1. Introduction
Theoretical foundation supporting today’s information-oriented society is Information Theory founded by Shannon [
1] about 60 years ago. Generally, this theory can treat the efficiency of the information transmission by using measures of complexity, that is, the entropy, in the commutative system of signal space. The information theory is based on the entropy theory that is formulated mathematically. Before Shannon’s work, the entropy was first introduced in thermodynamics by Clausius and in statistical mechanics by Boltzmann. These entropies are the criteria to characterize a property of the physical systems. Shannon’s construction of entropy is a use of the discrete probability theory based on the idea that “information obtained from a system with a large vagueness has been highly profitable”, and he introduced (1) the entropy measuring the amount of information of the state of system and (2) the mutual entropy (information) representing the amount of information correctly transmitted from the initial system to the final system through a channel. This entropy theory agreed with the development of the probability theory, due to Kolmogorov, gives a mathematical foundation of the classical information theory with the relative entropy of two states by Kullback-Leibler [
2] and the mutual entropy by Gelfand-Kolmogorov-Yaglom  [
3,
4] on the continuous probability space. In addition, a channel of the discrete systems given by a transition probability was generalized to the integral kernel theory. The channel of the continuous systems is expressed as a state change on the commutative probability space by introducing the averaged operator by Umegaki and it is extended to the quantum channel (prob. measure) describing a state change in the noncommutative systems [
5]. Since the present optical communication uses laser signal, it is necessary to construct new information theory dealing with those quantum quantities in order to discuss the efficiency of information transmission of optical communication processes rigorously. It is called the quantum information theory extending the important measures such as the entropy, the relative entropy and the mutual entropy formulated by Shannon et al into the quantum systems. The study of the entropy in quantum system was begun by von Neumann [
6] in 1932, and the quantum relative entropy was introduced by Umegaki [
7], and it is extended to general quantum system by Araki [
8,
9] , Uhlmann [
10] and Donald [
11]. In the quantum information theory, one of the important subjects is to examine how much information correctly carried through a channel, so that it is necessary to extend the mutual entropy of the classical system to the quantum system.
The mutual entropy in the classical system is defined by the joint probability distribution between input and output systems. However, the joint probability does not exist generally (see [
12]) in quantum systems. The compound state devised in [
13,
14] gives a key solving this problem. It is defined through the Schatten decomposion [
15] (one dimensional orthogonal decomposition) of the input state and the quantum channel. Ohya introduced the quantum mutual entropy based on the compound state in 1983 [
13,
16]. Since it satisfies Shannon’s inequalities, it describes the amount of information correctly transmitted from input system through a quantum channel. By using fundamental entropies such as the von Neumann entropy and the Ohya mutual entropy, the complete quantum version of Shannon’s information theory was formulated.
The quantum entropy for a density operator was defined by von Neumann [
6] about 20 years before the Shannon entropy appeared. The properties of entropy are summarized in [
17]. Main properties of the quantum relative entropy are taken from the articles [
8,
9,
10,
11,
17,
18,
19,
20]. The quantum mutual entropy was introduced by Holevo, Livitin, Ingarden [
21,
22] for classical input and output passing through a possible quantum channel. The complete quantum mechanical mutual entropy was defined by Ohya [
13], and its generalization to C*-algebra was done in [
23]. The applications of the mutual entropy have been been studied in various fields [
16,
24,
25,
26,
27,
28,
29]. The applications of channel were given in [
13,
16,
30,
31,
32,
33].
Concerning quantum communication, the following studies have been done. The characterization of quantum communication or stochastic procesess is discussed and the beam splitting was rigorously studied by Fichtner, Freutenberg and Liebscher [
34,
35]. The transition expectation was introduced by Accardi [
36] to study quantum Markov process [
37]. The noisy optical channel was discussed in [
28]. In quantum optics, a linear amplifier have been discussed by several authours [
38,
39], and its rigorous expression given here is in [
30]. The channel capacities are discussed here based on the papers [
40,
41]. The bound of the capacity has been studied by first Holevo [
42] and many others [
39,
41,
43].
The entangled state is an important concept for quantum theory and it has been studied recently by several authors and its rigorous mathematical study was given in [
44,
45].
Let us comment general entropies of states in C*-dynamical systems. The C*-entropy was introduced in [
23] and its property is discussed in [
25,
26,
46]. The relative entropy for two general states was introduced by Araki [
8,
9] in von Neumann algebra and Uhlmann [
10] in *-algebra. The mutual entropy in C*-algebra was introduced by Ohya [
16]. Other references of quantum entropy is totally discussed in a book [
17].
The classical dynamical (or Kolmogorov-Sinai) entropy 
 [
47] for a measure preserving transformation 
T was defined on a message space through finite partitions of the measurable space.
The classical coding theorems of Shannon are important tools to analyse communication processes which have been formulated by the mean dynamical entropy and the mean dynamical mutual entropy. The mean dynamical entropy represents the amount of information per one letter of a signal sequence sent from an input source, and the mean dynamical mutual entropy does the amount of information per one letter of the signal received in an output system.
The quantum dynamical entropy (QDE) was studied by Connes-Størmer [
48], Emch [
49], Connes-Narnhofer-Thirring [
50], Alicki-Fannes [
51], and others [
52,
53,
54,
55,
56]. Their dynamical entropies were defined in the observable spaces. Recently, the quantum dynamical entropy and the quantum dynamical mutual entropy were studied by the present authors [
16,
29]: (1) The dynamical entropy is defined in the state spaces through the complexity of Information Dynamics [
57]. (2) It is defined through the quantum Markov chain (QMC) was done in [
58]. (3) The dynamical entropy for a completely positive (CP) maps was introduced in [
59].
In this review paper, we give an overview of the entropy theory mentioned above. Their details of are discussed in the following book [
17,
60].
  2. Setting of Quantum Systems
We first summarize mathematical description of both classical and quantum systems.
(1) Classical System: Let  be the set of all real random variables on a probability measure space  and  be the set all probability measures on a measurable space .  and  represent a observable and a state of classical systems, respectively. The expectation value of the observable  with respect to a state  is given by 
(2) Usual Quantum Systems: We denote the set of all bounded linear operators on a Hilbert space  by , and the set of all density operators on  by . A hermite operator  and  denote an observable and a state of usual quantum systems. The expectation value of the observable  with respect to a state  is obtained by 
(3) General Quantum System: More generally, let  be a C*-algebra (i.e., complex normed algebra with involution * such that ,  and complete w.r.t. ) and  be the set of all states on  (i.e., positive continuous linear functionals φ on  such that  if the unit I is in ).
If  is a unital map from a C*-algebra  to a C*-algebra  then its dual map  is called the channel. That is, tr tr Remark that the algebra  sometimes will be denoted 
Such algebraic approach contains both classical and quantum theories. The description of a classical probabilistic system (CDS), a usual quantum dynamical system(QDS) and a general quantum dynamical system(GQDS) are given in the following Table:
      
  
    
  
  
    Table. 1.1.
    Descriptions of CDS, QDS and GQDS.
  
 
        
        Table. 1.1.
    Descriptions of CDS, QDS and GQDS. 
        |  | CDS | QDS | GQDS | 
|  | real r.v. | Hermitian operator | self-adjoint element | 
| observable | in | A on | A in | 
|  |  | (self adjoint operator | C*-algebra | 
|  |  | in ) |  | 
| state | probability measure | density operator | p.l.fnal | 
|  |  | ρ on | with | 
| expectation |  |  |  | 
      
   3. Communication Processes
We discuss the quantum communication processes in this section.
Let 
 be the infinite direct product of the alphabet 
  calling a message space. A coding is a one to one map Ξ from 
 to some space 
X which is called the coded space. This space 
X may be a classical object or a quantum object. For a quantum system, 
X may be a space of quantum observables on a Hilbert space 
, then the coded input system is described by 
. The coded output space is denoted by 
 and the decoded output space 
 is made by another alphabets. An transmission (map) Γ from 
X to 
 (actually its dual map as discussed below) is called 
a channel, which reflects the property of a physical device. With a decoding 
 the whole information transmission process is written as 
That is, a message  is coded to  and it is sent to the output system through a channel Γ, then the output coded message becomes  and it is decoded to  at a receiver.
Then the occurrence probability of each message in the sequence 
 of 
N messages is denoted by 
 which is a state in a classical system. If Ξ is a quantum coding, then 
 is a quantum object (state) such as a coherent state. Here we consider such a quantum coding, that is, 
 is a quantum state, and we denote 
 by 
 Thus the coded state for the sequence 
 is written as 
 This state is transmitted through the dual map of Γ which is called a 
channel in the sequel. This channel (the dual of 
 is expressed by a completely positive mapping 
 in the sense of Chapter 5 , from the state space of 
X to that of 
 , hence the output coded quantum state 
 is 
 Since the information transmission process can be understood as a process of state (probability) change, when Ω and 
 are classical and 
X and 
 are quantum, the process is written as 
 where 
 (resp. 
) is the channel corresponding to the coding Ξ (resp. 
) and 
 (resp. 
) is the set of all density operators (states) on 
 (resp.
).
We have to be care to study the objects in the above transmission process. Namely, we have to make clear which object is going to study. For instance, if we want to know the information of a quantum state through a quantum channel Γ(or  then we have to take X so as to describe a quantum system like a Hilbert space and we need to start the study from a quantum state in quantum space  not from a classical state associated to messages. We have a similar situation when we treat state change (computation) in quantum computer.
  4. Quantum Entropy for Density Operators
The entropy of a quantum state was introduced by von Neumann. This entropy of a state 
ρ is defined by 
For a state 
 there exists a unique spectral decomposition 
 where 
 is an eigenvalue of 
ρ and 
 is the associated projection for each 
. The projection 
 is not one-dimensional when 
 is degenerated, so that the spectral decomposition can be further decomposed into one-dimensional projections. Such a decomposition is called a Schatten decomposition, namely, 
 where 
 is the one-dimensional projection associated with 
 and the degenerated eigenvalue 
 repeats dim
 times; for instance, if the eigenvalue 
 has the degeneracy 3, then 
. To simplify notations we shall write the Schatten decomposition as 
 where the numbers 
 form a probability distribution 
 This Schatten decomposition is not unique unless every eigenvalue is non-degenerated. Then the entropy (von Neumann entropy) 
 of a state 
ρ equals to the Shannon entropy of the probability distribution 
 :
Therefore the von Neumann entropy contains the Shannon entropy as a special case.
Let us summarize the fundamental properties of the entropy .
Theorem 1  For any density operator , the followings hold:- (1) 
- Positivity : . 
- (2) 
- Symmetry : Let  for an unitary operator U. Then  
- (3) 
- Concavity :  for any  and . 
- (4) 
- Additivity :  for any . 
- (5) 
- Subadditivity : For the reduced states  of ,  
- (6) 
- Lower Semicontinuity : If  as , then  
- (7) 
- Continuity : Let  be elements in  satisfying the following conditions : (i)  weakly as , (ii)  for some compact operator A, and (iii)  for the eigenvalues  of A. Then . 
- (8) 
- Strong Subadditivity : Let  and denote the reduced states  by  and  by . Then  and  
- (9) 
- Entropy increasing: (i) Let  be finite dimensional space. If the channel  is unital, that is, for the dual map Λ of  satisfies  then  (ii) For arbitrary Hilbert space , if the dual map Λ of the channel  satisfies , then  
  In order to prove Theorem, we need the following lemma.
Lemma 2  Let f be a convex  function on a proper domain and . Then - (1) 
- Klein’s inequality: tr 
- (2) 
- Peierls inequality:  tr for any CONS  in . (Remark: ) 
    5. Relative Entropy for Density Operators
For two states 
, the 
relative entropy is first defined by Umegaki 
 where 
 means that 
 for 
. Main properties of relative entropy are summarized as:
Theorem 3  The relative entropy satisfies the following properties: - (1) 
- Positivity:  iff  
- (2) 
- Joint Convexity :  for any . 
- (3) 
- Additivity : . 
- (4) 
- Lower Semicontinuity : If  and , then . Moreover, if there exists a positive number λ satisfying , then . 
- (5) 
- Monotonicity : For a channel  from  to ,  
- (6) 
- Lower Bound :  
- (7) 
- Invaiance under the unitary mapping:  where U is a unitary operator. 
  Let us extend the relative entropy to two positive operators instead of two states. If 
A and 
B are two positive Hermitian operators (not necessarily the states, 
i.e. not necessarily with unit traces) then we set 
 The following 
Bogoliubov inequality holds [
41].
This inequality gives us the upper bound of the channel capacity [
41].
  6. Channel and Lifting
The concept of the channel plays an important role for mathematical description of the quantum communication. The attenuation channel is one of the most important models to discuss in quantum optical communication [
13] . Moreover, there exists a special channel named ”lifting”, and it is useful to characterize quantum communication or stochastic procesess. Here, we briefly review the definition and fundamental properties of quantum channel and lifting [
17,
28,
29,
60].
  6.1. Quantum Channels
A general quantum system containing all systems such as discrete and continuous in both classical and quantum is described by a C*-algebra or a von Neumann algebra, so that we discuss the channeling transformation in C*-algebaric contexts. However it is enough for the readers who are not familiar with C*-algebra to imagine usual quantum system, for instace, regard  and  below as  and , respectively. Let  and  be C*-algebras and  and  be the set of all states on  and .
A channel is a mapping from  to . There exist channels with various properties.
Definition 5  Let  be an input system and  be an output system. Take any . - (1)
-  is linear if  holds for any . 
- (2)
-  is completely positive (CP) if  -  is linear and its dual  -  satisfies  -  for any  -  and any  - ,  - . 
- (3)
-  is Schwarz type if  and . 
- (4)
-  is stationary if  for any . - (Here  and  are groups of automorphisms of the algebra  and  respectively.) 
- (5)
-  is ergodic if  is stationary and . - (Here  is the set of extreme points of the set of all stationary states .) 
- (6)
-  is orthogonal if any two orthogonal states  (denoted by ) implies . 
- (7)
-  is deterministic if  is orthogonal and bijective. 
- (8)
- For a subset  of ,  is chaotic for  if  for any . 
- (9)
-  is chaotic if  is chaotic for . 
- (10)
- Stinespring-Sudarshan-Kraus representation: a completely positive channel  -  can be represented as  -  Here  -  are bounded operators in  H- . 
  Most of channels appeared in physical processes are CP channels. Examples of such channels are the followings: Take a density operator ρ  as an input state.
(1) Unitary evolution: Let 
H be the Hamiltonian of a system. 
 where 
.
 (2) Semigroup evolution: Let 
 be an one parameter semigroup on 
. 
 (3) Quantum measurement: If a measuring apparatus is prepared by an positive operator valued measure 
 then the state 
ρ changes to a state 
 after this measurement, 
 (4) Reduction: If a system 
 interacts with an external system 
 described by another Hilbert space 
 and the initial states of 
 and 
 are 
ρ and 
σ, respectively, then the combined state 
 of 
 and 
 at time 
t after the interaction between two systems is given by 
 where 
 with the total Hamiltonian 
H of 
 and 
. A channel is obtained by taking the partial trace w.r.t. 
 such as 
 (5) Optical communication processes: Quantum communication process is described by the following scheme [
13]. 
  The above maps 
 are given as 
 where 
ν is a noise coming from the outside of the system. The map 
 is a certain channel determined by physical properties of the device transmitting information. Hence the channel for the above process is given as 
(6)Attenuation process: Based on the construction of the optical communication processes of (5), the attenuation channel is defined as follows [
13]: Take 
 vacuum state and 
 given by 
 Then the output state of the attenuation channel 
 is obtained by 
  (
) is called a transmission rate of the attenuation channel 
. In particular, for a coherent input state 
, one has 
 which is called a beam splitting operator.
 (7) Noisy optical channel: Based on (5), the noisy optical channel is defined as follows [
28]: Take a noise state 
, 
 photon number state of 
 and a linear mapping 
 as 
 with 
 where 
 Then the output state of the noisy optical channel 
 is defined by 
 for the input state 
. In particular, for a coherent input state 
 and a coherent noise state 
, we obtain 
 which is called a generalized beam splitting operator.
   6.2. Liftings
There exists a special channel named ”lifting”, and it is a useful concept to characterize quantum communication or stochastic procesess. It can be a mathematical tool to describe a process in quantum algorithm, so that we will explain its foundation here.
Definition 6  Let  be C*-algebras and let  be a fixed C*-tensor product of  and . A lifting from  to  is a weak *-continuous map  If  is affine and its dual is a completely positive map, we call it a linear lifting; if it maps pure states into pure states, we call it pure.
 The algebra 
 can be that of the output, namely, 
 above. Note that to every lifting from 
 to 
 we can associate two channels: one from 
 to 
, defined by 
 another from 
 to 
, defined by 
 In general, a state 
 such that 
 is called a compound state of the states 
 and 
. In classical probability theory, also the term coupling between 
 and 
 is used.
The following problem is important in several applications: Given a state 
 and a channel 
, find a standard lifting 
 such that 
 is a compound state of 
 and 
. Several particular solutions of this problem have been proposed by Ohya [
13,
14], Ceccini and Petz [
63], however an explicit description of all the possible solutions to this problem is still missing.
Definition 7  A lifting from  to  is called nondemolition for a state  if  is invariant for  i.e.
, if for all   The idea of this definition being that the interaction with system 2 does not alter the state of system 1.  Definition 8  Let  be C*-algebras and let  be a fixed C*-tensor product of  and . A transition expectation from  to  is a completely positive linear map  satisfying   An input signal is transmitted and received by an apparatus which produces an output signal. Here  (resp. ) is interpreted as the algebra of observables of the input (resp. output) signal and  describes the interaction between the input signal and the receiver as well as the preparation of the receiver. If  is the input signal, then the state  is the state of the (observed) output signal. Therefore in the reduction dynamics discussed before, the correspondence from a state ρ to the interacting state  gives us a time dependent lifting.
Further another important lifting related to this signal transmission is one due to a quantum communication process discussed above. In several important applications, the state  of the system before the interaction (preparation, input signal) is not known and one would like to know this state knowing only , i.e., the state of the apparatus after the interaction (output signal). From a mathematical point of view this problem is not well posed, since the map  is usually not invertible. The best one can do in such cases is to acquire a control on the description of those input states which have the same image under  and then choose among them according to some statistical criterion.
In the following we rewrite some communication processes by using liftings.
Example 9 (1) : Isometric lifting.  Let  be an isometry  Then the map  is a transition expectation in the sense of Accardi, and the associated lifting maps a density matrix  on  into  on  Liftings of this type are called isometric. Every isometric lifting is a pure lifting, which is applied to some of quantum algorithm such as Shor’s.  These extend linearly to isometry, and their isometric liftings are neither of convex product type nor nondemolition type.
Example 10 (2) : The compound lifting.  Let  be a channel. For any  in the closed convex hull of the external states, fix a decomposition of  as a convex combination of extremal states in   where μ is a Borel measure on  with support in the extremal states, and define  Then  is a lifting, nonlinear even if  is linear, and it is a nondemolition type. The most general lifting, mapping  into the closed convex hull of the extermal product states on  is essentially of this type. This nonlinear nondemolition lifting was first discussed by Ohya to define the compound state and the mutual entropy as explained before. However the above is a bit general because we shall weaken the condition that μ is concentrated on the extremal states.  Therefore once a channel is given, by which a lifting of convex product type can be constructed. For example, the von Neumann quantum measurement process is written, in the terminology of lifting, as follows: Having measured a compact observable 
 (spectral decomposition with 
) in a state 
ρ, the state after this measurement will be 
 and a lifting 
, of convex product type, associated to this channel 
 and to a fixed decomposition of 
ρ as 
ρ (
) is given by :
Before closing this section, we reconsider noisy channel, attenutaion channel and amplifier process (lifting) in optical communication.
Example 11 (3) :  The attenuation (or beam splitting) lifting. 
It is the particular isometric lifting characterized by the properties.   is characterized by the expression  where  is the normalized coherent vector parametrized by  and  are such that   Notice that this liftings maps coherent states into products of coherent states. So it maps the simplex of the so called classical states (i.e., the convex combinations of coherent vectors) into itself. Restricted to these states it is of convex product type explained below, but it is not of convex product type on the set of all states.
Denoting, for 
 the coherent state on 
 namely, 
 then for any 
  so that this lifting is not nondemolition. These equations mean that, by the effect of the interaction, a coherent signal (beam) 
 splits into 2 signals (beams) still coherent, but of lower intensity, but the total intensity (energy) is preserved by the transformation.
Finally we mention two important beam splitting which are used to discuss quantum gates and quantum teleportation [
64,
65].
(1) Superposed beam splitting:
(2) Beam splitting with two inputs and two output: Let 
 and 
 be two input coherent vectors. Then 
Example 12 (4) Amplifier channel:  To recover the loss, we need to amplify the signal (photon). In quantum optics, a linear amplifier is usually expressed by means of annihilation operators a and b on  and , respectively :  where  is a constant and c satisfies CCR i.e., ) on . This expression is not convenient to compute several informations like entropy. The lifting expression of the amplifier is good for such use and it is given as follows:  Let 
 with 
 and 
 be the eigenvector of 
c : 
. For two coherent vectors 
 on 
 and 
 on 
, 
 can be written by the squeezing expression : 
 and the lifting is defined by an isometry 
 such that 
 The channel of the amplifier is 
  7. Quantum Mutual Entropy
Quantum relative entropy was introduced by Umegeki and generalized by Araki, Uhlmann. Then a quantum analogue of Shannon’s mutual entropy was considered by Levitin, Holevo, Ingarden for classical input and output passing through a possible quantum channel, in which case, as discussed below, the Shannon theory is essentially applied. Thus we call such quantum mutual entropy semi-quantum mutual entropy in the sequel. The fully quantum mutual entropy, namely, for quantum input and quantum output with quantum channel, was introduced by Ohya, which is called the quantum mutual entropy. It could be generalized to a general quantum system described by a C*-algebra.
The quantum mutual entropy clearly contains the semi-quantum mutual entropy as shown below. We mainly discuss the quantum mutual entropy in usual quantum system described by a Hilbert space, and its generalization to C-systems will be explained briefly for future use (e.g., relativistic quantum information ) in the last section of this Chapter. Note that the general mutual entropy contains all other cases including the measure theoretic definition of Gelfand and Yaglom.
Let  be a Hilbert space for an input space, and an output space is described by another Hilbert space  , often one takes . A channel from the input system to the output system is a mapping  from  to .
An input state ρ  is sent to the output system through a channel , so that the output state is written as  Then it is important to investigate how much information of ρ is correctly sent to the output state  This amount of information transmitted from input to output is expressed by the mutual entropy (or mutual information).
The quantum mutual entropy was introduced on the basis of von Neumann entropy for purely quantum communication processes. The mutual entropy depends on an input state ρ and a channel , so it is denoted by , which should satisfy the following conditions:
(1) The quantum mutual entropy is well-matched to the von Neumann entropy. That is, if a channel is trivial, i.e.,  identity map, then the mutual entropy equals to the von Neumann entropy: .
(2) When the system is classical, the quantum mutual entropy reduces to classical one.
(3) Shannon’s type fundamental inequality  is held.
In order to define the quantum mutual entropy, we need the quantum relative entropy and the joint state (it is called ”compound state” in the sequel) describing the correlation between an input state 
ρ and the output state 
 through a channel 
. A finite partition of Ω in classical case corresponds to an orthogonal decomposition 
 of the identity operator 
I of 
 in quantum case because the set of all orthogonal projections is considered to make an event system in a quantum system. It is known that the following equality holds 
 and the supremum is attained when 
 is a Schatten decomposition of 
 Therefore the Schatten decomposition is used to define the compound state and the quantum mutual entropy.
The compound state 
 (corresponding to joint state in classical systems) of 
ρ and 
 was introduced by Ohya in 1983. It is given by 
 where 
E stands for a Schatten decomposition 
 of 
ρ so that the compound state depends on how we decompose the state 
ρ into basic states (elementary events), in other words, how to see the input state. It is easy to see that tr
Applying the relative entropy 
 for two compound states 
 and 
 (the former includes a certain correlation of input and output and the later does not), we can define the 
Ohya’s quantum mutual entropy (information) as 
 where the supremum is taken over all Schatten decompositions of 
ρ because this decomposition is not always unique unless every eigenvalue of 
ρ is not degenerated. Some computations reduce it to the following form for a linear channel.
It is easy to see that the quantum mutual entropy satisfies all conditions (1)∼(3) mentioned above.
When the input system is classical, an input state 
ρ is given by a probability distribution or a probability measure. In either case, the Schatten decomposition of 
ρ is unique, namely, for the case of probability distribution ; 
  where 
 is the delta measure, that is, 
 Therefore for any channel 
 the mutual entropy becomes 
 which equals to the following usual expression when one of the two terms is finite for an infinite dimentional Hilbert space:
 The above equality has been taken by Levitin and Holevo (LH for short in the sequel), which is one associated with a classical-quantum channel. Thus the Ohya’s quantum mutual entropy (we call it the quantum mutual entropy in the sequel) contains the LH quantum mutual entropy (we call it the semi-quantum mutual entropy in the sequel) as a special one.
Note that the definition of the quantum mutual entropy might be written as 
 where 
 is the set of all orthogonal finite decompositions of 
 Here 
 is orthogonal to 
 (denoted by 
 means that the range of 
 is orthogonal to that of 
 We briefly explain this equality in the next theorem.
Theorem 14  One has 
 Moreover the following fundamental inequality follows from the monotonicity of relative entropy :
Theorem 15 (Shannon’s inequality)   For given two channels 
 and 
 one has the 
quantum data processing inequality. That is, 
 The second inequality follows from monotonicity of the relative entropy.
This is analogous to the classical data processing inequality for a Markov process 
  where 
 is the mutual information between random variables 
X and 
The mutual entropy is a measure for not only information transmission but also description of state change, so that this quantity can be applied to several topics in quantum dynamics. It can be also applied to some topics in quantum computer or computation to see the ability of information transmission.
  8. Some Applications to Statistical Physics
  8.1. Ergodic theorem
We have an ergodic type theorem with respect to quantum mutual entropy.
Theorem 16  Let a state φ be given by  . - (1) 
- If a channel  is deterministic, then . 
- (2) 
- If a channel  is chaotic, then . 
- (3) 
- If ρ is a faithful state and the every eigenvalue of ρ is nondegenerate, then . 
 (Remark: Here 
ρ is said to be faithful if tr
 implies 
 )
    8.2. CCR and channel
We discuss the attenuation channel in the context of the Weyl algebra.
Let 
T be a symplectic transformation of 
 to 
, 
i.e., 
. Then there is a homomorphism 
 such that 
 We may regard the Weyl algebra 
) as 
), and given a state 
ψ on CCR(
), a channeling transformation arises as 
 where the input state 
ω is an arbitrary state of 
 and 
 (this 
ψ is a noise state above). To see a concrete example discussed in [
13], we choose 
, 
 and 
 If 
 holds for the numbers 
a and 
b, this 
F is an isometry, and a symplectic transformation, and we arrive at the channeling transformation 
 In order to have an alternative description of Λ in terms of density operators acting of 
 we introduce the linear operator 
 defined by 
 we have 
 hence 
Theorem 17  Let ω be a state of CCR(H) which has a density D in the Fock representation. Then the output state  of the attenuation channel has density tr in the Fock representation.
 The lemma says that  is really the same to the noisy channel with 
We note that 
 the dual of 
 is a so-called quasifree completely positive mapping of 
 given as 
Theorem 18  If ψ is a regular state of , that is  is a continuous function on  for every , then  pointwise, where φ is a Fock state.  It is worth noting that the singular state 
 is an invariant state of CCR(
H). On the other hand, the proposition applies to states possesing density operator in the Fock representation. Therefore, we have
Corollary 19   regarded as a channel of  has a unique invariant state, the Fock state, and correspondingly  is ergodic.
  is not only ergodic but it is completely dissipative in the sense that 
 may happen only in the trivial case when 
A is a multiple of the identity, which was discussed by M. Fannes and A. Verbeure. In fact, 
 where 
 is given by (
1) and (
3) and 
 is a quasi-free state.
  8.3. Irreversible processes
Irreversible phenomena can be treated by several different methods. One of them is due to the entropy change. However, it is difficult to explain the entropy change from reversible equations of motion such as Schrodinger equation, Liouville equation. Therefore we need some modifications to explain the irreversibility of nature:
(i) QM + "α", where α represents an
effect of noise, the coarse graining, etc.
Here we discuss some trials concerning (i) and (iii) above, essentially done in [
16]. Let 
ρ be a state and 
 be some channels. Then we ask
(1) ?
(2) ?
(3) Consider the change of . ( should be decreasing!)
  8.4. Entropy change in linear response dynamics
We first discuss the entropy change in the linear response dynamics. Let H be a lower bounded Hamiltonian and take
For a KMS state 
φ given by a density operator 
ρ and a perturbation 
 (
), the perturbed time evolution is defined by a Dyson series:
 and the perturbed state is 
 where 
 The linear response time evolution and the linear response perturbed state are given by 
  This linear response perturbed state 
 is written as 
 where 
 The linear response time dependent state is 
 Put 
  The change of the linear response entropy 
 is shown in the following theorem [
16].
Theorem 20  If  goes to  as  and , then  as .
 Remark: Even when 
 we always have 
 Concerning the entropy change in exact dynamics, we have the following general result [
16]:
Theorem 21  Let :  be a channel satisfying  Then     8.5. Time development of mutual entropy
Frigerio studied the approach to stationarity of an open system in [
68]. Let an input 
 and an output 
 be a same von Neumann algebra and 
 be a dynamical semigroup (
i.e., 
 is a weak* continuous semigroup and 
 is a normal channel) on 
 having at least one faithful normal stationary state 
θ (
i.e., 
 for any 
). For this 
, put 
 and 
 Then 
 is a von Neumann subalgebra of 
. Frigerio proved the following theorem [
68].
Theorem 22  (1) There exists a conditional expectation  from  to .
(2) When , for any normal states ω,  converges to a stationary state in the w*- sense.
 From the above theorem, we obtain [
16].
Theorem 23  For a normal channel  and a normal state φ, if a measure , is orthogonal and if  holds and A is type I, then  decreases in time and approaches to  as .
 This theorem tells that the mutual entropy decreases with respect to time if the system is dissipative, so that the mutual entropy can be a measure for the irreversibility.
  9. Entropies for General Quantum States
We briefly discuss some basic facts of the entropy theory for general quantum systems, which might be needed to treat communication (computation) process from general standing point, that is, independently from classical or quantum.
Let 
 be a C*-system. The entropy (uncertainty) of a state 
 seen from the reference system, a weak *-compact convex subset of the whole state space 
 on the C
-algebra 
, was introduced by Ohya [
16]. This entropy contains von Neumann’s entropy and classical entropy as special cases.
Every state 
 has a maximal measure 
μ pseudosupported on 
 (extreme points in 
) such that 
 The measure 
μ giving the above decomposition is not unique unless 
 is a Choquet simplex (
i.e., for the set 
, define an order such that 
 iff 
, 
 is a Choquet simplex if 
 is a lattice for this order), so that we denote the set of all such measures by 
. Take 
 where 
 is the delta measure concentrated on 
 . Put 
 for a measure 
.
Definition 24  The entropy of a general state  w.r.t.  is defined by  When 
 is the total space 
 we simply denote 
 by 
  This entropy (mixing -entropy) of a general state φ satisfies the following properties.
Theorem 25  When  and  (i.e.
,  for any ) with a unitary operator , for any state φ given by  with a density operator ρ, the following facts hold: - (1)
- . 
- (2)
- If φ is an α-invariant faithful state and every eigenvalue of ρ is non-degenerate, then  where  is the set of all α-invariant faithful states. 
- (3)
- If , then , where K is the set of all KMS states. 
  Theorem 26  For any  , we have - (1) 
- . 
- (2) 
- . 
  This  (or mixing) entropy gives a measure of the uncertainty observed from the reference system  so that it has the following merits : Even if the total entropy  is infinite,  is finite for some , hence it explains a sort of symmetry breaking in . Other similar properties as  hold for . This entropy can be appllied to characterize normal states and quantum Markov chains in von Neumann algebras.
The relative entropy for two general states φ and ψ was introduced by Araki and Uhlmann and their relation is considered by Donald and Hiai et al.
<
Araki’s relative entropy> 
[8,9]Let 
 be 
σ-finite von Neumann algebra acting on a Hilbert space 
 and 
 be normal states on 
 given by 
 and 
 with 
 (a positive natural cone)
. The operator 
 is defined by 
 on the domain 
, where 
 is the projection from 
 to 
, the 
 -support of 
y. Using this 
, the relative modular operator 
 is defined as 
, whose spectral decomposition is denoted by 
 ( 
 is the closure of 
 ). Then the Araki relative entropy is given by
Definition 27   where  means that  implies  for  .  <
Uhlmann’s relative entropy> 
[10]Let 
 be a complex linear space and 
 be two seminorms on 
. Moreover, let 
 be the set of all positive hermitian forms 
α on 
 satisfying 
 for all 
. Then the quadratical mean 
 of 
p and 
q is defined by 
There exists a family of seminorms 
 of 
 for each 
 satisfying the following conditions:
- (1)
- For any ,  is continuous in t, 
- (2)
- , 
- (3)
- , 
- (4)
- . 
This seminorm 
 is denoted by 
 and is called the quadratical interpolation from 
p to 
q. It is shown that for any positive hermitian forms 
, there exists a unique function 
 of 
 with values in the set 
 such that 
 is the quadratical interpolation from 
 to 
. The relative entropy functional 
 of 
α and 
β is defined as 
 for 
. Let 
 be a *-algebra 
 and 
 be positive linear functionals on 
 defining two hermitian forms 
 such as 
 and 
.
Definition 28  The relative entropy of φ and ψ is defined by   <
Ohya’s mutual entropy> 
[16]Next we discuss the mutual entropy in C*−systems. For any 
 and a channel 
, define the compound states by 
 and 
The first compound state generalizes the joint probability in classical systems and it exhibits the correlation between the initial state φ and the final state .
Definition 29  The mutual entropy w.r.t.  and μ is  and the mutual entropy w.r.t. 
 is defined as 
 where 
 The following fundamental inequality is satisfied for almost all physical cases. 
  The main properties of the relative entropy and the mutual entropy are shown in the following theorems.
Theorem 30  - (1) 
- Positivity : . 
- (2) 
- Joint Convexity :  for any . 
- (3) 
- Additivity : . 
- (4) 
- Lower Semicontinuity : If  and , then . Moreover, if there exists a positive number λ satisfying , then . 
- (5) 
- Monotonicity : For a channel  from  to ,  
- (6) 
- Lower Bound :  
 Remark 31  This theorem is a generalization of the theorem 3.
 <Connes-Narnhofer-Thirring Entropy>
Before closing this section, we mention the dynamical entropy introduced by Connes, Narnhofer and Thirring [
50].
The CNT entropy 
 of C*-subalgebra 
 is defined by 
 where the supremum is taken over all finite decompositions 
 of 
φ and 
 is the restriction of 
φ to 
. This entropy is the mutual entropy when a channel is the restriction to subalgebra and the decomposition is orthogonal. There are some relations between the mixing entropy 
 and the CNT entropy [
26].
Theorem 32.  - (1) 
- For any state φ on a unital C*-algebra ,  
- (2) 
- Let  with a certain group G be a W*-dynamical system andf φ be a G-invariant normal state of , then  where  is the fixed points algebra of  w.r.t. α. 
- (3) 
- Let  be the C*-algebra  of all compact operators on a Hilbert space , and G be a group, α be a *-automorphic action of G-invariant density operator. Then  
- (4) 
- There exists a model such that  
   10. Entropy Exchange and Coherent Information
First we define the entropy exchange [
43,
70,
71,
72,
73]
. If a quantum operation 
 is represented as 
 then the 
entropy exchange of the quantum operation 
 with input state 
ρ is defined to be 
 where the matrix 
W has elements 
 Remark that if 
 holds, then the quantum operation 
 is a channel.
Definition 33  [] The coherent information is defined by   Let 
ρ be a quantum state and 
 and 
 trace-preserving quantum operations. Then 
 which has similar property of quantum mutual entropy.
Another entropy is defined by this coherent information with the von Neuman entropy 
 such that 
 We call this mutua type information the coherent mutual entropy here.
However these coherent information can not be considered as a candidate of the mutual entropy due to a theorem of the next section.
  11. Comparison of various quantum mutual type entropies
There exist several different information a la mutual entropy. We compare these mutual type entropies [
60,
74].
Let 
 be a CONS in the input Hilbert space 
, a quantum channel 
 is given by 
 where 
 is a one-dimensional projection satisfying 
 Then one has the following theorem:
Theorem 34  When  is a projection valued measure and dim(ran for arbitary state ρ we have (1) , (2)  (3) 
 Proof.  For any density operator 
 and the channel 
 given above, one has 
tr
 so that one has 
 Then the entropy exchange of 
ρ with respect to the quantum channel 
 is 
 Since 
 the coherent information of 
ρ with respect to the quantum channel 
 is given by 
 for any 
 The Lindblad-Nielsen entropy is defined by 
 for any 
. The quantum mutual entropy becomes 
 where the sup is taken over all Schatten decompositions 
 so we obtain 
where 
 and 
 This means that 
 takes various values depending on the input state 
 for instance, ■ 
 We further can prove that the coherent information vanishes for a general class of channels.
Theorem 35  Let in the input Hilbert space be given a CONS  and in the output Hilbert space a sequence of the density operators . Consider a channel  given by  where ρ is any state in the input Hilbert space. (One can check that it is a trace preserving CP map). Then the coherent information vanishes:  for any state   Remark 36  The channel of the form  can be considered as the classical-quantum channel iff the classical probability distribution  is a priori given.
 For the attenuation channel 
, one can obtain the following theorems [
74,
75]:
Theorem 37  For any state  and the attenuation channel  with , one has - 1. 
-  (Ohya mutual entropy), 
- 2. 
-  (coherent entropy), 
- 3. 
-  (Lindblad-Nielsen entropy). 
  Theorem 38  For the attenuation channel  and the input state, we have - 1. 
-  (Ohya mutual entropy), 
- 2. 
-  (coherent entropy), 
- 3. 
-  (Lindblad-Nielsen entropy). 
  The above theorem shows that the coherent entropy  takes a minus value for  and the Lindblad-Nielsen entropy  is grater than the von Neumann entropy of the input state ρ for .
From these theorems, Ohya mutual entropy  only satisfies the inequality held in classical systems, so that Ohya mutual entropy can be a most suitable candidate as quantum extension of the classical mutual entropy.
  12. Quantum Capacity and Coding
We discuss the following topics in quantum information; (1) the channel capacity for quantum communication processes by applying the quantum mutual entropy, (2) formulations of quantum analogues of McMillan’s theorem.
As we discussed, it is important to check ability or efficiency of channel. It is the channel capacity which describes mathematically this ability. Here we discuss two types of the channel capacity, namely, the capacity of a quantum channel  and that of a classical (classical-quantum-classical) channel 
  12.1. Capacity of quantum channel
The capacity of a quantum channel is the ability of information transmission of the channel itself, so that it does not depend on how to code a message being treated as a classical object.
As was discussed in Introduction, main theme of quantum information is to study information carried by a quantum state and its change associated with a change of the quantum state due to an effect of a quantum channel describing a certain dynamics, in a generalized sense, of a quantum system. So the essential point of quantum communication through a quantum channel is the change of quantum states by the quantum channel, which should be first considered free from any coding of messages. The message is treated as classical object, so that the information transmission started from messages and their quantum codings is a semi-quantum and is discussed in the next subsection. This subsection treats the pure quantum case, in which the (pure) quantum capacity is discussed as a direct extension of the classical (Shannon’s) capacity.
Before starting mathematical discussion, we explain a bit more about what we mean "pure quantum" for transmission capacity. We have to start from any quantum state and a channel, then compute the supremum of the mutual entropy to define the "pure" quantum capacity. One often confuse in this point, for example, one starts from the coding of a message and compute the supremum of the mutual entropy and he says that the supremum is the capacity of a quantum channel, which is not purely quantum but a classical capacity through a quantum channel.
Even when his coding is a quantum coding and he sends the coded message to a receiver through a quantum channel, if he starts from a classical state, i.e., a probability distribution of messages, then his capacity is not the capacity of the quantum channel itself. In his case, usual Shannon’s theory is applied because he can easily compute the conditional distribution by a usual (classical) way. His supremum is the capacity of a classical-quantum-classical channel, and it is in the second category discussed in the next subsection.
The capacity of a quantum channel 
 is defined as follows: Let 
 be the set of all states prepared for expression of information. Then the 
quantum capacity of the channel 
 with respect to 
 is defined by 
 Here 
 is the mutual entropy given in 
Section 7 with 
 When 
 , 
 is denoted by 
 for simplicity. The capacity 
 is the largest information possibly sent through the channel 
We have
Remark 40  We also considered the pseudo-quantum capacity  defined [
76]
 with the pseudo-mutual entropy  where the supremum is taken over all finite decompositions instead of all orthogonal pure decompositions: However the pseudo-mutual entropy is not well-matched to the conditions explained, and it is difficult to be computed numerically. It is easy to see that   It is worthy of noting that in order to discuss the details of transmission process for a sequence of n messages we have to consider a channel on the n-tuple space and the average mutual entropy (transmission rate) per a message.
  12.2. Capacity of classical-quantum-classical channel
The capacity of C-Q-C channel  is the capacity of the information transmission process starting from the coding of messages, therefore it can be considered as the capacity including a coding (and a decoding). The channel  sends a classical state to a quantum one, and the channel  does a quantum state to a classical one. Note that  and  can be considered as the dual maps of  ( and  respectively.
The capacity of the C-Q-C channel 
 is 
 where 
 is the set of all probability distributions prepared for input (a-priori) states (distributions or probability measures, so that classical states). Moreover the capacity for coding free is found by taking the supremum of the mutual entropy over all probability distributions and all codings 
:
 The last capacity is for both coding and decoding free and it is given by 
 These capacities 
 do not measure the ability of the quantum channel 
 itself, but measure the ability of 
 through the coding and decoding.
The above three capacities 
 satisfy the following inequalities 
 Here 
 is the Shannon entropy: 
 for the initial probability distribution 
 of the message.
  12.3. Bound of mutual entropy and capacity
Here we discuss the bound of mutual entropy and capacity. The discussion of this subsection is based on the papers [
30,
31,
41,
42,
77].
To each input symbol 
 there corresponds a state 
 of the quantum communication system, 
 functions as the codeword of 
. The coded state is a convex combination 
 whose coefficients are the corresponding probabilities, 
 is the probability that the letter 
 should be transmitted over the channel. To each output symbol 
 there corresponds a non-negative observable, that is a selfadjoint operator 
 on the output Hilbert space 
, such that 
 (
 is called POVM). In terms of the quantum states the transition probabilities are tr
 and the probability that 
 was sent and 
 is read is 
 On the basis of these joint probability distribution the classical mutual information is given. 
 where 
 tr
 The next theorem provides a fundamental bound for the mutual information in terms of the quantum von Neumann entropy, which was proved by Holevo [
21] in 1973. Ohya introduced in 1983 the quantum mutual entropy by means of the relative entropy as discussed above.
Theorem 41  With the above notation holds.  Holevo’s upper bound can now be expressed by 
 For general quantum case, we have the following inequality according to 
the theorem 9.Theorem 42  When the Schatten decomposition i.e., one dimensional spectral decomposition)  is unique,  for any channel .  Go back to general discussion, an input state 
ρ is the probability distribution 
 of messages and its Schatten decomposition is unique as 
 with delta measures 
, so the mutual entropy is written by 
 If the coding 
 is a quantum coding, then 
 is expressed by a quantum state. Let denote the coded quantum state by 
 as above and put 
 Then the above mutual entropy in a 
 channel 
 is written as 
 This is the expression of the mutual entropy of the whole information transmission process starting from a coding of classical messages.
Remark that if 
 is finite, then (
18) becomes 
 Further, if 
ρ is a probability measure having a density function 
; that is, 
  where 
A is an interval in 
 , and each 
λ corresponds to a quantum coded state 
 then 
 and 
 One can prove that this is less than 
This upper bound is a special one of the following inequality 
 which comes from the monotonicity of the relative entropy and gives the proof of Theorem 42 above
.We can use the 
Bogoliubov inequality  where 
A and 
B are two positive Hermitian operators and use the monotonicity of the mutual entropy, one has the following bound of the mutual entropy 
 [
41].
Theorem 43  For a probability distribution  and a quantum coded state , , , one has the following inequality for any quantum channel decomposed as  such that ,   In the case that the channel 
 is identical, 
 the above inequality reduces to the bound of Theorem 41:
 where 
Note that  and  are the quantum mutual entropy  for special channels  as discussed above and that the lower bound is equal to the classical mutual entropy, which depends on the POVM 
Using the above upper and lower bounds of the mutual entropy, we can compute these bounds of the capacity in many different cases.
  13. Computation of Capacity
Shannon’s communication theory is largely of asymptotic character, the message length 
N is supposed to be very large. So we consider the 
N-fold tensor product of the input and output Hilbert spaces 
 and 
, 
 Note that 
 A channel 
 sends density operators acting on 
 into those acting on 
. In particular, we take a 
memoryless channel which is the tensor product of the same 
single site channels: 
 (
N-fold). In this setting we compute the quantum capacity and the classical-quantum-classical capacity, denoted by 
 and 
 below.
A 
pseudo-quantum code (of order 
N) is a probability distribution on 
 with finite support in the set of product states. So 
 is 
a pseudo-quantum code if  is a probability vector and  are product states of. This code is nothing but a quantum code for a classical input (so a classical-quantum channel) such that 
 , as discussed in the previous chapter. Each quantum state 
 is sent over the quantum mechanical media (e.g., optical fiber) and yields the output quantum states 
. The performance of coding and transmission is measured by the quantum mutual entropy 
 We regard 
φ as the quantum state of the 
n-component quantum system during the information transmission. Taking the supremum over certain classes of pseudo-quantum codes, we obtain various capacities of the channel. The supremum is over product states when we consider memoryless channels, so the capacity is 
Next we consider a subclass of pseudo-quantum codes. A 
quantum code is defined by the additional requirement that is a set of pairwise orthogonal pure states. This code is pure quantum, namely, we start a quantum state 
φ and take 
orthogonal extremal decompositions , whose decomposition is not unique. Here the coding is how to take such an orthogonal extremal decomposition. The quantum mutual entropy is 
 where the supremum is over all 
orthogonal extremal decompositions  as defined in 
Section 7. Then we arrive at the capacity 
It follows from the definition that 
 holds for every channel.
Proposition 44  For a memoryless channel the sequences  and  are subadditive.
 Therefore the following limits exist and they coincide with the infimum. 
 (For multiple channels with some memory effect, one may take the limsup in (
20) to get a good concept of capacity per single use.)
Example 45  Let  be a channel on the  density matrices such that  Consider the input density matrix  For  the orthogonal extremal decomposition is unique, in fact  and we have  However, . Since  we conclude that     13.1. Divergence center
In order to estimate the quantum mutual entropy , we introduce the concept of divergence center. Let  be a family of states and a constant .
Definition 46  We say that the state ω is a divergence center for a family of states  with radius  if   In the following discussion about the geometry of relative entropy (or divergence as it is called in information theory) the ideas of the divergence center can be recognized very well.
Lemma 47  Let  be a quantum code for the channel  and ω a divergence center with radius  for . Then   Definition 48  Let  be a family of states. We say that the state ω is an exact divergence center with radius r if  and ω is a minimizer for the right hand side.  When 
r is finite, then there exists a minimizer, because 
 is lower semicontinuous with compact level sets: (cf. Proposition 5.27 in [
17].)
Lemma 49  Let  and ω be states of B  such that the Hilbert space  is finite dimensional and set  . If ,  are finite and  then   Lemma 50  Let  be a finite set of states of  such that the Hilbert space  is finite dimensional. Then the exact divergence center is unique and it is in the convex hull on the states .
 Theorem 51  Let  be a channel with finite dimensional  . Then the capacity  is the divergence radius of the range of .
   13.2. Comparison of capacities
Up to now our discussion has concerned the capacities of coding and transmission, which are bounds for the performance of quantum coding and quantum transmission. After a measurement is performed, the quantum channel becomes classical and Shannon’s theory is applied. The 
total capacity (or 
classical-quantum-classical capacity) of a quantum channel 
 is 
 where the supremum is taken over both all pseudo-quantum codes 
 and all measurements 
. Due to the monotonicity of the mutual entropy 
 and similarly 
 holds for the capacities per single use.
Example 52  Any  density operators has the following standard representation  where  are the Pauli matrices and  with . For a positive semi-definite  matrix A the application  gives a channel when . Let us compute the capacities of . Since a unitary conjugation does not change capacity obviously, we may assume that A is diagonal with eigenvalues . The range of  is visualized as an ellipsoid with (Euclidean) diameter . It is not difficult to see that the tracial state τ is the exact divergence center of the segment connected the states  and hence τ must be the divergence center of the whole range. The divergence radius is  This gives the capacity  according to Theorem 51. The inequality (19) tells us that the capacity  cannot exceed this value. On the other hand,  and we have .  The relations among 
, 
 and 
 form an important problem and are worthy of study. For a noiseless channel 
 was obtained in [
78], where 
n is the dimension of the output Hilbert space (actually identical to the input one). Since the tracial state is the exact divergence center of all density matrix, we have 
 and also 
.
We expect that  for "truely quantum mechanical channels" but   must hold for a large class of memoryless channels.
One can obtain the following results for the attenuation channel which is discussed in the previous chapter.
Lemma 53  Let  be the attenuation channel. Then  when the supremum is over all pseudo-quantum codes  applying n coherent states.  The next theorem follows directly from the previous lemma.
Theorem 54  The capacity  of the attenuation channel is infinite.
 Since the argument of the proof of the above Lemma works for any quasi-free channel, we can conclude  also in that more general case. Another remark concerns the classical capacity . Since the states  used in the proof of Lemma commute in the limit , the classical capacity  is infinite as well.  follows also from the proof of the next theorem.
Theorem 55  The capacity  of the attenuation channel is infinite.
 Let us make some comments on the previous results. The theorems mean that arbitrarily large amount of information can go through the attenuation channel, however the theorems do not say anything about the price for it. The expectation value of the number of particles needed in the pseudo-quantum code of Lemma 53 tends to infinity. Indeed, 
 which increases rapidly with 
n. (Above 
N denoted the number operator.) Hence the good question is to ask the capacity of the attenuation channel when some energy constrain is posed:
 To be more precise, we have posed a bound on the average energy, different constrain is also possible. Since 
 for the dual operator Λ of the channel 
 and the number operator 
N, we have 
 The solution of this problem is the same as 
 and the well-known maximizer of this problem is a so-called Gibbs state. Therefore, we have 
 This value can be realized as a classical capacity if the number states can be output states of the attenuation channel.
  13.3. Numerical computation of quantum capacity
Let us consider the quantum capacity of the attenuation channel for the set of density operators consisted of two pure states with the energy constrain [
31,
79].
Let 
 and 
 be two subsets of 
 given by 
 The quantum capacities of the attenuation channel 
 with respect to the above two subsets are computed under an energy constraint 
 for any 
: 
 Since the Schatten decomposition is unique for the above two state subsets, by using the notations 
we obtain the following result [
31].
Theorem 56  (1) For , the quantum mutual entropy  is calculated rigorously by  (2) For , the quantum mutual entropy  is computed precisely by  (3) For any  we have inequality of two quantum capacities:  Note that  and  represent the state subspaces generated by means of modulations of PSK (Phase-Shift-Keying) and OOK (On-Off-Keying) [75].    14. Quantum Dynamical Entropy
Classical dynamical entropy is an important tool to analyse the efficiency of information transmission in communication processes. Quantum dynamical entropy was first studied by Connes, Størmer [
48] and Emch [
49]. Since then, there have been many attempts to formulate or compute the dynamical entropy for some models [
52,
53,
54,
55,
56,
80]. Here we review four formulations due to (a) Connes, Narnhofer and Thirring (CNT) [
50], (b) Muraki and Ohya (Complexity) [
27,
81], (c) Accardi, Ohya and Watanabe [
58], (d) Alicki and Fannes (AF) [
51]. We consider mutual relations among these formulations [
80].
A dynamical entropy (Kossakowski, Ohya and Watanabe) [
59] for not only a shift but also a completely positive (CP) map is defined by generalizing the entropy defined through quantum Markov chain and AF entropy defined by a finite operational partition.
  14.1. Formulation by CNT
Let  be a unital -algebra, θ be an automorphism of , and φ be a stationary state over  with respect to θ; . Let  be a finite dimensional -subalgebra of .
The CNT entropy [
50] for a subalgebra 
 is given by 
 where 
 is the restriction of the state 
φ to 
 and 
 is the relative entropy for 
-algebra [
7,
8,
10].
The CNT dynamical entropy with respect to 
θ and 
 is given by 
 and the dynamical entropy for 
θ is defined by 
  14.2. Formulation by MO
We define three complexities as follows:
 Based on the above complexities, we explain the quantum dynamical complexity (QDC) [
14].
Let 
θ (resp. 
) be a stationary automorphism of 
 (resp. 
); 
 and Λ (the dual map of channel 
) be a covariant CP map (
i.e., 
) from 
 to 
. 
 (resp. 
) is a finite subalgebra of 
 (resp. 
). Moreover, let 
 (resp. 
) be a CP unital map from 
 (resp. 
) to 
 (resp. 
) and 
 and 
 are given by 
 Two compound states for 
 and 
, with respect to 
, are defined as 
 Using the above compound states, the three transmitted complexities [
81] are defined by 
 When 
, 
, 
, 
, where 
α is a unital CP map from 
 to 
, the mean transmitted complexities are 
 and the same for 
 and 
. These quantities have properties similar to those of the CNT entropy [
27,
81].
  14.3. Formulation by AOW
A construction of dynamical entropy is due to the quantum Markov chain [
58].
Let 
 be a von Neumann algebra acting on a Hilbert space 
 and let 
φ be a state on 
 and 
 (
 matrix algebra). Take the transition expectation 
 of Accardi [
36,
82] such that 
 where 
 and 
 is a finite partition of unity 
 . Quantum Markov chain is defined by 
 such that 
 where 
, 
Aut
, and 
 is an embeding of 
 into 
 such that 
.
Suppose that for 
φ there exists a unique density operator 
ρ such that 
 tr 
 for any 
. Let us define a state 
 on 
 expressed as 
 The density operator 
 for 
 is given by 
 Put 
 The dynamical entropy through QMC is defined by 
 If 
 satisfies the Markov property, then the above equality is written by 
 The dynamical entropy through QMC with respect to 
θ and a von Neumann subalgebra 
 of 
 is given by 
  14.4. Formulation by AF
Let 
 be a 
-algebra, 
θ be an automorphism on 
 and 
φ be a stationary state with respect to 
θ and 
 be a unital 
-subalgebra of 
. A set 
 of elements of 
 is called a finite operational partition of unity of size 
k if 
γ satisfies the following condition:
 The operation ∘ is defined by 
 for any partitions 
 and 
. For any partition 
γ of size 
k, a 
 density matrix 
 is given by 
 Then the dynamical entropy 
 with respect to the partition 
γ and shift 
θ is defined by von Neumann entropy 
; 
 The dynamical entropy 
 is given by taking the supremum over operational partition of unity in 
 as 
  14.5. Relation between CNT and MO
In this section we discuss relations among the above four formulations. The 
-mixing entropy in GQS introduced in [
16] is 
 where 
 is given by 
 and 
 is the set of all finite partitions of 
.
The following theorem [
27,
81] shows the relation between the formulation by CNT and that by complexity.
Theorem 57  Under the above settings, we have the following relations: - (1) 
-  , 
- (2) 
-  , 
- (3) 
- , for any density operator ρ, and  
  Since there exists a model showing that ,  distinguishes states more sharply than , where .
Furthermore, we have the following results [
83]. 
- (1)
- When  -  are abelian  - -algebras and  -  is an embedding map, then  -  are satisfied for any finite partitions  -  on the probability space (Ω= spec - ,  - ,  μ- ). 
- (2)
- When Λ is the restriction of  -  to a subalgebra  -  of  - ;  - ,  
We show the relation between the formulation by complexity and that by QMC. Under the same settings in 
Section 3, we define a map 
 from 
, the set of all density operators in 
, to 
 by 
 for any density operator 
. Let us take a map 
 from 
 to 
 such that 
 Then a map 
 from 
 to 
 is given by 
 so that 
 and 
 From the above Theorem, we have 
. Hence 
  14.6. Formulation by KOW
Let 
 (resp. 
 be the set of all bounded linear operators on separable Hilbert space 
 (resp. 
 We denote the set of all density operators on 
 (resp. 
 by 
 (resp. 
 Let 
 be a normal, unital CP linear map, that is, Γ satisfies 
  for any increasing net 
 converging to 
 and 
 hold for any 
 and any 
. For a normal state 
ω on 
 there exists a density operator 
 associated to 
ω (
i.e., 
). Then a map 
 defined as 
 is a transition expectation in the sense of [
84] (
i.e., 
 is a linear unital CP map from 
 to 
), whose dual is a map 
 given by 
 The dual map 
 is a lifting in the sense of [
84]; that is, it is a continuous map from 
 to 
.
For a normal, unital CP map 
, 
 is a normal, unital CP map, where 
 is the identity map on 
. Then one defines the transition expectation 
 and the lifting 
 The above 
 has been called a quantum channel [
13] from 
 to 
 in which 
ρ is regarded as an input signal state and 
 is as a noise state.
The equality 
 for all 
 and any 
 defines 
- (1) 
- a lifting 
 and 
- (2) 
- marginal states 
 Here, the state 
 is a compound state for 
 and 
 in the sense of [
13]. Note that generally 
 is not equal to 
Definition 58  The quantum dynamical entropy with respect to Γ and ω is defined by  where  is von Neumann entropy [6]; that is, . The dynamical entropy with respect to Λ and ρ is defined as     14.7. Generalized AF entropy and generalized AOW entropy
In this section, we generalize both the AOW entropy and the AF entropy. Then we compare the generalized AF entropy with the generalized AOW entropy.
Let θ be an automorphism of , ρ be a density operator on  and  be the transition expectation on  with .
One introduces a transition expectation 
 from 
 to 
 such as 
 The quantum Markov state 
 on 
 is defined through this transition expectation 
 by 
 for all 
 and any 
Let consider another transition expectation 
 such that 
 One can define the quantum Markov state 
 in terms of 
  for all 
 and any 
 Then we have the following theorem.
Theorem 59   Let 
 be a subalgebra of 
. Taking the restriction of a transition expectation 
 to 
 i.e., 
 is the transition expectation from 
 to 
 The QMC (quantum Markov chain) defines the state 
 on 
 through Equation (
40) ), which is 
 The subalgebra 
 of 
 can be constructed as follows: Let 
 be projection operators on mutually orthogonal subspaces of 
 such that 
 Putting 
, the subalgebra 
 is generated by 
 One observes that in the case of 
  and one has for any 
  from which the following theorem is proved (c.f., see [
17]).
Taking into account the construction of subalgebra  of  one can construct a transition expectation in the case that  is a finite subalgebra of 
Let 
 be the 
 matrix algebra 
 (
) in 
 and 
 with normalized vectors 
. Let 
 be a finite operational partition of unity, that is, 
 then a transition expectation 
 is defined by 
 Remark that the above type complete positive map 
 is also discussed in [
85].
Let 
 be a subalgebra of 
 consisting of diagonal elements of 
 Since an element of 
 has the form 
 one can see that the restriction 
 of 
 to 
 is defined as 
 When 
 is a normal unital CP map, the transition expectations 
 and 
 are defined by 
 Then one obtains the quantum Markov states 
 and 
  and 
 where we put 
    The above 
 become the special cases of 
 defined by taking Γ and 
ω in Equation (
33). Therefore the dynamical entropy Equation (
36) becomes 
  The dynamical entropies of Λ with respect to a finite dimensional subalgebra 
 and the transition expectations 
 and 
 are given by 
  We call (
56) and (57) a generalized AF entropy and a generalized dynamical entropy by QMC, respectively. When 
 is PVM (projection valued measure) and Λ is an automorphism 
θ, 
 is equal to the AOWdynamical entropy by QMC [
58]. When 
 is POV (positive operater valued measure) and 
, 
 is equal to the AF entropy [
51].
From theorem 60, one obtains an inequality
That is, the generalized dynamical entropy by QMC is greater than the generalized AF entropy. Moreover the dynamical entropy 
 is rather difficult to compute because there exist off-diagonal parts in (
48). One can easily compute the dynamical entropy 
.
Here, we note that the dynamical entropy defined in terms of 
 on 
 is related to that of flows by Emch [
49], which was defined in terms of the conditional expectation, provided 
 is a subalgebra of 
  15. Conclusion
As is mentioned above, we reviewed the mathematical aspects of quantum entropy and discussed several applications to quantum communication and statistical physics. All of them were studied by the present authors. Other topics for quantum information are recently developed in various directions, such as quantum algorithm, quantum teleportation, quantum cryptography, 
etc., which are discussed in [
60].