Quantum Mechanics in a Metric Sheaf: a Model Theoretic Approach
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Some Properties of the Essential Numerical Range on Banach Spaces
Pure Mathematical Sciences, Vol. 6, 2017, no. 1, 105 - 111 HIKARI Ltd, www.m-hikari.com https://doi.org/10.12988/pms.2017.7912 Some Properties of the Essential Numerical Range on Banach Spaces L. N. Muhati, J. O. Bonyo and J. O. Agure Department of Pure and Applied Mathematics Maseno University, P.O. Box 333 - 40105, Maseno, Kenya Copyright c 2017 L. N. Muhati, J. O. Bonyo and J. O. Agure. This article is dis- tributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract We study the properties of the essential algebraic numerical range as well as the essential spatial numerical range for Banach space operators. Mathematics Subject Classification: 47A10; 47A05; 47A53 Keywords: Essential algebraic numerical range, Essential spatial numer- ical range, Calkin algebra 1 Introduction Let A be a complex normed algebra with a unit and let A∗ denote its dual space. For a comprehensive theory on normed algebras, we refer to [4]. We define the algebraic numerical range of an element a 2 A by V (a; A) = ff(a): f 2 A∗; f(1) = 1 = kfkg. Let X denote a complex Banach space and L(X) be the Banach algebra of all bounded linear operators acting on X. We de- note the algebraic numerical range of any T 2 L(X) by V (T; L(X)). For T 2 L(X), the spatial numerical range is defined by W (T ) = fhT x; x∗i : x 2 X; x∗ 2 X∗; kxk = 1 = kx∗k = hx; x∗ig. -
Numerical Range of Operators, Numerical Index of Banach Spaces, Lush Spaces, and Slicely Countably Determined Banach Spaces
Numerical range of operators, numerical index of Banach spaces, lush spaces, and Slicely Countably Determined Banach spaces Miguel Martín University of Granada (Spain) June 5, 2009 Preface This text is not a book and it is not in its nal form. This is going to be used as classroom notes for the talks I am going to give in the Workshop on Geometry of Banach spaces and its Applications (sponsored by Indian National Board for Higher Mathematics) to be held at the Indian Statistical Institute, Bangalore (India), on 1st - 13th June 2009. Miguel Martín Contents Preface 3 Basic notation7 1 Numerical Range of operators. Surjective isometries9 1.1 Introduction.....................................9 1.2 The exponential function. Isometries....................... 15 1.3 Finite-dimensional spaces with innitely many isometries............ 17 1.3.1 The dimension of the Lie algebra..................... 19 1.4 Surjective isometries and duality......................... 20 2 Numerical index of Banach spaces 23 2.1 Introduction..................................... 23 2.2 Computing the numerical index.......................... 23 2.3 Numerical index and duality............................ 28 2.4 Banach spaces with numerical index one..................... 32 2.5 Renorming and numerical index.......................... 34 2.6 Finite-dimensional spaces with numerical index one: asymptotic behavior.. 38 2.7 Relationship to the Daugavet property....................... 39 2.8 Smoothness, convexity and numerical index 1 .................. 43 3 Lush spaces 47 3.1 Examples of lush spaces.............................. 49 5 3.2 Lush renormings.................................. 52 3.3 Some reformulations of lushness.......................... 53 3.4 Lushness is not equivalent to numerical index 1 ................. 55 3.5 Stability results for lushness............................ 56 4 Slicely countably determined Banach spaces 59 4.1 Slicely countably determined sets........................ -
Numerical Range and Functional Calculus in Hilbert Space
Numerical range and functional calculus in Hilbert space Michel Crouzeix Abstract We prove an inequality related to polynomial functions of a square matrix, involving the numerical range of the matrix. We also show extensions valid for bounded and also unbounded operators in Hilbert spaces, which allow the development of a functional calculus. Mathematical subject classification : 47A12 ; 47A25 Keywords : functional calculus, numerical range, spectral sets. 1 Introduction In this paper H denotes a complex Hilbert space equipped with the inner product . , . and h i corresponding norm v = v, v 1/2. We denote its unit sphere by Σ := v H; v = 1 . For k k h i H { ∈ k k } a bounded linear operator A (H) we use the operator norm A and denote by W (A) its ∈ L k k numerical range : A := sup A v , W (A) := Av,v ; v ΣH . k k v ΣH k k {h i ∈ } ∈ Recall that the numerical range is a convex subset of C and that its closure contains the spectrum of A. We keep the same notation in the particular case where H = Cd and A is a d d matrix. × The main aim of this article is to prove that the inequality p(A) 56 sup p(z) (1) k k≤ z W (A) | | ∈ holds for any matrix A Cd,d and any polynomial p : C C (independently of d). ∈ → It is remarkable that the completely bounded version of this inequality is also valid. More precisely, we consider now matrix-valued polynomials P : C Cm,n, i.e., for z C, P (z) = → ∈ (p (z)) is a matrix, with each entry p (.) a polynomial C C. -
Numerical Range of Square Matrices a Study in Spectral Theory
Numerical Range of Square Matrices A Study in Spectral Theory Department of Mathematics, Linköping University Erik Jonsson LiTH-MAT-EX–2019/03–SE Credits: 16 hp Level: G2 Supervisor: Göran Bergqvist, Department of Mathematics, Linköping University Examiner: Milagros Izquierdo, Department of Mathematics, Linköping University Linköping: June 2019 Abstract In this thesis, we discuss important results for the numerical range of general square matrices. Especially, we examine analytically the numerical range of complex-valued 2 × 2 matrices. Also, we investigate and discuss the Gershgorin region of general square matrices. Lastly, we examine numerically the numerical range and Gershgorin regions for different types of square matrices, both contain the spectrum of the matrix, and compare these regions, using the calculation software Maple. Keywords: numerical range, square matrix, spectrum, Gershgorin regions URL for electronic version: urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157661 Erik Jonsson, 2019. iii Sammanfattning I denna uppsats diskuterar vi viktiga resultat för numerical range av gene- rella kvadratiska matriser. Speciellt undersöker vi analytiskt numerical range av komplexvärda 2 × 2 matriser. Vi utreder och diskuterar också Gershgorin områden för generella kvadratiska matriser. Slutligen undersöker vi numeriskt numerical range och Gershgorin områden för olika typer av matriser, där båda innehåller matrisens spektrum, och jämför dessa områden genom att använda beräkningsprogrammet Maple. Nyckelord: numerical range, kvadratisk matris, spektrum, Gershgorin områden URL för elektronisk version: urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157661 Erik Jonsson, 2019. v Acknowledgements I’d like to thank my supervisor Göran Bergqvist, who has shown his great interest for linear algebra and inspired me for writing this thesis. -
An Introduction to Operad Theory
AN INTRODUCTION TO OPERAD THEORY SAIMA SAMCHUCK-SCHNARCH Abstract. We give an introduction to category theory and operad theory aimed at the undergraduate level. We first explore operads in the category of sets, and then generalize to other familiar categories. Finally, we develop tools to construct operads via generators and relations, and provide several examples of operads in various categories. Throughout, we highlight the ways in which operads can be seen to encode the properties of algebraic structures across different categories. Contents 1. Introduction1 2. Preliminary Definitions2 2.1. Algebraic Structures2 2.2. Category Theory4 3. Operads in the Category of Sets 12 3.1. Basic Definitions 13 3.2. Tree Diagram Visualizations 14 3.3. Morphisms and Algebras over Operads of Sets 17 4. General Operads 22 4.1. Basic Definitions 22 4.2. Morphisms and Algebras over General Operads 27 5. Operads via Generators and Relations 33 5.1. Quotient Operads and Free Operads 33 5.2. More Examples of Operads 38 5.3. Coloured Operads 43 References 44 1. Introduction Sets equipped with operations are ubiquitous in mathematics, and many familiar operati- ons share key properties. For instance, the addition of real numbers, composition of functions, and concatenation of strings are all associative operations with an identity element. In other words, all three are examples of monoids. Rather than working with particular examples of sets and operations directly, it is often more convenient to abstract out their common pro- perties and work with algebraic structures instead. For instance, one can prove that in any monoid, arbitrarily long products x1x2 ··· xn have an unambiguous value, and thus brackets 2010 Mathematics Subject Classification. -
Morphophoric Povms, Generalised Qplexes, Ewline and 2-Designs
Morphophoric POVMs, generalised qplexes, and 2-designs Wojciech S lomczynski´ and Anna Szymusiak Institute of Mathematics, Jagiellonian University,Lojasiewicza 6, 30-348 Krak´ow, Poland We study the class of quantum measurements with the property that the image of the set of quantum states under the measurement map transforming states into prob- ability distributions is similar to this set and call such measurements morphophoric. This leads to the generalisation of the notion of a qplex, where SIC-POVMs are re- placed by the elements of the much larger class of morphophoric POVMs, containing in particular 2-design (rank-1 and equal-trace) POVMs. The intrinsic geometry of a generalised qplex is the same as that of the set of quantum states, so we explore its external geometry, investigating, inter alia, the algebraic and geometric form of the inner (basis) and the outer (primal) polytopes between which the generalised qplex is sandwiched. In particular, we examine generalised qplexes generated by MUB-like 2-design POVMs utilising their graph-theoretical properties. Moreover, we show how to extend the primal equation of QBism designed for SIC-POVMs to the morphophoric case. 1 Introduction and preliminaries Over the last ten years in a series of papers [2,3, 30{32, 49, 68] Fuchs, Schack, Appleby, Stacey, Zhu and others have introduced first the idea of QBism (formerly quantum Bayesianism) and then its probabilistic embodiment - the (Hilbert) qplex, both based on the notion of symmetric informationally complete positive operator-valued measure -
Projective Hilbert Space Structures at Exceptional Points
IOP PUBLISHING JOURNAL OF PHYSICS A: MATHEMATICAL AND THEORETICAL J. Phys. A: Math. Theor. 40 (2007) 8815–8833 doi:10.1088/1751-8113/40/30/014 Projective Hilbert space structures at exceptional points Uwe Gunther¨ 1, Ingrid Rotter2 and Boris F Samsonov3 1 Research Center Dresden-Rossendorf, PO 510119, D-01314 Dresden, Germany 2 Max Planck Institute for the Physics of Complex Systems, D-01187 Dresden, Germany 3 Physics Department, Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, Russia E-mail: [email protected], [email protected] and [email protected] Received 23 April 2007, in final form 6 June 2007 Published 12 July 2007 Online at stacks.iop.org/JPhysA/40/8815 Abstract A non-Hermitian complex symmetric 2 × 2-matrix toy model is used to study projective Hilbert space structures in the vicinity of exceptional points (EPs). The bi-orthogonal eigenvectors of a diagonalizable matrix are Puiseux- expanded in terms of the root vectors at the EP. It is shown that the apparent contradiction between the two incompatible normalization conditions with finite and singular behaviour in the EP-limit can be resolved by projectively extending the original Hilbert space. The complementary normalization conditions correspond then to two different affine charts of this enlarged projective Hilbert space. Geometric phase and phase-jump behaviour are analysed, and the usefulness of the phase rigidity as measure for the distance to EP configurations is demonstrated. Finally, EP-related aspects of PT - symmetrically extended quantum mechanics are discussed and a conjecture concerning the quantum brachistochrone problem is formulated. PACS numbers: 03.65.Fd, 03.65.Vf, 03.65.Ca, 02.40.Xx 1. -
C-Star Algebras
C∗ Algebras Prof. Marc Rieffel notes by Theo Johnson-Freyd UC-Berkeley Mathematics Department Spring Semester 2008 Contents 1: Introduction 4 2: January 23{28, 20084 3: January 30, 20084 3.1 The positive cone......................................5 4: February 1, 20086 4.1 Ideals............................................7 5: February 4, 20089 5.1 Quotient C∗ algebras....................................9 5.2 Beginnings of non-commutative measure theory..................... 10 6: February 6, 2008 11 6.1 Positive Linear Functionals................................ 11 7: February 8, 2008 13 7.1 GNS Construction..................................... 13 8: February 11, 2008 14 9: February 13, 2008 16 9.1 We continue the discussion from last time........................ 17 9.2 Irreducible representations................................. 18 10:Problem Set 1: \Preventive Medicine" Due February 20, 2008 18 1 11:February 15, 2008 19 12:February 20, 2008 20 13:February 22, 2008 22 13.1 Compact operators..................................... 24 14:February 25, 2008 25 15:February 27, 2008 27 15.1 Continuing from last time................................. 27 15.2 Relations between irreducible representations and two-sided ideal........... 28 16:February 29, 2008 29 17:March 3, 2008 31 17.1 Some topology and primitive ideals............................ 31 18:March 5, 2008 32 18.1 Examples.......................................... 32 19:March 7, 2008 35 19.1 Tensor products....................................... 35 20:Problem Set 2: Due March 14, 2008 37 Fields of C∗-algebras....................................... 37 An important extension theorem................................ 38 The non-commutative Stone-Cechˇ compactification...................... 38 Morphisms............................................ 39 21:March 10, 2008 39 21.1 C∗-dynamical systems................................... 39 22:March 12, 2008 41 23:March 14, 2008 41 23.1 Twisted convolution, approximate identities, etc.................... -
Numerical Ranges and Applications in Quantum Information
Numerical Ranges and Applications in Quantum Information by Ningping Cao A Thesis presented to The University of Guelph In partial fulfilment of requirements for the degree of Doctor of Philosophy in Mathematics & Statistics Guelph, Ontario, Canada © Ningping Cao, August, 2021 ABSTRACT NUMERICAL RANGES AND APPLICATIONS IN QUANTUM INFORMATION Ningping Cao Advisor: University of Guelph, 2021 Dr. Bei Zeng Dr. David Kribs The numerical range (NR) of a matrix is a concept that first arose in the early 1900’s as part of efforts to build a rigorous mathematical framework for quantum mechanics andother challenges at the time. Quantum information science (QIS) is a new field having risen to prominence over the past two decades, combining quantum physics and information science. In this thesis, we connect NR and its extensions with QIS in several topics, and apply the knowledge to solve related QIS problems. We utilize the insight offered by NRs and apply them to quantum state inference and Hamiltonian reconstruction. We propose a new general deep learning method for quantum state inference in chapter 3, and employ it to two scenarios – maximum entropy estimation of unknown states and ground state reconstruction. The method manifests high fidelity, exceptional efficiency, and noise tolerance on both numerical and experimental data. A new optimization algorithm is presented in chapter 4 for recovering Hamiltonians. It takes in partial measurements from any eigenstates of an unknown Hamiltonian H, then provides an estimation of H. Our algorithm almost perfectly deduces generic and local generic Hamiltonians. Inspired by hybrid classical-quantum error correction (Hybrid QEC), the higher rank (k : p)-matricial range is defined and studied in chapter 5. -
On Invariance of the Numerical Range and Some Classes of Operators In
ON INVARIANCE OF THE NUMERICAL RANGE AND SOME CLASSES OF OPERATORS IN HILBERT SPACES By Arthur Wanyonyi Wafula A thesis submitted in fulfillment of the requirements for the award of the Degree of Doctor of Philosophy in Pure Mathematics School of Mathematics,University of Nairobi 15 April 2013 G^ o a op h University of NAIROBI Library 0430140 4 0.1 DECLARATION This thesis is my orginal work and has not been presented for a degree in any other University S ignat ure.....D ate . .....A* / ° 5 / 7'tTL. m h I Arthur Wanyonyi Wanambisi Wafula This thesis has been submitted for examination with our approval as University super visors; Signature Date 2- Signature P rof. J. M. Khalagai Prof. G.P.Pokhariyal 1 0.2 DEDICATION This work is dedicated to my family ; namely, my wife Anne Atieno and my sons Davies Magudha and Mishael Makali. n 0.3 ACKNOWLEDGEMENT My humble acknowledgement go to the Almighty God Jehovah who endows us withdhe gift of the Human brain that enables us to be creative and imaginative. I would also like to express my sincere gratitude to my Supervisor Prof.J.M.Khalagai for tirelessly guiding me in my research.I also wish to thank Prof.G.P.Pokhariyal for never ceasing to put me on my toes. I sincerely wish to thank the Director of the School of Mathematics DrJ .Were for his invaluable support. Furthermore,! am indebted to my colleagues Mrs.E.Muriuki, Dr.Mile, Dr.I.Kipchichir, Mr R.Ogik and Dr.N.'Owuor who have been a source of inspiration. -
Binary Integer Programming and Its Use for Envelope Determination
Binary Integer Programming and its Use for Envelope Determination By Vladimir Y. Lunin1,2, Alexandre Urzhumtsev3,† & Alexander Bockmayr2 1 Institute of Mathematical Problems of Biology, Russian Academy of Sciences, Pushchino, Moscow Region, 140292 Russia 2 LORIA, UMR 7503, Faculté des Sciences, Université Henri Poincaré, Nancy I, 54506 Vandoeuvre-les-Nancy, France; [email protected] 3 LCM3B, UMR 7036 CNRS, Faculté des Sciences, Université Henri Poincaré, Nancy I, 54506 Vandoeuvre-les-Nancy, France; [email protected] † to whom correspondence must be sent Abstract The density values are linked to the observed magnitudes and unknown phases by a system of non-linear equations. When the object of search is a binary envelope rather than a continuous function of the electron density distribution, these equations can be replaced by a system of linear inequalities with respect to binary unknowns and powerful tools of integer linear programming may be applied to solve the phase problem. This novel approach was tested with calculated and experimental data for a known protein structure. 1. Introduction Binary Integer Programming (BIP in what follows) is an approach to solve a system of linear inequalities in binary unknowns (0 or 1 in what follows). Integer programming has been studied in mathematics, computer science, and operations research for more than 40 years (see for example Johnson et al., 2000 and Bockmayr & Kasper, 1998, for a review). It has been successfully applied to solve a huge number of large-scale combinatorial problems. The general form of an integer linear programming problem is max { cTx | Ax ≤ b, x ∈ Zn } (1.1) with a real matrix A of a dimension m by n, and vectors c ∈ Rn, b ∈ Rm, cTx being the scalar product of the vectors c and x. -
Limit on Time-Energy Uncertainty with Multipartite Entanglement
Limit on Time-Energy Uncertainty with Multipartite Entanglement Manabendra Nath Bera, R. Prabhu, Arun Kumar Pati, Aditi Sen(De), Ujjwal Sen Harish-Chandra Research Institute, Chhatnag Road, Jhunsi, Allahabad 211 019, India We establish a relation between the geometric time-energy uncertainty and multipartite entan- glement. In particular, we show that the time-energy uncertainty relation is bounded below by the geometric measure of multipartite entanglement for an arbitrary quantum evolution of any multi- partite system. The product of the time-averaged speed of the quantum evolution and the time interval of the evolution is bounded below by the multipartite entanglement of the target state. This relation holds for pure as well as for mixed states. We provide examples of physical systems for which the bound reaches close to saturation. I. INTRODUCTION of quantum evolution? Or, more generally, can the geo- metric quantum uncertainty relation be connected, quan- titatively, with the multipartite entanglement present in In the beginning of the last century, the geometry of the system? This question is important not only due space-time has played an important role in the reformu- to its fundamental nature, but also because of its prac- lation of classical mechanics. Similarly, it is hoped that tical relevance in quantum information. We establish a a geometric formulation of quantum theory, and an un- relationship between the multipartite entanglement in a derstanding of the geometry of quantum state space can many-body quantum system and the total distance trav- provide new insights into the nature of quantum world eled by the state (pure or mixed) during its evolution.