8.2: Properties of quasi-product observables
Consider the measurement ${\mathsf M}_{\overline{\mathcal A}} ( {\mathsf O}_{12} {{=}} ( X_1 \times X_2, {\cal F}_1 \boxtimes {\cal F}_2, F_{12}), S_{[\rho]})$ with the sample probability space $
(
X_1 \times X_2,
{\cal F}_1 \boxtimes {\cal F}_2,
{}_{{\mathcal A}^*} \big(\rho,
{}F_{12} (\cdot ) {}\big) {}_{\overline{\mathcal A} }
)$.
Put
where, $\Xi^c$ is the complement of $\Xi$,
that is,
$\Xi^c=\{ x \in X \;|\; x \notin \Xi \}$. Also, note that
Let ${\mathsf O}_{1}$ ${{=}}$ $( X_1, {\cal F}_1 , F_{1})$ and ${\mathsf O}_{2}$ ${{=}}$ $( X_2, {\cal F}_2 , F_{2}{} )$ be observables in $C (\Omega)$. Let ${\mathsf O}_{12}$ ${{=}}$ $(
X_1 \times X_2,
{\cal F}_1 \times {\cal F}_2,
F_{12}{}{{=}} F_1
\mathop{\overset{qp}{\times}}
F_2
)$ be a quasi-product observable
of ${\mathsf O}_{1}$ and ${\mathsf O}_{2}$. That is, it holds that
Then, putting $\alpha_\rho^{^{\Xi_1 \times \Xi_2}}
=
{}_{{\mathcal A}^*} \big(\rho,
{}F_{12} (\Xi_1 \times \Xi_2) {}\big) {}_{\overline{\mathcal A} }
=\rho(
F_{12} (\Xi_1 \times \Xi_2)
)
$, we see
Reversely, for any $\alpha^{{}^{\Xi_1 \times \Xi_2}}_\rho$ satisfying (8.3), the observable ${\mathsf O}_{12}$ defined by (8.2) is a quasi-product observable of ${\mathsf O}_1$ and ${\mathsf O}_2$. Also, it holds that
Proof. Though this lemma is easy, we add a brief proof for completeness. $ 0 {{\; \leqq \;}}$ $ \rho( {}F (( \Xi'_1 \times \Xi'_2) {}) ) $ $ {{\; \leqq \;}}1$, $(\forall \Xi'_1
\in {\cal F}_1,
\Xi'_2 \in {\cal F}_2
)$ we see, by (8.2) that
which clearly implies (8.3). Conversely. if $\alpha$ satisfies (8.3), then we easily see (8.2), Also, (8.4) is obvious. This completes the proof.
Let ${\mathsf O}_{12}$ ${{=}}$ $( X_1 \times X_2,
{\cal F}_1 \boxtimes {\cal F}_2,
F_{12}{}{{=}} F_1
{\mathop{\overset{qp}{\times}}_{}}
F_2)$ be a quasi-product observable of ${\mathsf O}_{1}$ ${{=}}$ $(
X_1,
{\cal F}_1 ,
F_{1})$ and ${\mathsf O}_{2}$ ${{=}}$ $(
X_2,
{\cal F}_2 ,
F_{2})$ in $\overline{\mathcal A}$. Consider the measurement ${\mathsf M}_{\overline{\mathcal A}} ({\mathsf O}_{12} $ ${{=}}(X_1 \times X_2,
{\cal F}_1 \boxtimes {\cal F}_2,
F_{12}{}{{=}} F_1
{\mathop{\overset{qp}{\times}}}
F_2),$ $ S_{[\rho]})$). And assume that a measured value$(x_1, x_2)$ $(\in X_1 \times X_2 )$ is obtained. And assume that we know that $x_1 \in \Xi_1$. Then, the probability (i.e., the conditional probability) that $x_2 \in \Xi_2$ is given by
Example 8.5 [Example of tomatoes]
Let $\Omega$ $ = $ $ \{ \omega_1 , \omega_2 , ...., \omega_N \}$ be a set of tomatoes, which is regarded as a compact Hausdorff space with the discrete topology. Consider the classical basic structure
Consider yes-no observables ${\mathsf O}_{RD}$ $\equiv$ $(X_{ RD} , 2^{ X_{RD} } , F_{RD})$ and ${\mathsf O}_{SW}$ $\equiv$ $(X_{SW} , 2^{ X_{SW} } , F_{SW})$ in $C(\Omega )$ such that:
where we consider that "$y_{{}_{RD}}$" and "$n_{{}_{RD}}$" respectively mean "RED" and "NOT RED". Similarly, "$y_{{}_{SW}}$" and "$n_{{}_{SW}}$" respectively mean "SWEET" and "NOT SWEET".
For example, the $\omega_1$ is red and not sweet, the $\omega_2$ is red and sweet, etc. as follows.
Lemma 8.4 [The condition of quasi-product observables]
Consider the general basic structure
\begin{align}
\mbox{
{$[ {\mathcal A} \subseteq \overline{\mathcal A} \subseteq B(H)]$}.
}
\end{align}
Next, consider the quasi-product observable as follows.
\begin{align} {\mathsf O}_{12} = (X_{{}_{RD}} \times X_{{}_{SW}} , 2^{ X_{{}_{RD}} \times X_{{}_{SW}} }, F {{=}} F_{{}_{RD}} \times F_{{}_{SW}}) \end{align} That is, \begin{align} & \; \; \mbox{Rep}^{\{( y_{{{}_{RD}}} , y_{{{}_{SW}}}) \}}_{\omega_k} [{\mathsf O}_{12}] = \left[\begin{array}{ll} [{}F (\{( y_{{{}_{RD}}} , y_{{{}_{SW}}}) \}) {}] ({\omega_k}) & [{}F (\{( y_{{{}_{RD}}} , n_{{{}_{SW}}}) \}) {}] ({\omega_k}) \\ {}[{}F (\{( n_{{{}_{RD}}} , y_{{{}_{SW}}}) \}) {}] ({\omega_k}) & [{}F (\{( n_{{{}_{RD}}} , n_{{{}_{SW}}}) \}) {}] ({\omega_k}) \\ \end{array}\right] \\ = & \left[\begin{array}{ll} \alpha_{_{\{( y_{{}_{RD}} , y_{{{}_{SW}}}) \} }} & [{}F_{{}_{RD}} (\{ y_{{{}_{RD}}} \}) {}] - \alpha_{_{\{( y_{{{}_{RD}}} , y_{{{}_{SW}}}) \} }} \\ {}[{}F_{{}_{SW}} (\{ y_{{{}_{SW}}} \}) {}] - \alpha_{_{\{( y_{{{}_{RD}}} , y_{{{}_{SW}}}) \} }} & 1+ \alpha_{_{\{( y_{{{}_{RD}}} , y_{{{}_{SW}}}) \} }} - [{}F_{{}_{RD}} (\{ y_{{{}_{RD}}} \}) {}] - [{}F_{{}_{SW}} (\{ y_{{{}_{SW}}} \}) {}] \\ \end{array}\right] \end{align}where $\alpha_{_{\{( y_{{{}_{RD}}} , y_{{{}_{SW}}}) \} }} ({\omega_k})$ satisfies the (8.3). When we know that a tomato ${\omega_k} $ is red, the probability $P$ that the tomato ${\omega_k} $ is sweet is given by
\begin{align} P= \frac{ [{}F (\{(y_{{}_{RD}} , y_{{}_{SW}}) \}){}]({\omega_k}) } { [{}F (\{(y_{{}_{RD}} , y_{{}_{SW}}) \}){}]({\omega_k}) + [{}F (\{(y_{{}_{RD}} , n_{{}_{SW}}) \}){}]({\omega_k}) } = \frac{ [{}F (\{(y_{{}_{RD}} , y_{{}_{SW}}) \}){}]({\omega_k}) } { [{}F_{{}_{RD}} (\{ y_{{{}_{RD}}} \}) {}] ({\omega_k}) } \end{align}Since $[{}F (\{(y_{{}_{RD}} , y_{{}_{SW}}) \}){}]({\omega_k})= \alpha_{_{\{(y_{{}_{RD}} , y_{{}_{SW}}) \}} } (\omega_k)$, the conditional probability $P$ is estimated by
\begin{align} & \frac{\max \{ 0, [{}F_1 ( \{ y_{{{}_{RD}}} \} {}) {}] ({\omega_k}) + [{}F_2 ( \{ y_{{{}_{SW}}} \} {}) {}] ({\omega_k}) -1 {} \} }{{ [{}F_{{}_{RD}} (\{ y_{{{}_{RD}}} \}) {}] ({\omega_k}) }} {{\; \leqq \;}} P {{\; \leqq \;}} \frac{ \min [{}F_1 ( \{ y_{{{}_{SW}}} \} {}) {}] ({\omega_k}) , \; [{}F_2 ( \{ y_{{{}_{SW}}} \} {}) {}] ({\omega_k}) \} }{{ [{}F_{{}_{RD}} (\{ y_{{{}_{RD}}} \}) {}] ({\omega_k}) }} \end{align}