Programmable Unitary Operations for Orbital Angular Momentum Encoded
States
- URL: http://arxiv.org/abs/2011.03250v2
- Date: Wed, 24 Aug 2022 07:55:55 GMT
- Title: Programmable Unitary Operations for Orbital Angular Momentum Encoded
States
- Authors: Shikang Li, Xue Feng, Kaiyu Cui, Fang Liu, Wei Zhang, Yidong Huang
- Abstract summary: We have proposed and demonstrated a scalable and efficient scheme for programmable unitary operations in orbital angular momentum domain.
Based on matrix decomposition into diagonal and Fourier factors, arbitrary matrix operators can be implemented only by diagonal matrices.
- Score: 6.027164112828568
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We have proposed and demonstrated a scalable and efficient scheme for
programmable unitary operations in orbital angular momentum (OAM) domain. Based
on matrix decomposition into diagonal and Fourier factors, arbitrary matrix
operators can be implemented only by diagonal matrices alternately acting on
orbital angular momentum domain and azimuthal angle domain, which are linked by
Fourier transform. With numerical simulations, unitary matrices with
dimensionality of 3*3 are designed and discussed for OAM domain. Meanwhile, the
parallelism of our proposed scheme is also presented with two 3*3 matrices.
Furthermore, as an alternative to verify our proposal, proof of principle
experiments have been performed on path domain with the same matrix
decomposition method, in which an average fidelity of 0.97 is evaluated through
80 experimental results with dimensionality of 3*3.
Related papers
- Efficient conversion from fermionic Gaussian states to matrix product states [48.225436651971805]
We propose a highly efficient algorithm that converts fermionic Gaussian states to matrix product states.
It can be formulated for finite-size systems without translation invariance, but becomes particularly appealing when applied to infinite systems.
The potential of our method is demonstrated by numerical calculations in two chiral spin liquids.
arXiv Detail & Related papers (2024-08-02T10:15:26Z) - Regularized Projection Matrix Approximation with Applications to Community Detection [1.3761665705201904]
This paper introduces a regularized projection matrix approximation framework designed to recover cluster information from the affinity matrix.
We investigate three distinct penalty functions, each specifically tailored to address bounded, positive, and sparse scenarios.
Numerical experiments conducted on both synthetic and real-world datasets reveal that our regularized projection matrix approximation approach significantly outperforms state-of-the-art methods in clustering performance.
arXiv Detail & Related papers (2024-05-26T15:18:22Z) - Infeasibility of constructing a special orthogonal matrix for the
deterministic remote preparation of arbitrary n-qubit state [2.3455770974978933]
We present a complex-complexity algorithm to construct a special orthogonal matrix for the remote deterministic state preparation (DRSP) of an arbitrary n-qubit state.
We use the proposed algorithm to confirm that the unique form does not have any solution when n>3, which means it is infeasible to construct such a special orthogonal matrix for the DRSP of an arbitrary n-qubit state.
arXiv Detail & Related papers (2023-09-23T11:06:34Z) - Mutually-orthogonal unitary and orthogonal matrices [6.9607365816307]
We show that the minimum and maximum numbers of an unextendible maximally entangled bases within a real two-qutrit system are three and four, respectively.
As an application in quantum information theory, we show that the minimum and maximum numbers of an unextendible maximally entangled bases within a real two-qutrit system are three and four, respectively.
arXiv Detail & Related papers (2023-09-20T08:20:57Z) - Third quantization of open quantum systems: new dissipative symmetries
and connections to phase-space and Keldysh field theory formulations [77.34726150561087]
We reformulate the technique of third quantization in a way that explicitly connects all three methods.
We first show that our formulation reveals a fundamental dissipative symmetry present in all quadratic bosonic or fermionic Lindbladians.
For bosons, we then show that the Wigner function and the characteristic function can be thought of as ''wavefunctions'' of the density matrix.
arXiv Detail & Related papers (2023-02-27T18:56:40Z) - Cubic-Regularized Newton for Spectral Constrained Matrix Optimization
and its Application to Fairness [9.649070872824957]
Matrix functions are utilized to rewrite smooth spectral constrained matrix optimization problems.
A new convergence analysis is provided for cubic-regularized Newton for matrix vector spaces.
arXiv Detail & Related papers (2022-09-02T18:11:05Z) - Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation [64.49871502193477]
We propose a novel semi-supervised subspace clustering method, which is able to simultaneously augment the initial supervisory information and construct a discriminative affinity matrix.
Comprehensive experimental results on six commonly-used benchmark datasets demonstrate the superiority of our method over state-of-the-art methods.
arXiv Detail & Related papers (2022-05-21T01:47:17Z) - Quantum algorithms for matrix operations and linear systems of equations [65.62256987706128]
We propose quantum algorithms for matrix operations using the "Sender-Receiver" model.
These quantum protocols can be used as subroutines in other quantum schemes.
arXiv Detail & Related papers (2022-02-10T08:12:20Z) - Direct solution of multiple excitations in a matrix product state with
block Lanczos [62.997667081978825]
We introduce the multi-targeted density matrix renormalization group method that acts on a bundled matrix product state, holding many excitations.
A large number of excitations can be obtained at a small bond dimension with highly reliable local observables throughout the chain.
arXiv Detail & Related papers (2021-09-16T18:36:36Z) - Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian
Processes [3.564709604457361]
"Projection" mapping consists of an orthonormal matrix that is considered a priori unknown and needs to be inferred jointly with the GP parameters.
We extend the proposed framework to multi-fidelity models using GPs including the scenarios of training multiple outputs together.
The benefits of our proposed framework, are illustrated on the computationally challenging three-dimensional aerodynamic optimization of a last-stage blade for an industrial gas turbine.
arXiv Detail & Related papers (2020-08-05T22:28:53Z) - Multi-Objective Matrix Normalization for Fine-grained Visual Recognition [153.49014114484424]
Bilinear pooling achieves great success in fine-grained visual recognition (FGVC)
Recent methods have shown that the matrix power normalization can stabilize the second-order information in bilinear features.
We propose an efficient Multi-Objective Matrix Normalization (MOMN) method that can simultaneously normalize a bilinear representation.
arXiv Detail & Related papers (2020-03-30T08:40:35Z)
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.