Lecture 18 — October 26, 2015 1 Overview 2 Quantum Entropy
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Quantum Information, Entanglement and Entropy
QUANTUM INFORMATION, ENTANGLEMENT AND ENTROPY Siddharth Sharma Abstract In this review I had given an introduction to axiomatic Quantum Mechanics, Quantum Information and its measure entropy. Also, I had given an introduction to Entanglement and its measure as the minimum of relative quantum entropy of a density matrix of a Hilbert space with respect to all separable density matrices of the same Hilbert space. I also discussed geodesic in the space of density matrices, their orthogonality and Pythagorean theorem of density matrix. Postulates of Quantum Mechanics The basic postulate of quantum mechanics is about the Hilbert space formalism: • Postulates 0: To each quantum mechanical system, has a complex Hilbert space ℍ associated to it: Set of all pure state, ℍ⁄∼ ≔ {⟦푓⟧|⟦푓⟧ ≔ {ℎ: ℎ ∼ 푓}} and ℎ ∼ 푓 ⇔ ∃ 푧 ∈ ℂ such that ℎ = 푧 푓 where 푧 is called phase • Postulates 1: The physical states of a quantum mechanical system are described by statistical operators acting on the Hilbert space. A density matrix or statistical operator, is a positive operator of trace 1 on the Hilbert space. Where is called positive if 〈푥, 푥〉 for all 푥 ∈ ℍ and 〈⋇,⋇〉. • Postulates 2: The observables of a quantum mechanical system are described by self- adjoint operators acting on the Hilbert space. A self-adjoint operator A on a Hilbert space ℍ is a linear operator 퐴: ℍ → ℍ which satisfies 〈퐴푥, 푦〉 = 〈푥, 퐴푦〉 for 푥, 푦 ∈ ℍ. Lemma: The density matrices acting on a Hilbert space form a convex set whose extreme points are the pure states. Proof. Denote by Σ the set of density matrices. -
A Mini-Introduction to Information Theory
A Mini-Introduction To Information Theory Edward Witten School of Natural Sciences, Institute for Advanced Study Einstein Drive, Princeton, NJ 08540 USA Abstract This article consists of a very short introduction to classical and quantum information theory. Basic properties of the classical Shannon entropy and the quantum von Neumann entropy are described, along with related concepts such as classical and quantum relative entropy, conditional entropy, and mutual information. A few more detailed topics are considered in the quantum case. arXiv:1805.11965v5 [hep-th] 4 Oct 2019 Contents 1 Introduction 2 2 Classical Information Theory 2 2.1 ShannonEntropy ................................... .... 2 2.2 ConditionalEntropy ................................. .... 4 2.3 RelativeEntropy .................................... ... 6 2.4 Monotonicity of Relative Entropy . ...... 7 3 Quantum Information Theory: Basic Ingredients 10 3.1 DensityMatrices .................................... ... 10 3.2 QuantumEntropy................................... .... 14 3.3 Concavity ......................................... .. 16 3.4 Conditional and Relative Quantum Entropy . ....... 17 3.5 Monotonicity of Relative Entropy . ...... 20 3.6 GeneralizedMeasurements . ...... 22 3.7 QuantumChannels ................................... ... 24 3.8 Thermodynamics And Quantum Channels . ...... 26 4 More On Quantum Information Theory 27 4.1 Quantum Teleportation and Conditional Entropy . ......... 28 4.2 Quantum Relative Entropy And Hypothesis Testing . ......... 32 4.3 Encoding -
Some Trace Inequalities for Exponential and Logarithmic Functions
Bull. Math. Sci. https://doi.org/10.1007/s13373-018-0123-3 Some trace inequalities for exponential and logarithmic functions Eric A. Carlen1 · Elliott H. Lieb2 Received: 8 October 2017 / Revised: 17 April 2018 / Accepted: 24 April 2018 © The Author(s) 2018 Abstract Consider a function F(X, Y ) of pairs of positive matrices with values in the positive matrices such that whenever X and Y commute F(X, Y ) = X pY q . Our first main result gives conditions on F such that Tr[X log(F(Z, Y ))]≤Tr[X(p log X + q log Y )] for all X, Y, Z such that TrZ = Tr X. (Note that Z is absent from the right side of the inequality.) We give several examples of functions F to which the theorem applies. Our theorem allows us to give simple proofs of the well known logarithmic inequalities of Hiai and Petz and several new generalizations of them which involve three variables X, Y, Z instead of just X, Y alone. The investigation of these logarith- mic inequalities is closely connected with three quantum relative entropy functionals: The standard Umegaki quantum relative entropy D(X||Y ) = Tr[X(log X − log Y ]), and two others, the Donald relative entropy DD(X||Y ), and the Belavkin–Stasewski relative entropy DBS(X||Y ). They are known to satisfy DD(X||Y ) ≤ D(X||Y ) ≤ DBS(X||Y ). We prove that the Donald relative entropy provides the sharp upper bound, independent of Z on Tr[X log(F(Z, Y ))] in a number of cases in which F(Z, Y ) is homogeneous of degree 1 in Z and −1inY . -
Lecture 6: Quantum Error Correction and Quantum Capacity
Lecture 6: Quantum error correction and quantum capacity Mark M. Wilde∗ The quantum capacity theorem is one of the most important theorems in quantum Shannon theory. It is a fundamentally \quantum" theorem in that it demonstrates that a fundamentally quantum information quantity, the coherent information, is an achievable rate for quantum com- munication over a quantum channel. The fact that the coherent information does not have a strong analog in classical Shannon theory truly separates the quantum and classical theories of information. The no-cloning theorem provides the intuition behind quantum error correction. The goal of any quantum communication protocol is for Alice to establish quantum correlations with the receiver Bob. We know well now that every quantum channel has an isometric extension, so that we can think of another receiver, the environment Eve, who is at a second output port of a larger unitary evolution. Were Eve able to learn anything about the quantum information that Alice is attempting to transmit to Bob, then Bob could not be retrieving this information|otherwise, they would violate the no-cloning theorem. Thus, Alice should figure out some subspace of the channel input where she can place her quantum information such that only Bob has access to it, while Eve does not. That the dimensionality of this subspace is exponential in the coherent information is perhaps then unsurprising in light of the above no-cloning reasoning. The coherent information is an entropy difference H(B) − H(E)|a measure of the amount of quantum correlations that Alice can establish with Bob less the amount that Eve can gain. -
From Joint Convexity of Quantum Relative Entropy to a Concavity Theorem of Lieb
PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 140, Number 5, May 2012, Pages 1757–1760 S 0002-9939(2011)11141-9 Article electronically published on August 4, 2011 FROM JOINT CONVEXITY OF QUANTUM RELATIVE ENTROPY TO A CONCAVITY THEOREM OF LIEB JOEL A. TROPP (Communicated by Marius Junge) Abstract. This paper provides a succinct proof of a 1973 theorem of Lieb that establishes the concavity of a certain trace function. The development relies on a deep result from quantum information theory, the joint convexity of quantum relative entropy, as well as a recent argument due to Carlen and Lieb. 1. Introduction In his 1973 paper on trace functions, Lieb establishes an important concavity theorem [Lie73, Thm. 6] concerning the trace exponential. Theorem 1 (Lieb). Let H be a fixed self-adjoint matrix. The map (1) A −→ tr exp (H +logA) is concave on the positive-definite cone. The most direct proof of Theorem 1 is due to Epstein [Eps73]; see Ruskai’s papers [Rus02, Rus05] for a condensed version of this argument. Lieb’s original proof develops the concavity of the function (1) as a corollary of another deep concavity theorem [Lie73, Thm. 1]. In fact, many convexity and concavity theorems for trace functions are equivalent with each other, in the sense that the mutual implications follow from relatively easy arguments. See [Lie73, §5] and [CL08, §5] for discussions of this point. The goal of this paper is to demonstrate that a modicum of convex analysis allows us to derive Theorem 1 directly from another major theorem, the joint convexity of the quantum relative entropy. -
Classical, Quantum and Total Correlations
Classical, quantum and total correlations L. Henderson∗ and V. Vedral∗∗ ∗Department of Mathematics, University of Bristol, University Walk, Bristol BS8 1TW ∗∗Optics Section, Blackett Laboratory, Imperial College, Prince Consort Road, London SW7 2BZ Abstract We discuss the problem of separating consistently the total correlations in a bipartite quantum state into a quantum and a purely classical part. A measure of classical correlations is proposed and its properties are explored. In quantum information theory it is common to distinguish between purely classical information, measured in bits, and quantum informa- tion, which is measured in qubits. These differ in the channel resources required to communicate them. Qubits may not be sent by a classical channel alone, but must be sent either via a quantum channel which preserves coherence or by teleportation through an entangled channel with two classical bits of communication [?]. In this context, one qubit is equivalent to one unit of shared entanglement, or `e-bit', together with two classical bits. Any bipartite quantum state may be used as a com- munication channel with some degree of success, and so it is of interest to determine how to separate the correlations it contains into a classi- cal and an entangled part. A number of measures of entanglement and of total correlations have been proposed in recent years [?, ?, ?, ?, ?]. However, it is still not clear how to quantify the purely classical part of the total bipartite correlations. In this paper we propose a possible measure of classical correlations and investigate its properties. We first review the existing measures of entangled and total corre- lations. -
Quantum Information Science
Quantum Information Science Seth Lloyd Professor of Quantum-Mechanical Engineering Director, WM Keck Center for Extreme Quantum Information Theory (xQIT) Massachusetts Institute of Technology Article Outline: Glossary I. Definition of the Subject and Its Importance II. Introduction III. Quantum Mechanics IV. Quantum Computation V. Noise and Errors VI. Quantum Communication VII. Implications and Conclusions 1 Glossary Algorithm: A systematic procedure for solving a problem, frequently implemented as a computer program. Bit: The fundamental unit of information, representing the distinction between two possi- ble states, conventionally called 0 and 1. The word ‘bit’ is also used to refer to a physical system that registers a bit of information. Boolean Algebra: The mathematics of manipulating bits using simple operations such as AND, OR, NOT, and COPY. Communication Channel: A physical system that allows information to be transmitted from one place to another. Computer: A device for processing information. A digital computer uses Boolean algebra (q.v.) to processes information in the form of bits. Cryptography: The science and technique of encoding information in a secret form. The process of encoding is called encryption, and a system for encoding and decoding is called a cipher. A key is a piece of information used for encoding or decoding. Public-key cryptography operates using a public key by which information is encrypted, and a separate private key by which the encrypted message is decoded. Decoherence: A peculiarly quantum form of noise that has no classical analog. Decoherence destroys quantum superpositions and is the most important and ubiquitous form of noise in quantum computers and quantum communication channels. -
On the Complementary Quantum Capacity of the Depolarizing Channel
On the complementary quantum capacity of the depolarizing channel Debbie Leung1 and John Watrous2 1Institute for Quantum Computing and Department of Combinatorics and Optimization, University of Waterloo 2Institute for Quantum Computing and School of Computer Science, University of Waterloo October 2, 2018 The qubit depolarizing channel with noise parameter η transmits an input qubit perfectly with probability 1 − η, and outputs the completely mixed state with probability η. We show that its complementary channel has positive quantum capacity for all η > 0. Thus, we find that there exists a single parameter family of channels having the peculiar property of having positive quantum capacity even when the outputs of these channels approach a fixed state independent of the input. Comparisons with other related channels, and implications on the difficulty of studying the quantum capacity of the depolarizing channel are discussed. 1 Introduction It is a fundamental problem in quantum information theory to determine the capacity of quantum channels to transmit quantum information. The quantum capacity of a channel is the optimal rate at which one can transmit quantum data with high fidelity through that channel when an asymptotically large number of channel uses is made available. In the classical setting, the capacity of a classical channel to transmit classical data is given by Shannon’s noisy coding theorem [12]. Although the error correcting codes that allow one to approach the capacity of a channel may involve increasingly large block lengths, the capacity expression itself is a simple, single letter formula involving an optimization over input distributions maximizing the input/output mutual information over one use of the channel. -
Quantum Information Chapter 10. Quantum Shannon Theory
Quantum Information Chapter 10. Quantum Shannon Theory John Preskill Institute for Quantum Information and Matter California Institute of Technology Updated June 2016 For further updates and additional chapters, see: http://www.theory.caltech.edu/people/preskill/ph219/ Please send corrections to [email protected] Contents 10 Quantum Shannon Theory 1 10.1 Shannon for Dummies 2 10.1.1 Shannon entropy and data compression 2 10.1.2 Joint typicality, conditional entropy, and mutual infor- mation 6 10.1.3 Distributed source coding 8 10.1.4 The noisy channel coding theorem 9 10.2 Von Neumann Entropy 16 10.2.1 Mathematical properties of H(ρ) 18 10.2.2 Mixing, measurement, and entropy 20 10.2.3 Strong subadditivity 21 10.2.4 Monotonicity of mutual information 23 10.2.5 Entropy and thermodynamics 24 10.2.6 Bekenstein’s entropy bound. 26 10.2.7 Entropic uncertainty relations 27 10.3 Quantum Source Coding 30 10.3.1 Quantum compression: an example 31 10.3.2 Schumacher compression in general 34 10.4 Entanglement Concentration and Dilution 38 10.5 Quantifying Mixed-State Entanglement 45 10.5.1 Asymptotic irreversibility under LOCC 45 10.5.2 Squashed entanglement 47 10.5.3 Entanglement monogamy 48 10.6 Accessible Information 50 10.6.1 How much can we learn from a measurement? 50 10.6.2 Holevo bound 51 10.6.3 Monotonicity of Holevo χ 53 10.6.4 Improved distinguishability through coding: an example 54 10.6.5 Classical capacity of a quantum channel 58 ii Contents iii 10.6.6 Entanglement-breaking channels 62 10.7 Quantum Channel Capacities and Decoupling -
The Squashed Entanglement of a Quantum Channel
The squashed entanglement of a quantum channel Masahiro Takeoka∗y Saikat Guhay Mark M. Wildez January 22, 2014 Abstract This paper defines the squashed entanglement of a quantum channel as the maximum squashed entanglement that can be registered by a sender and receiver at the input and output of a quantum channel, respectively. A new subadditivity inequality for the original squashed entanglement measure of Christandl and Winter leads to the conclusion that the squashed en- tanglement of a quantum channel is an additive function of a tensor product of any two quantum channels. More importantly, this new subadditivity inequality, along with prior results of Chri- standl, Winter, et al., establishes the squashed entanglement of a quantum channel as an upper bound on the quantum communication capacity of any channel assisted by unlimited forward and backward classical communication. A similar proof establishes this quantity as an upper bound on the private capacity of a quantum channel assisted by unlimited forward and backward public classical communication. This latter result is relevant as a limitation on rates achievable in quantum key distribution. As an important application, we determine that these capacities can never exceed log((1 + η)=(1 − η)) for a pure-loss bosonic channel for which a fraction η of the input photons make it to the output on average. The best known lower bound on these capacities is equal to log(1=(1 − η)). Thus, in the high-loss regime for which η 1, this new upper bound demonstrates that the protocols corresponding to the above lower bound are nearly optimal. -
Geometry of Quantum States
Geometry of Quantum States Ingemar Bengtsson and Karol Zyczk_ owski An Introduction to Quantum Entanglement 12 Density matrices and entropies A given object of study cannot always be assigned a unique value, its \entropy". It may have many different entropies, each one worthwhile. |Harold Grad In quantum mechanics, the von Neumann entropy S(ρ) = Trρ ln ρ (12.1) − plays a role analogous to that played by the Shannon entropy in classical prob- ability theory. They are both functionals of the state, they are both monotone under a relevant kind of mapping, and they can be singled out uniquely by natural requirements. In section 2.2 we recounted the well known anecdote ac- cording to which von Neumann helped to christen Shannon's entropy. Indeed von Neumann's entropy is older than Shannon's, and it reduces to the Shan- non entropy for diagonal density matrices. But in general the von Neumann entropy is a subtler object than its classical counterpart. So is the quantum relative entropy, that depends on two density matrices that perhaps cannot be diagonalized at the same time. Quantum theory is a non{commutative proba- bility theory. Nevertheless, as a rule of thumb we can pass between the classical discrete, classical continuous, and quantum cases by choosing between sums, integrals, and traces. While this rule of thumb has to be used cautiously, it will give us quantum counterparts of most of the concepts introduced in chapter 2, and conversely we can recover chapter 2 by restricting the matrices of this chapter to be diagonal. 12.1 Ordering operators The study of quantum entropy is to a large extent a study in inequalities, and this is where we begin. -
Quantum Channel Capacities
Quantum Channel Capacities Graeme Smith IBM TJ Watson Research Center 1101 Kitchawan Road Yorktown NY 10598 [email protected] Abstract—A quantum communication channel can be put to must consider the quantum capacity of our channel, and if we many uses: it can transmit classical information, private classical have access to arbitrary quantum correlations between sender information, or quantum information. It can be used alone, with and receiver the relevant capacity is the entanglement assisted shared entanglement, or together with other channels. For each of these settings there is a capacity that quantifies a channel’s capacity. In general, these capacities are all different, which potential for communication. In this short review, I summarize gives a variety of inequivalent ways to quantify the value of what is known about the various capacities of a quantum channel, a quantum channel for communication. including a discussion of the relevant additivity questions. I The communication capacities of a quantum channel are also give some indication of potentially interesting directions for not nearly as well understood as their classical counterparts, future research. and many basic questions about quantum capacities remain open. The purpose of this paper is to give an introduction to a I. INTRODUCTION quantum channel’s capacities, summarize what we know about The capacity of a noisy communication channel for noise- them, and point towards some important unsolved problems. less information transmission is a central quantity in the study of information theory [1]. This capacity establishes the II. QUANTUM STATES AND CHANNELS ultimate boundary between communication rates which are The states of least uncertainty in quantum mechanics are achievable in principle and those which are not.