Newton Cradle Spectra
- URL: http://arxiv.org/abs/2206.09927v1
- Date: Mon, 20 Jun 2022 18:00:02 GMT
- Title: Newton Cradle Spectra
- Authors: Barbara \v{S}oda, Achim Kempf
- Abstract summary: We prove nonperturbative results on the behavior of eigenvalues and eigenvectors.
We apply these results to quantum computing and information theory.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present broadly applicable nonperturbative results on the behavior of
eigenvalues and eigenvectors under the addition of self-adjoint operators and
under the multiplication of unitary operators, in finite-dimensional Hilbert
spaces. To this end, we decompose these operations into elementary 1-parameter
processes in which the eigenvalues move similarly to the spheres in Newton's
cradle. As special cases, we recover level repulsion and Cauchy interlacing. We
discuss two examples of applications. Applied to adiabatic quantum computing,
we obtain new tools to relate algorithmic complexity to computational slowdown
through gap narrowing. Applied to information theory, we obtain a
generalization of Shannon sampling theory, the theory that establishes the
equivalence of continuous and discrete representations of information. The new
generalization of Shannon sampling applies to signals of varying information
density and finite length.
Related papers
- Quantum tomography of helicity states for general scattering processes [55.2480439325792]
Quantum tomography has become an indispensable tool in order to compute the density matrix $rho$ of quantum systems in Physics.
We present the theoretical framework for reconstructing the helicity quantum initial state of a general scattering process.
arXiv Detail & Related papers (2023-10-16T21:23:42Z) - Understanding the Generalization Ability of Deep Learning Algorithms: A
Kernelized Renyi's Entropy Perspective [11.255943520955764]
We propose a novel information theoretical measure: kernelized Renyi's entropy.
We establish the generalization error bounds for gradient/Langevin descent (SGD/SGLD) learning algorithms under kernelized Renyi's entropy.
We show that our information-theoretical bounds depend on the statistics of the gradients, and are rigorously tighter than the current state-of-the-art (SOTA) results.
arXiv Detail & Related papers (2023-05-02T01:17:15Z) - General quantum algorithms for Hamiltonian simulation with applications
to a non-Abelian lattice gauge theory [44.99833362998488]
We introduce quantum algorithms that can efficiently simulate certain classes of interactions consisting of correlated changes in multiple quantum numbers.
The lattice gauge theory studied is the SU(2) gauge theory in 1+1 dimensions coupled to one flavor of staggered fermions.
The algorithms are shown to be applicable to higher-dimensional theories as well as to other Abelian and non-Abelian gauge theories.
arXiv Detail & Related papers (2022-12-28T18:56:25Z) - Operator relaxation and the optimal depth of classical shadows [0.0]
We study the sample complexity of learning the expectation value of Pauli operators via shallow shadows''
We show that the shadow norm is expressed in terms of properties of the Heisenberg time evolution of operators under the randomizing circuit.
arXiv Detail & Related papers (2022-12-22T18:46:46Z) - Instance-Dependent Generalization Bounds via Optimal Transport [51.71650746285469]
Existing generalization bounds fail to explain crucial factors that drive the generalization of modern neural networks.
We derive instance-dependent generalization bounds that depend on the local Lipschitz regularity of the learned prediction function in the data space.
We empirically analyze our generalization bounds for neural networks, showing that the bound values are meaningful and capture the effect of popular regularization methods during training.
arXiv Detail & Related papers (2022-11-02T16:39:42Z) - Sum-of-Squares Relaxations for Information Theory and Variational
Inference [0.0]
We consider extensions of the Shannon relative entropy, referred to as $f$-divergences.
We derive a sequence of convex relaxations for computing these divergences.
We provide more efficient relaxations based on spectral information divergences from quantum information theory.
arXiv Detail & Related papers (2022-06-27T13:22:40Z) - Generalization Metrics for Practical Quantum Advantage in Generative
Models [68.8204255655161]
Generative modeling is a widely accepted natural use case for quantum computers.
We construct a simple and unambiguous approach to probe practical quantum advantage for generative modeling by measuring the algorithm's generalization performance.
Our simulation results show that our quantum-inspired models have up to a $68 times$ enhancement in generating unseen unique and valid samples.
arXiv Detail & Related papers (2022-01-21T16:35:35Z) - Geometry of quantum complexity [0.0]
Computational complexity is a new quantum information concept that may play an important role in holography.
We consider quantum computational complexity for $n$ qubits using Nielsen's geometrical approach.
arXiv Detail & Related papers (2020-11-15T18:41:19Z) - Entanglement and Complexity of Purification in (1+1)-dimensional free
Conformal Field Theories [55.53519491066413]
We find pure states in an enlarged Hilbert space that encode the mixed state of a quantum field theory as a partial trace.
We analyze these quantities for two intervals in the vacuum of free bosonic and Ising conformal field theories.
arXiv Detail & Related papers (2020-09-24T18:00:13Z) - Models of zero-range interaction for the bosonic trimer at unitarity [91.3755431537592]
We present the construction of quantum Hamiltonians for a three-body system consisting of identical bosons mutually coupled by a two-body interaction of zero range.
For a large part of the presentation, infinite scattering length will be considered.
arXiv Detail & Related papers (2020-06-03T17:54:43Z)
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.