Abstract
We consider three types of entities for quantum measurements. In order of generality, these types are observables, instruments and measurement models. If and are entities, we define what it means for to be a part of . This relationship is essentially equivalent to being a function of and in this case can be employed to measure . We then use the concept to define the coexistence of entities and study its properties. A crucial role is played by a map which takes an entity of a certain type to one of a lower type. For example, if is an instrument, then is the unique observable measured by . Composite systems are discussed next. These are constructed by taking the tensor product of the Hilbert spaces of the systems being combined. Composites of the three types of measurements and their parts are studied. Reductions in types to their local components are discussed. We also consider sequential products of measurements. Specific examples of Lüders, Kraus and trivial instruments are used to illustrate various concepts. We only consider finite-dimensional systems in this article. Finally, we mention the role of symmetry representations for groups using quantum channels.
1. Introduction
Two important operations on quantum systems are the formations of parts and composites. In a rough sense, these operations are opposites to each other. The parts of a measurement are smaller components of in the sense that they can be simultaneously measured by . A composite system is a combination of two or more other systems. This combination is formed using the tensor product , where and are the Hilbert spaces describing two subsystems. The composite system contains more information than the individual systems because H describes how and interact. We can reduce measurements on H to simpler ones on and but information is lost in the process.
Section 2 presents the basic definitions that are needed in the sequel. Three types of quantum measurements are considered. In order of generality, these types are observables, instruments and measurement models. At the basic level is an observable A which is a measurement whose outcome probabilities are determined by the state of the system. At the next level is an instrument . We think of as an apparatus that can be employed to measure an observable . Although is unique, there are many instruments that can be used to measure an observable. Moreover, gives more information than because, depending on the outcome x, updates the input state to the output state . At the highest level is a measurement model that measures a unique instrument . Again, there are many measurement models that measure an instrument and contains more detailed information. For conciseness, we call these types of measurements entities. We should mention that all the quantum systems in this article are assumed to be finite-dimensional.
Section 3 considers system parts. If and are entities, we define what it means for to be a part of and when this is the case, we write . If and , we say that and are equivalent. We show that implies and that → is a partial order to within equivalence. The relation is the same as being a function of or and in this case, can be employed to measure . We then use this concept to define the coexistence of entities and study its properties. We show that joint measurability is equivalent to coexistence. We then introduce the sequential products of observables and use this concept to illustrate parts of entities.
Section 4 discusses composite systems. These are constructed by taking the tensor product , where are the Hilbert spaces of the systems being combined. Composites of the three types of measurements and parts of these composites are studied. Reductions of types into their local components are discussed. Specific examples of Lüders, Kraus and trivial instruments are employed to illustrate various concepts.
2. Basic Definitions
This section discusses the basic concepts and definitions that are needed in the sequel. Since these ideas are well developed in the literature [1,2,3,4,5], we shall proceed quickly and leave details and motivation to the reader’s discretion. In this article, we shall only consider finite-dimensional complex Hilbert spaces H. Let be the set of linear operators on H. For , we write if for all . We define the set of effects by
where are the zero and identity operators, respectively. Effects correspond to yes–no measurements and when the result of the measurement a is yes, we say that a occurs. The complement of is and occurs if and only if a does not occur. A one-dimensional projection , where is an effect called an atom. We call a partial state if and is a state if . We denote the set of partial states by and the set of states by . If , , we call the probability that a occurs in the state [1,2,3]. For , their sequential product is the effect , where is the unique square root of a [6,7,8]. We interpret as the effect that results from first measuring a and then measuring b. We also call the effect b conditioned on the effect a and write .
Let be a finite set. A (finite) observable with outcome-space is a subset:
satisfying . We denote the set of observables on H by . If is another observable, we define the sequential product [8,9,10] to be the observable with outcome-space given by
We also define the observable B conditioned by A as
where . If , we define the effect-valued measure (or POVM) from to by and we also call an observable [2,3,8]. Moreover, we have the observables:
and:
If and , the probability that A has an outcome in when the system is in state is . Notice that is a probability measure on . We call:
the joint probability of then [8,9,10].
An operation is a completely positive map [1,2,3]. Any operation has a Kraus decomposition:
where with . An operation is a channel if for all . In this case, and we denote the set of channels on H by . Notice that if , then is an operation and if , then is a channel. The simplest example of a channel has the form where U is a unitary operator on H. Letting be the group of unitary operators on H, we have that and for all . In particular, if we have a symmetry group for the system, then , , gives a symmetry representation of G. For a finite set , a (finite) instrument with outcome-space is a set of operations satisfying [1,2,3,11]. Defining for , we see that is an operation-valued measure on H that we also call an instrument. We denote the set of instruments on H by . We say that measures if and:
for every , . There is a unique that measures and we write [1,2,11]. For , we define the product instrument with outcome space by
for every . We also define the conditioned instrument with outcome-space by
We conclude that:
for all and:
for all [8,9,10].
A finite measurement model (MM) is a 5-tuple where H, K are finite-dimensional Hilbert spaces called the base and probe systems, respectively, is an initial probe state, is a channel describing the measurement interaction between the base and probe systems, and is the probe (or meter) observable [1,2,12]. We say that measures the model instrument where is the unique instrument satisfying:
for all , . In (2), is the partial trace over K [2,3]. We also say that measures the model observable .
We thus have three levels of abstraction. At the basic level is an observable A which is a measurement whose outcome probabilities are determined by the state of the system. At the next level is an instrument . We think of as an apparatus that can be employed to measure an observable . Although is unique, there are many instruments that can be used to measure an observable. Moreover, gives more information than because, depending on the outcome x (or event X), updates the input state to the output partial state (or ). At the highest level is a measurement model that measures a unique model instrument and a unique model observable . Again, there are many s that measure any instrument or observable and contains more detailed information on how the measurement is performed.
3. System Parts
We begin by discussing parts of systems at the three levels considered in Section 2. We then show how parts can be used to define coexistence at these levels and even between levels. We also show that coexistence is equivalent to simultaneous measurability.
An element at one of the three levels discussed in Section 2 is called an entity. The three levels are said to be the types 1, 2 and 3, respectively. The concept of an entity being part of another entity was originally introduced in [12,13]. If , we say that A is part of B (and write ) if there exists a surjection such that for all . We then write . It follows that for all and that:
If , we say that is part of (and write ) if there exists a surjection such that for all . We then write and an equation analogous to (3) holds. For s and , we say that is part of (and write ) if . It follows that and we write . We can also define “part of” for entities of different types. An observable is part of (written ) if and A is part of (written ) if which is equivalent to . Finally, we say that is part of (written ) if . Two entities and are equivalent (written ) if and . It is easy to check that ≅ is an equivalence relation and that if and only if for f a bijection. Our first result summarizes properties possessed by “part of”. Some of these properties have been verified in [13], however, we give the full proof for completeness.
Theorem 1.
(a) If are of types 2 or 3 and , then ; (b) and ; (c) If are of the same type and , , then ; (d) The relation → is a partial order to within equivalence; (e) If α and β are of different types and , then where .
Proof.
(a) Let with . Then, there exists a surjection such that . We now show that . Indeed, for any , we have that:
Hence, so . Let , be s where . Then, for any , we have that:
Hence, so . If , then . As before, so .
(b) This was proved in (a). (c) We prove the result for observables and the result for instruments and s is similar. We have that and . Since and , we have that . Hence:
Hence, . (d) We only need to prove that if and , then . If are of the same type, the follows from (c). Suppose , and , . Then, and these are the same type so and hence, . Suppose and , . Then, and . By (a), we have . Since have the same type, and hence, . Suppose that and . Then, and . By (a) so and . Since these are the same type, we have that so . Similar reasoning holds for the cases and .
(e) If , and , then so for some surjection . By (b), we have that so letting we have that . Hence, . If , then . By (b), . Letting , we have that , . If , then . By (b) . Letting , we have that and . □
For an entity , we denote its set of parts by . We say that a set of entities coexist if for some entity . A coexistent set is thought of as being simultaneously measured by . A related concept is that of joint measurability [14]. We say that observables with outcome sets , are jointly measurable with joint observable if and for all we have:
We interpret as being the ith marginal of B as in classical probability theory [8,9,12]. Similar definitions can be made for the joint measurability of instruments and s.
Theorem 2.
A set of observables , is jointly measurable if and only if the coexists.
Proof.
If are jointly measurable, there exists a joint observable satisfying (4). Defining by
for , then by (4), we have that for all . Hence, , , so coexist. Conversely, suppose that coexist so there exists an observable such that , . We then have surjections such that , . Define , a surjection by and let . For , we obtain:
Thus, (4) holds so are jointly measurable. □
Theorem 2 also holds for instruments and s. An important property of coexistent entities is that they have joint probability distributions for all . For example, if coexist, then , for some . Then, for any , , the joint probability becomes:
As another example, if , then so , for surjections . We then obtain:
We can continue this for many coexistent entities. Moreover, the entities do not need to be of the same type. For instance, suppose where and . Then, we have that:
For , we define the probability distribution for all , . In a similar way, if , we define and if is a , then .
Lemma 1.
If α is an entity and is a surjection, then .
Proof.
We give the proof for and the proof for other entities is similar. For , we obtain:
The result now follows. □
We now consider sequential products of observables.
Theorem 3.
If and is a surjection, then A, and are parts of .
Proof.
Defining by we have that:
Thus, so . Defining by we obtain:
Hence, so . Defining by we have that:
It follows that . Hence, . □
Some results analogous to Theorem 3 hold for other entities.
Example 1.
We consider the simplest nontrivial example of a sequential product of observables. Let , be binary (diatomic) observables. Then, and:
Except in trivial cases, has precisely the following nine parts to within equivalence:
Notice that the sixth of the parts is and the seventh is A as required by Theorem 3. Each of the parts is a function of . The parts listed correspond to the following functions , (Table 1). □
Table 1.
Function values.
Example 2.
Similarly to Example 1, for the two binary instruments , , we have the instrument with and:
The nine parts of to within equivalence are:
As in Example 1, the sixth part is , however, unlike the observable case, the seventh part is not . In fact, unlike that case, is not a part of . □
If , the corresponding Lüders instrument is defined by and for all . It follows that [15]:
for all , . It is easy to check that . Hence, for , we have that if and only if .
Theorem 4.
(a) if and only if for all , ; (b) ; (c) An observable C satisfies if and only if .
Proof.
(a) For all , we have that:
On the other hand:
Hence, . (c) This follows from (b) and Theorem 1(a). □
Example 3.
We saw from Theorem 4(b) that . We now show that in general. Let and . The instruments and are called trivial instruments with observables and states , respectively, [2]. We have that:
Hence, and similarly . For all we obtain:
On the other hand:
We saw in Theorem 4(a) that , in general. The following lemma shows they can differ in a striking way.
Lemma 2.
If and are atomic observables on H, then for all , there exist numbers with such that and for all .
Proof.
For all we have that:
Since:
we obtain:
Hence:
Letting , the result follows. □
4. Composite Systems
Let and be Hilbert spaces with and . If represent quantum systems, we call a composite quantum system. For , we define the reduced effects , by , . We view to be the effect a as measured in a system . The map is a surjective effect algebra morphism from onto and similarly for [6,7]. Conversely, if , , then and:
Similarly, . It follows that:
and
An effect is factorized if for , [2].
Lemma 3.
If with , then a is factorized if and only if:
Proof.
If (9) holds, then a is factorized. Conversely, suppose a is factorized with , , . Then, and . Hence, and . We conclude that:
Corollary 1.
If , then if and only if or .
Proof.
If or , then clearly . Conversely, if , then by Lemma 3, or . In the latter case, . □
An effect is indecomposable if it has the form where and b is an atom.
Theorem 5.
Let be an atom where : (a) a is factorized if and only if and are indecomposable; (b) We can arrange the nonzero eigenvalues of and the nonzero eigenvalues of so that , . Hence, if , then the eigenvalues of and are identical.
Proof.
The unit vector has a Schmidt decomposition , , . We have that:
Hence:
and similarly:
Now a is factorized if and only if is factorized which is equivalent to and . Applying (10) and (11), we conclude that a is factorized if and only if and , in which case and are indecomposable. This completes the proof of (a). To prove (b), we see from (10), (11) that the eigenvalues of are and . It follows that . □
If we define the reduced observables , by and . Note that is indeed an observable because:
Lemma 4.
If and , then:
Proof.
For we have that:
The result now follows. □
In a similar way:
For , we define the A-random measure on by
for all . Thus, is the distribution of A in the random state . If , , we define the composite observable:
In this case, and for , we have that:
Hence, .
Lemma 5.
and .
Proof.
For , we obtain:
Hence:
The second equation is similar. □
A transition probability from to is a map satisfying for all . (The matrix is called a stochastic matrix.) Let with outcome-space and let be a transition probability from to . Then, is an observable on
H1 with outcome-space called a post-processing of A from to [12]. If we also have with outcome-space and μ a transition probability from to , we can form the post-processing μ • B.
Theorem 6.
(a) with outcome-space and is a post-processing from to where ; (b) If , then and .
Proof.
(a) The map is a transition probability because and:
Moreover, with outcome-space and we have that:
Hence, . (b) This follows from:
That is similar. □
We have seen in Theorem 2 that coexistence is equivalent to joint measurability. This is used in the next theorem [13].
Theorem 7.
(a) If coexist with joint observable and coexist with joint observable , then , coexist with joint observable ; (b) If coexist with joint observable C, then coexist with joint observable and coexist with joint observable .
Proof.
(a) We write for and for . Then:
and we have that:
Moreover:
and the result follows. (b) For all , we obtain:
Similarly, so coexist with joint observable . The result for is similar. □
For an instrument on the composite system, the reduced instrument on system 1 is defined by [9,10]
for all , . Similarly:
for all , .
Theorem 8.
and .
Proof.
For all , we have that:
We conclude that and similarly, . □
For we define the -random measure on by
For , we define with outcome-space by . It is easy to check that is indeed an instrument.
Theorem 9.
Let . (a) for all and for all . (b) .
Proof.
(a) For all we have that:
Similarly, for all . (b) For all , we have that:
and the result follows. □
A Kraus instrument is an instrument of the form where , . The operators are called Kraus operators for [5].
Lemma 6.
Let , be Kraus instruments with operators , , respectively. (a) is a Kraus instrument with operators ; (b) are Kraus instruments with operators:
Proof.
(a) For all , we have that:
and the result follows. (b) For we obtain:
This can be considered to be a Kraus instrument with operators given above. The result for is similar. □
Notice that a Lüders instrument defined by for all is a particular case of a Kraus instrument with operators [15].
Corollary 2.
Let , : (a) ; (b) where and where .
We say that a Kraus instrument with operators is factorized if for all . We conjecture that if is Kraus, then and need not be Kraus. However, we do have the following result.
Lemma 7.
If is Kraus and factorized, then and are Kraus.
Proof.
If the operators for satisfy , then for all , we have that:
Hence, is Kraus with operators . Similarly, is Kraus with operators . □
We do not know if the converse of Lemma 7 holds. We now consider trivial instruments (see Example 3).
Lemma 8.
Let , be trivial instruments with:
(a) is trivial with observable and state (b) , are trivial with observables , and states , respectively.
Proof.
(a) For all , , , we have that:
The result now follows. (b) This follows from:
and similarly:
Lemma 9.
Let be trivial with . (a) , are trivial with observables , and states , , respectively; (b) Letting we have that is trivial with observable and state . Moreover, and for all .
Proof.
(a) For all and , we have that:
Similarly, so the result follows. (b) This result follows from Lemma 8(b). □
We now consider s for composite systems. A single probe on has the form as defined before. As discussed earlier, is the instrument measured by . Then, and for , we obtain:
We have a similar expression for .
Corresponding to , we define the reduced where is given by
We then have for that:
Similarly, we define and an analogous formula for . Notice that (12) and (13) are quite similar and they are essentially an interchange of the two partial traces. We now show that they coincide.
Theorem 10.
(a) Let be finite-dimensional Hilbert spaces and let , . Then:
(b) and .
Proof.
(a) First suppose that is factorized. We then obtain:
We considered single probe composite s. We now briefly discuss general composite s. Let , be two s. Define the unitary swap operator [2]:
by
We now define the channel by
The composite of and is declared to be:
For , we have that:
The next result shows that has desirable properties.
Theorem 11.
(a) For we have:
(b) defining and in the usual way, we obtain:
and:
for all , .
5. Concluding Remarks
In this article, we only considered finite-dimensional Hilbert spaces. One reason for this was to avoid various measure theoretic details and thus greatly simplify the exposition. A second reason was that the direction of quantum investigations has largely changed over the last twenty years. This modern research is mainly concerned with more practical matters involving quantum computation and information theory [2,3,4,13]. Although it is restrictive to only consider finite-dimensional Hilbert spaces, the resulting structures are general enough to include these modern theories. Nevertheless, with more work, many of our results extend to the infinite-dimensional case. For example, in this situation, an observable is defined as an effect-valued measure , where is the collection of Borel subsets of [1,2,3]. If is another observable, we can define their sequential product by
for all . Some measure theoretic details are required to show that as defined in (17) for product sets extends to an effect-valued measure on . In a similar way, we define an instrument as an operation-valued measure on . Just as we did before, an instrument measures a unique observable . Moreover, it is straightforward to define measurement models for infinite-dimensional Hilbert spaces. One can define parts of observables in a natural way and employing measure theory, Theorem 1 extends to infinite-dimensions. We leave it to the reader to check which other theorems extend.
We now summarize the main results of this article. The basic concept of our work is an effect . An effect represents the simplest type of experiment in which the result of a measurement is either yes or no (also called true or false). An example would be to flip a switch: a light either goes on or does not go on. An observable A corresponds to a more complicated experiment that has a finite number of possible outcomes . If a measurement of A results in outcome x, then the effect is true (has answer yes) and otherwise is false (has answer no). If our underlying physical system is in a state , then the probability that a measurement of A results in outcome x is given by Born’s rule . Various physical apparatuses may be employed to measure an observable A. The most common of these is where is a completely positive linear map. We say that measures the observable A if . Thus, and A have the same probability distribution. However, gives more information than its unique measured observable because determines the updated state when A has outcome x.
Finally, we have the concept of a finite measurement model given by . In this case, H is the Hilbert space describing the system being observed and K is the Hilbert space describing the measuring apparatus which is in the initial state . In order to perform the measurement, the two systems interact, which is described by the tensor product and a channel ν on . The initial state of the observed system combines with η to form the state . This state is sent through the channel ν and the probe observable is applied to give a measurement outcome. The model measures a unique instrument given by
and a unique observable .
We defined a function of an observable B and call A a part of B. We can then think of A as being obtained from B by piecing together parts of B. This concept is extended to parts of instruments and ’s. Theorem 1 shows that parts are preserved under measurements. For example, for all instruments and for all ’s . We then consider the coexistence of observables and Theorem 2 shows that observables coexist if and only if they are jointly measurable. We next introduce the concepts of sequential products and conditioning of observables . We interpret as the observable obtained by first measuring A and then measuring B and as the observable B conditioned on first measuring the observable A. Theorem 3 shows that and are parts of . Sequential products of instruments are also discussed. For an observable , we consider the corresponding Lüders instrument . Theorem 4 shows that if and only if A and B commute. Moreover, and C is a part of if and only if C is a part of .
Section 4 considers composite systems . If , we define the reduced effects , , where is thought of as the effect as measured in the system , . This concept is extended to the reduced observables , , for . Theorem 6 proves results concerning post-processing for composite systems and Theorem 7 shows that coexistence is preserved on taking tensor products and under reducing observables. Reduced instruments , , for are also defined and Theorems 8 and 9 show (among other things) that , and . Finally, we consider ’s for composite systems. A single probe on has the usual form . Then, for , we obtain given by
where . There is a similar expression for . Corresponding to , we define the reduced given by where is given by
We think of as the as measured by the system. We then obtain:
Similarly, we define and obtain an analogous formula for . Now, and essentially represent the same object and (18), (19) are quite similar. In fact, Theorem 10 shows that they coincide. This paper closes with a definition of a general composite MM and Theorem 11 shows that this definition has desirable properties.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Conflicts of Interest
The author declares no conflict of interest.
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