms. Apart from showing superfluid-Mott…

High Energy Physics - Theory · Physics 2024-01-02 Arpan Bhattacharyya , Debodirna Ghosh , Poulami Nandi

In the presence of Lindblad decoherence, i.e. dissipative effects in an open quantum system due to interaction with an environment, we examine the transition probabilities between the eigenstates in the two-level quantum system described by…

Quantum Physics · Physics 2024-04-24 Tommy Ohlsson , Shun Zhou

In this paper, we propose a successive pseudo-convex approximation algorithm to efficiently compute stationary points for a large class of possibly nonconvex optimization problems. The stationary points are obtained by solving a sequence of…

Optimization and Control · Mathematics 2018-12-17 Yang Yang , Marius Pesavento

Certain aspects of some unitary quantum systems are well-described by evolution via a non-Hermitian effective Hamiltonian, as in the Wigner-Weisskopf theory for spontaneous decay. Conversely, any non-Hermitian Hamiltonian evolution can be…

High Energy Physics - Lattice · Physics 2021-12-01 Jay Hubisz , Bharath Sambasivam , Judah Unmuth-Yockey

For a non-Hermitian Hamiltonian H possessing a real spectrum, we introduce a canonical orthonormal basis in which a previously introduced unitary mapping of H to a Hermitian Hamiltonian h takes a simple form. We use this basis to construct…

Quantum Physics · Physics 2011-07-19 Ali Mostafazadeh , Ahmet Batal

We discuss classical algorithms for approximating the largest eigenvalue of quantum spin and fermionic Hamiltonians based on semidefinite programming relaxation methods. First, we consider traceless $2$-local Hamiltonians $H$ describing a…

Quantum Physics · Physics 2019-10-08 Sergey Bravyi , David Gosset , Robert Koenig , Kristan Temme

We consider simulating an $n$-qubit Hamiltonian with nearest-neighbor interactions evolving for time $t$ on a quantum computer. We show that this simulation has gate complexity $(nt)^{1+o(1)}$ using product formulas, a straightforward…

Quantum Physics · Physics 2019-12-19 Andrew M. Childs , Yuan Su

In polaritons, the properties of matter are modified by mixing the molecular transitions with light modes inside a cavity. Resultant hybrid light-matter states exhibit energy level shifts, are delocalized over many molecular units and have…

A self-consistent, non-perturbative scheme of approximation is proposed for arbitrary interacting quantum systems by generalization of the Hartree method.The scheme consists in approximating the original interaction term $\lambda H_I$ by a…

Quantum Physics · Physics 2007-05-23 B. P. Mahapatra , N. Santi , N. B. Pradhan

A new algorithm for time dependent Hamilton Jacobi equations on networks, based on semi Lagrangian scheme, is proposed. It is based on the definition of viscosity solution for this kind of problems recently given in. A thorough convergence…

Numerical Analysis · Mathematics 2023-10-11 Elisabetta Carlini , Antonio Siconolfi

Motivated by the recent advances in modelling the pseudo-Hermitian Hamiltonian (pHH) systems using superconducting qubits we analyze their quantum dynamics subject to a small time-dependent perturbation. In particular, We develop the linear…

Quantum Physics · Physics 2022-11-03 L. Tetling , M. V. Fistul , Ilya M. Eremin

Principal component analysis is an important dimension reduction technique in machine learning. In [S. Lloyd, M. Mohseni and P. Rebentrost, Nature Physics 10, 631-633, (2014)], a quantum algorithm to implement principal component analysis…

Quantum Physics · Physics 2019-04-09 Changpeng Shao

We present a stochastic quantum computing algorithm that can prepare any eigenvector of a quantum Hamiltonian within a selected energy interval $[E-\epsilon, E+\epsilon]$. In order to reduce the spectral weight of all other eigenvectors by…

Quantum Physics · Physics 2021-07-26 Kenneth Choi , Dean Lee , Joey Bonitati , Zhengrong Qian , Jacob Watkins

Hamiltonian Monte-Carlo (HMC) and its auto-tuned variant, the No U-Turn Sampler (NUTS) can struggle to accurately sample distributions with complex geometries, e.g., varying curvature, due to their constant step size for leapfrog…

Computation · Statistics 2024-10-30 Chirag Modi

We construct a quantum-inspired classical algorithm for computing the permanent of Hermitian positive semidefinite matrices, by exploiting a connection between these mathematical structures and the boson sampling model. Specifically, the…

Quantum Physics · Physics 2017-09-01 L. Chakhmakhchyan , N. J. Cerf , R. Garcia-Patron

In the functional approach to quantum chromodynamics, the properties of hadronic bound states are accessible via covariant integral equations, e.g. the Bethe-Salpeter equations for mesons. In particular, one has to deal with linear,…

High Energy Physics - Phenomenology · Physics 2011-05-05 M. Blank , A. Krassnigg

Pseudo-hermitian matrices are matrices hermitian with respect to an indefinite metric. They can be thought of as the truncation of pseudo-hermitian operators, defined over some Krein space, together with the associated metric, to a finite…

Mathematical Physics · Physics 2022-02-03 Joshua Feinberg , Roman Riser

For Hermitian positive definite linear systems and eigenvalue problems, the eigCG algorithm is a memory efficient algorithm that solves the linear system and simultaneously computes some of its eigenvalues. The algorithm is based on the…

High Energy Physics - Lattice · Physics 2010-02-19 Abdou Abdel-Rehim , Kostas Orginos , Andreas Stathopoulos

Gaussian latent variable models are a key class of Bayesian hierarchical models with applications in many fields. Performing Bayesian inference on such models can be challenging as Markov chain Monte Carlo algorithms struggle with the…

Computation · Statistics 2020-11-09 Charles C. Margossian , Aki Vehtari , Daniel Simpson , Raj Agrawal