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Chapter 9 Density Matrices

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Chapter 9 Density Matrices Chapter 9 Density Matrices In this chapter we want to introduce density matrices, also called density operators, which conceptually take the role of the state vectors discussed so far, as they encode all the (accessible) information about a quantum mechanical system. It turns out that the \pure" states, described by state vectors1 j i on Hilbert space, are idealized descriptions that cannot characterize statistical (incoherent) mixtures, which often occur in the ex- periment, i.e. in Nature. These objects are very important for the theory of quantum information and quantum communication. More detailed information about the density matrix formalism can be found in [17]. 9.1 General Properties of Density Matrices Consider an observable A in the \pure" state j i with the expectation value given by h A i = h j A j i ; (9.1) then the following definition is obvious: Definition 9.1 The density matrix ρ for the pure state j i is given by ρ := j i h j This density matrix has the following properties: I) ρ2 = ρ projector (9.2) II) ρy = ρ hermiticity (9.3) III) Tr ρ = 1 normalization (9.4) IV) ρ ≥ 0 positivity (9.5) 1Remark for experts: It is possible to find a vector representation for every given quantum mechanical state, even those represented by a density matrix. This can be done via the so-called GNS (Gelfand- Neumark-Segal) construction. This vector representation need not, however, be of any practical form and the concept of the density matrix is therefore inevitable. 159 160 CHAPTER 9. DENSITY MATRICES The first two properties follow immediately from Definition 9.1 and property III) can be verified using the definition of the trace operation for an arbitrary operator D : Definition 9.2 The trace of an operator D is given by Tr D := P h n j D j n i n where f j n i g is an arbitrary CONS. Lets e.g. take the operator D = j i h φ j and calculate its trace X X Tr D = h n j i h φ j n i = h φ j n i h n j i = h φ j i : (9.6) n n | {z1 } Property IV) means that the eigenvalues of ρ are greater or equal to zero, which can also be expressed as h ' j ρ j ' i = h ' j i h j ' i = j h ' j i j2 ≥ 0 ; (9.7) which is an important property because probabilities are always greater or equal to zero. What we still have to ensure is that the expectation value of an observable in the state j i can be reproduced, which we will formulate in the following theorem: Theorem 9.1 The expectation value of an observable A in a state, represented by a density matrix ρ , is given by h A iρ = Tr (ρ A) Proof: X Tr (ρ A) = Tr (j i h j A) = h n j i h j A j n i = n X = h j A j n i h n j i = h j A j i = h A i : q.e.d. (9.8) n | {z1 } 9.2 Pure and Mixed States Now we can introduce a broader class of states represented by density matrices, the so- called mixed states in contrast to the states we have considered until now, the so-called pure states. 9.2. PURE AND MIXED STATES 161 9.2.1 Pure States Let's begin with the pure states. Consider an ensemble of given objects in the states f j i i g. If all the objects are in the same state, the ensemble is represented by a pure state. To make probabilistic statements the whole ensemble of identically prepared sys- tems must be considered. Let the system be, e.g., in the state j i which we can expand with respect to the eigenstates of an (hermitian) operator A X j i = cn j n i ; where A j n i = an j n i : (9.9) n The expectation value is then given by X X Nn h A i = jc j2 a = a ; (9.10) n n N n n n 2 where jcnj is the probability to measure the eigenvalue an. It corresponds to the frac- tion Nn=N , the incidence the eigenvalue an occurs, where Nn is the number of times this eigenvalue has been measured out of an ensemble of N objects. The state is characterized by a density matrix of the form of Definition 9.1, with the properties I) - IV) (Eqs. (9.2) - (9.5)), where we can combine property I) and III) to conclude Tr ρ2 = 1 : (9.11) 9.2.2 Mixed States Let us next study the situation where not all of the N systems (objects) of the ensemble are P in the same state, i.e. Ni systems are in the state j i i respectively, such that Ni = N. The probability pi to find an individual system of the ensemble described by the state j i i is then given by Ni X p = ; where p = 1 : (9.12) i N i i We can thus write down the mixed state as a convex sum, i.e. a weighted sum with P pi = 1, of pure state density matrices i X pure X ρmix = pi ρi = p i j i i h i j : (9.13) i i The expectation value is again given by Theorem 9.1, i.e. h A i = Tr (ρ A) ; (9.14) ρmix mix 162 CHAPTER 9. DENSITY MATRICES where we can express the expectation value of the mixed state as a convex sum of expec- tation values of its constituent pure states, i.e. X h A i = p h j A j i : (9.15) ρmix i i i i Proof: ! X Tr (ρmix A) = Tr p i j i i h i j A = i X X = p i h n j i i h i j A j n i = n i X X = p i h i j A j n i h n j i i = i n | {z1 } X = p i h i j A j i i : q.e.d. (9.16) i Properties II) - IV) (Eqs. (9.3) - (9.5)) are still valid for mixed states, but property I) does no longer hold 2 X X X 2 ρmix = p i p j j i i h i j j i h j j = p i j i i h i j 6= ρmix ; (9.17) i j | {z } i δij where we, w.l.o.g., assumed that j i i and j j i are orthonormal. We can then calculate the trace of ρ2, which, in contrast to pure states, is no longer equal to 1 but smaller 2 X X X Tr ρmix = h n j p i p j j i i h i j j i h j j n i = n i j X X X = p i p j h i j j i h j j j n i h n j i i = i j n X X 2 = p i p j j h i j j i j = i j X 2 X = p i < p i = 1 : (9.18) i i 2 The last step in this calculation is obvious, since 0 ≤ p i ≤ 1 and therefore p i ≤ p i. We conclude that the trace of ρ2 is a good measure for the mixedness of a density matrix, since it is equal to 1 for pure states and strictly smaller than 1 for mixed states. For a maximally mixed state we have for a given dimension d of the system 2 1 Tr ρmix = d > 0 : (9.19) 9.3. TIME EVOLUTION OF DENSITY MATRICES 163 9.3 Time Evolution of Density Matrices We now want to find the equation of motion for the density matrix. We start from the time dependent Schr¨odinger equation and its hermitian conjugate @ y @ i j i = H j i −! −i h j = h j H: (9.20) ~ @t ~ @t Then we differentiate the density matrix of a mixed state (Eq. (9.13)) with respect to time, we multiply it by i~ and combine this with Eq. (9.20) @ X _ _ i~ ρ = i~ pi ( j i i h i j + j i i h i j ) = @t |{z} |{z} i i i − H j i i h i j H ~ ~ X pure pure = pi ( H ρi − ρi H ) = i = [ H ; ρ ] : (9.21) Theorem 9.2 Density matrices satisfy the von Neumann Equation @ i~ @t ρ = [ H ; ρ ] The von Neumann equation is the quantum mechanical analogue to the classical Liouville equation, recall the substitution (2.85). The time evolution of the density matrix we can also describe by applying an unitary operator, the time shift operator U(t; t0), also called propagator i − H (t − t0) U(t; t0) = e ~ : (9.22) It allows us to relate the density matrix at a later time t to the density matrix at some earlier time t0 y ρ(t) = U(t; t0) ρ(t0) U (t; t0) : (9.23) Furthermore, it helps us to prove, for instance, that the mixedness Tr ρ2 of a density matrix is time independent 2 y y y 2 Tr ρ (t) = Tr (U ρ(t0) U U ρ(t0) U ) = Tr (ρ(t0) ρ(t0) U U) = Tr ρ (t0) ; (9.24) | {z1 } | {z1 } where we used the cyclicity of the trace operation.
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