Chapter 6 - Noise in Quantum Systems

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Introduction

Noise is the greatest obstacle to building a scalable and reliable quantum computing device. Furthermore, all realistic quantum systems are noisy. Therefore, the main goal of experimentalists trying to build quantum computing devices is to eliminate as much noise as possible. In this chapter, the objective will be to understand how to describe noise.

In Chapter 3, the Schrodinger equation was discussed as a way to describe quantum systems' evolution. The process described by Schrodinger's Equation is the evolution of a system which has been isolated from everything else (a closed system). However, as just stated, realistic systems are noisy often because of unwanted interactions with the environment. There are other noises---such as a gating operation that necessarily has a finite precision. Such noise can also be described by the representations of open quantum system evolution that are provided in this chapter. This chapter is about noise in general.

SMR Representation or Operator-Sum Representation

The operator-sum representation is a method for representing open system evolution. It now goes by other names, such as Kraus representation or Kraus decomposition. However, it originated with Sudarshan, Mathews, and Rau [11] in 1961 (SMR) and was later taken up by Kraus [12] and others. Kraus's name is now attached to it due to a set of lecture notes published in the early 1970's. In this section, it will likely be clear that the description in all its generality was very well (and simply) described by SMR [11] and that is the line of argument which will be followed.


Let us consider a mapping from one density operator to another with no other restrictions. This can be written as a linear map,


(6.1)

or more explicitly,


(6.2)

One way to think of this is a linear mapping from one vector () to another () by a matrix (). ( can be viewed as a vector. Simply rearrange the elements of the matrix into a column vector. is then a matrix.) It is apparent that this is a very general mapping; yet one might think a more general one could be constructed by adding a constant term. This would be, in fact, not more general as it can be absorbed into the definition of . We recall that the density matrix is required to be Hermitian, positive semi-definite (has no negative eigenvalues), and having a trace of one. Thus the following are true:


(6.3)

(6.4)

and


(6.5)

The first condition ensures real eigenvalues, while the second and third ensure a valid probability interpretation of the density matrix. One can show that, given the properties Eqs. (6.3), (6.4), and (6.5), the mapping (which can be considered a matrix) has the following properties:


(6.6)

(6.7)

and


(6.8)

We could also introduce a new matrix, , which is related to by relabeling,


(6.9)

This has the following properties:


(6.10)

(6.11)

and


(6.12)

Now the important point to note is that can be considered a Hermitian matrix and thus diagonalizable. Letting be its eigenvalues and the corresponding eigenvectors, we see that the mapping (6.2) can be written as


(6.13)

or in a short-hand notation (defining ),

If all of the are positive, then a factor of can be absorbed into the . In other words, if we define , then the map can be written as


(6.14)

This is what is sometimes called the Operator-Sum representation or Kraus decomposition, originally given in SMR [11].

Modelling Open System Evolution

Noise in a quantum system can arise in two different ways. The first is through imperfect controls; a unitary transformation is not implemented exactly as one would like due to experimental limitations. The second is through an unwanted interaction with another system, usually called a bath or environment. Both of these can be modeled using completely positive maps if the assumption is made that the system and bath are initially uncorrelated. The definition, concept, and assumption of complete positivity will be discussed in a later section.

Initially Uncorrelated System and Bath

The dynamics of an open quantum system coupled to a bath is formally obtained from the evolution under the combined system-bath Hamiltonian,


(6.15)

where is the Hamiltonian for the system alone, is the Hamiltonian for the bath alone, and the and are operators on the system and the bath respectively. This last term couples the system to the bath so that the system is no longer considered a closed quantum system; the bath and system do not evolve independently. If we assume that the system and the bath are decoupled at the beginning of the experiment, then we may write at

We then act with a unitary operator on the closed system (our system plus the bath) and trace (or average) over the bath using a partial trace to get


(6.16)

where is the initial density matrix of the (open) system and is the initial density matrix of the bath. If we take the bath to be in a state , then we can write Eq. (6.16) as


(6.17)

Now we may define a new index, , as well as


(6.18)

In an analogous way, we define so that the operator-sum representation is given by


(6.19)

Note the relation to Eq.(6.14). It should be noted that in some places the definition (6.18) will be used without the factor ; this is, however, of little consequence. The equation (6.19) is the operator sum decomposition or the Kraus decomposition (or, as it was initially called, the eigenvalue decomposition) that was outlined in the last section. The s are the Kraus operators.

We should note that, in order for the density matrix to keep its trace equal to one (in other words for the map to be trace-preserving),

It can be shown that this agrees with the most general quantum evolution consistent with the condition of complete positivity, the so-called operator sum representation (OSR) SMR [11], Kraus [12], Schumacher [13]:


(6.20)

Fixed-Basis Operations

Another expression for the time-dependent density operator is the following:


(6.21)

In the last section, we defined the operators where and the initial bath density matrix written as Lidar, et al [14].. They satisfy the normalization condition, . The matrix is a time-dependent Hermitian coefficient matrix defined by a transformation of the Kraus operators to a fixed operator basis : .

We can express our in terms of a fixed basis of operators. The are functions of time (since is) and are thus not fixed operators. We can expand these 's in terms of a complete fixed chosen basis :


(6.22)

so that the time-dependence is in the 's. Now we would like to see how Eq. (6.21) transforms under a change of basis, that is, when the 's are different. We will do this to first order in time. We should note that the very general form of the Hamiltonian (Eq. (6.15)) tells us that each term of the Hamiltonian could be made Hermitian depending on the order and grouping in the terms of the sum. We will not see at any point where this choice will make a significant difference in our analysis since most of the bases we use are arbitrary. Thus, to first order (and without the factor )


(6.23)

Since the are Hermitian, they can be expanded in a complete basis for the algebra of the unitary matrices, plus the identity if the system is -dimensional. Therefore, under a unitary transformation of the operators or for that matter , the operators transform as


(6.24)

where the set are a complete set of basis elements for the algebra of -dimensional unitary matrices, with ranging from zero to with . This transformation can be written as


(6.25)

where


(6.26)

defines an action by the adjoint representation of the group. Alternatively, we can redefine the such that


(6.27)

so that our fixed basis stays fixed and we look at the transformation as an active, rather than a passive one. (That is, we transform the object instead of the basis.)

It is interesting to note that upon comparison of Eqs. (6.21) and (6.22) we can identify s and s as Lidar, et al [14].


(6.28)

Unitary Degree of Freedom in the OSR

The operator sum decomposition is not unique. There is a freedom in choosing the operators in Eq. (6.14) (or (6.19). It turns out, and this will be shown, that this is equivalent to having the freedom to choose a basis for the bath.

Unitary Freedom

Let us consider an operator-sum decomposition, of the form given in Eq. (6.19) which represents some quantum process whereby one quantum system, a bath, interacts with another and is then traced out. Now consider another given by a linear combination of the operators ,


(6.29)

Now, let us construct the operator-sum representation for the set of operators . This is


(6.30)

Rewriting this as


(6.31)

where , (i.e. the are elements of a matrix ) we see that if , then


(6.32)

Therefore the two different sets of operators and give rise to the same open-system evolution of if they are related by a unitary transformation of the form of . Note that if the two sets do not have the same number of elements, then we may either append zeroes (as is done in Nielsen and Chuang's book [2]) or let be a ''right unitary matrix'', which means the matrix , but the matrix need not be square or have .

Physical Interpretation of the Unitary Freedom

It is fairly easy to show that this unitary degree of freedom is associated with a change of the bath basis. Let us first recall


(6.33)

So that when we write we interpret this as . However, this clearly makes no difference in the result since we are tracing over the bath. One could just as well include this unitary transformation which changes the basis of the bath in .

Examples

In this section several examples are given which are quite important to quantum error correction and quantum computing. These are all examples of actions on single qubit density operators which, not only provide very relevant examples, but are also quite simple.

Example 1: Bit-flip

Bit-flip errors have a direct analogy in classical computation. If a is input, but, due to noise a is output (or vice versa) then a bit-flip error has occurred. If a quantum bit-flip error occurs with some probability , we may express this as


(6.34)

where is the The Pauli Matrix, and can be expressed as in Eq.(3.33). This has a clear interpretation. There is a probability that there is a bit flip, and there is a probability that nothing happens to the density operator. The operators of the operator-sum representation of this map can be taken to be and . This is a very important type of error and therefore will be discussed in connection with quantum error prevention methods.

Example 2: Phase-flip

Phase-flip errors do not have a direct analogue in classical computation. In this case a will acquire a (-1) sign change due to some noise, but a is unaffected. If a quantum phase-flip error occurs with some probability , we may express the phase flip error as


(6.35)

where is the The Pauli Matrix, and can be expressed as in Eq.(3.33). Here there is a probability that a phase-flip occurs, and there is a probability that nothing happens to the density operator. The operators of the operator-sum representation of this map can be taken to be and . This is another very important type of error.

Example 3: Bit-flip and Phase-flip

Suppose that both errors occur. If both errors occur with some probability , we may express the error as


(6.36)

where , is the The Pauli Matrix. As before, can be expressed as in Eq.(3.33). Here there is a probability that a occurs, and there is a probability that nothing happens to the density operator. The operators of the operator-sum representation of this map can be taken to be and . Note that .


It is important to note that the The Pauli Matrices form a basis for all of the possible errors on a qubit as discussed in Section 3.5.4.

Example 4: Depolarizing Error

A depolarizing error is an error which is symmetric in the three possible errors. For example, suppose that each of the three possible types of errors on a qubit all occur with equal probability. Let this probability be . The depolarizing operation is then


(6.37)

Using Eq.(3.37) and Eq.(3.33), this can also be written as


(6.38)

Thus the depolarizing error has quite interesting and simple properties since it can be seen as a uniform shrinking of the polarization vector .

Notes

Errors are the obstacle to building a quantum computing device. These errors are caused by noise in quantum systems. Noise in quantum systems is also known as decoherence, although the original meaning of decoherence referred (roughly) to the loss of off-diagonal terms of the density matrix. (An analogy of such loss is the error example above.) When a set of entangled states experiences decoherence, entanglement is lost. Since entanglement is believed to be the source of advantages of quantum computing and a great deal of other quantum information processing, this is a serious concern.

In the following chapters, several methods designed to reduce or eliminate noise in quantum systems will be discussed.


Continue to Chapter 7 - Quantum Error Correcting Codes