
# Observables & the Measurement Postulate

> Postulate 2 connects the abstract state vector to the laboratory. It tells us what is *measurable*
> (the eigenvalues of Hermitian operators), with what *probability* (the Born rule), and what happens
> to the state *afterward* (collapse). This is the postulate that makes quantum mechanics
> probabilistic and irreversible-on-measurement — and it is built entirely from the spectral theorem
> you proved in Term 0. Course 1.3 expands every piece; here we state the axiom and its core
> consequences.

## Learning Objectives

After this lesson you will be able to:

1. State **Postulate 2** using the spectral decomposition of a Hermitian observable.
2. Compute outcome probabilities via the **Born rule** and the **expectation value** $\langle A\rangle$.
3. Determine the **post-measurement state** (collapse) and explain repeatability.
4. Connect "observable = Hermitian operator" to the spectral theorem and real eigenvalues.
5. Simulate projective measurements in NumPy.

---

## Intuition

Classically, you read off a property and the system is unchanged. Quantum measurement is different in
three ways. First, the *possible results* of measuring a quantity are constrained — they are exactly
the eigenvalues of the operator representing that quantity. Second, the *result is random*, with
probabilities set by how much the state "overlaps" each eigenspace (the Born rule). Third, the act of
measuring *changes the state*: it collapses onto the eigenspace of the result you got. Measure again
immediately and you get the same answer — measurement is repeatable, but the first one already
disturbed the system.

---

## Theory

### Postulate 2 (Observables and measurement)

> **Postulate 2.** An **observable** is a Hermitian operator $A = A^\dagger$ on the state space, with
> spectral decomposition (from [0.1.5](../../term-0-foundations/c01-linear-algebra/05-eigen-spectral-theorem.md))
> $$ A = \sum_k \lambda_k P_k, \qquad P_k P_l = \delta_{kl}P_k,\quad \sum_k P_k = I, $$
> where $\lambda_k$ are the (real, distinct) eigenvalues and $P_k$ the orthogonal projectors onto their
> eigenspaces. Measuring $A$ on state $\lvert\psi\rangle$ yields outcome $\lambda_k$ with probability
> $$ \boxed{\,p(\lambda_k) = \langle\psi| P_k |\psi\rangle = \lVert P_k\lvert\psi\rangle\rVert^2\,} \quad(\textbf{Born rule}), $$
> and immediately after a result $\lambda_k$, the state **collapses** to the normalized projection
> $$ \lvert\psi'\rangle = \frac{P_k\lvert\psi\rangle}{\sqrt{p(\lambda_k)}} . $$

Why Hermitian? Because measured values are real numbers, and Hermitian operators are exactly those
with **real eigenvalues** and an **orthonormal eigenbasis** ([0.1.6](../../term-0-foundations/c01-linear-algebra/06-special-operators.md)).
The orthonormal eigenbasis guarantees the outcomes are perfectly distinguishable and the projectors
resolve the identity.

### The Born rule is a probability distribution

The numbers $p(\lambda_k)$ are a valid distribution: each $p(\lambda_k) = \langle\psi|P_k|\psi\rangle =
\langle\psi|P_k^\dagger P_k|\psi\rangle = \lVert P_k\lvert\psi\rangle\rVert^2 \ge 0$ (projectors are
Hermitian idempotents), and they sum to one,

$$
\sum_k p(\lambda_k) = \sum_k \langle\psi|P_k|\psi\rangle = \langle\psi|\Big(\sum_k P_k\Big)|\psi\rangle = \langle\psi|I|\psi\rangle = \langle\psi|\psi\rangle = 1,
$$

using completeness $\sum_k P_k = I$ and normalization. This is the physical meaning of the
[Parseval identity](../../term-0-foundations/c01-linear-algebra/02-inner-products-norms.md) and of
normalization from [1.1.1](01-state-postulate.md). For a **non-degenerate** observable with
eigenvectors $\lvert v_k\rangle$, $P_k = \lvert v_k\rangle\langle v_k|$ and the Born rule takes its
familiar form $p(\lambda_k) = |\langle v_k|\psi\rangle|^2$.

### Expectation value

The **expectation value** (mean outcome over many measurements on identically prepared states) is

$$
\langle A\rangle_\psi := \sum_k \lambda_k\, p(\lambda_k) = \sum_k \lambda_k \langle\psi|P_k|\psi\rangle = \langle\psi|\Big(\sum_k\lambda_k P_k\Big)|\psi\rangle = \langle\psi|A|\psi\rangle .
$$

So the operator does double duty: its eigenvalues are the outcomes, and the sandwich
$\langle\psi|A|\psi\rangle$ is their average. (This is real because $A$ is Hermitian.) The variance and
the uncertainty relation are built from this in [1.3.2](../c03-measurement/02-expectation-uncertainty.md).

### Collapse and repeatability

After outcome $\lambda_k$, the state is $P_k\lvert\psi\rangle/\sqrt{p(\lambda_k)}$, which lies entirely
in the $\lambda_k$-eigenspace. Measure $A$ again: now $P_k\lvert\psi'\rangle = \lvert\psi'\rangle$ so
$p(\lambda_k) = 1$ — the **same** result with certainty. Measurement is **repeatable**, but the first
measurement has irreversibly disturbed any prior superposition. This irreversibility is unique to
measurement; ordinary evolution (Postulate 3) is reversible.

> **Compatibility (preview).** Two observables can be measured simultaneously with definite values iff
> they **commute** ($[A,B]=0$), i.e. share an eigenbasis ([0.1.5](../../term-0-foundations/c01-linear-algebra/05-eigen-spectral-theorem.md)).
> Incompatible observables (like $X$ and $Z$) obey an uncertainty relation — [1.3.2](../c03-measurement/02-expectation-uncertainty.md).

---

## Worked Examples

### Example 1 — Measuring $Z$ on $\lvert+\rangle$

The observable $Z = (+1)\lvert0\rangle\langle0| + (-1)\lvert1\rangle\langle1|$ has projectors $P_{+1} =
\lvert0\rangle\langle0|$, $P_{-1} = \lvert1\rangle\langle1|$. On $\lvert+\rangle = \tfrac1{\sqrt2}(\lvert0\rangle+\lvert1\rangle)$:

$$
p(+1) = \langle+|P_{+1}|+\rangle = |\langle0|+\rangle|^2 = \tfrac12, \qquad p(-1) = |\langle1|+\rangle|^2 = \tfrac12 .
$$

Expectation $\langle Z\rangle = (+1)\tfrac12 + (-1)\tfrac12 = 0$. If the result is $+1$, the state
collapses to $P_{+1}\lvert+\rangle/\sqrt{1/2} = \lvert0\rangle$.

### Example 2 — Same state, different observable ($X$ on $\lvert+\rangle$)

Measure $X = (+1)\lvert+\rangle\langle+| + (-1)\lvert-\rangle\langle-|$ on $\lvert+\rangle$. Since
$\lvert+\rangle$ *is* the $+1$ eigenvector, $p(+1) = |\langle+|+\rangle|^2 = 1$ and $p(-1) = 0$:
a **certain** outcome, $\langle X\rangle = 1$, and no collapse. The same state is "random" for $Z$ but
"definite" for $X$ — measurement outcomes are observable-relative, the seed of complementarity.

---

## Hands-on (Python)

```python
import numpy as np

ket0 = np.array([1, 0], dtype=complex)
ket1 = np.array([0, 1], dtype=complex)
plus = (ket0 + ket1) / np.sqrt(2)

def projective_measure(psi, projectors, eigenvalues, rng=None):
    """Sample one outcome via the Born rule and return (eigenvalue, collapsed state)."""
    rng = rng or np.random.default_rng()
    probs = [np.real(psi.conj() @ (P @ psi)) for P in projectors]   # <ψ|P_k|ψ>
    k = rng.choice(len(projectors), p=probs)
    collapsed = (projectors[k] @ psi) / np.sqrt(probs[k])
    return eigenvalues[k], collapsed

# Z-measurement on |+>:
P0 = np.outer(ket0, ket0.conj())     # projector for eigenvalue +1
P1 = np.outer(ket1, ket1.conj())     # projector for eigenvalue -1
val, post = projective_measure(plus, [P0, P1], [+1, -1],
                               rng=np.random.default_rng(0))
print("outcome:", val, " collapsed to:", np.round(post, 3))   # +1 -> |0>  (rng-dependent)

# Expectation <Z> = <ψ|Z|ψ> directly:
Z = np.array([[1, 0], [0, -1]], dtype=complex)
print("〈Z〉 =", np.real(plus.conj() @ Z @ plus))               # 0.0
```

```python
# Empirical Born statistics over many shots match the predicted 50/50:
from collections import Counter
rng = np.random.default_rng(1)
counts = Counter(projective_measure(plus, [P0, P1], [+1, -1], rng)[0] for _ in range(2000))
print(counts)        # ~ Counter({1: ~1000, -1: ~1000})
```

> **On Braket.** A real device or simulator only ever returns *samples* (shots), exactly like
> `projective_measure` above — never the amplitudes. You estimate probabilities and expectation values
> from finite shots, with the $1/\sqrt{M}$ error you derived in
> [0.2.2](../../term-0-foundations/c02-probability-information/02-random-variables-expectation.md). You'll
> measure $\lvert+\rangle$ on the local simulator in [1.2.3](../c02-qubits-bloch/03-bases-and-expectation.md).

---

## Exercises

**E1 (easy).** For $\lvert\psi\rangle = \tfrac1{\sqrt5}(\lvert0\rangle + 2\lvert1\rangle)$, give the
$Z$-measurement probabilities and $\langle Z\rangle$.

<details><summary>Solution</summary>

$p(+1) = |1/\sqrt5|^2 = 1/5$, $p(-1) = |2/\sqrt5|^2 = 4/5$. $\langle Z\rangle = (1)(1/5)+(-1)(4/5) = -3/5$.
</details>

**E2 (easy).** Show the Born probabilities sum to $1$ for any normalized state and any observable, in
one line.

<details><summary>Solution</summary>

$\sum_k\langle\psi|P_k|\psi\rangle = \langle\psi|\sum_k P_k|\psi\rangle = \langle\psi|I|\psi\rangle = 1$
by completeness and normalization. ∎
</details>

**E3 (medium).** Prove $\langle A\rangle = \langle\psi|A|\psi\rangle$ is real for any Hermitian $A$
(without invoking the eigenvalue formula).

<details><summary>Solution</summary>

$\overline{\langle\psi|A|\psi\rangle} = \langle\psi|A^\dagger|\psi\rangle = \langle\psi|A|\psi\rangle$
using $A=A^\dagger$ (and $\overline{\langle\psi|M|\psi\rangle} = \langle\psi|M^\dagger|\psi\rangle$). A
number equal to its own conjugate is real. ∎
</details>

**E4 (medium).** A qubit is measured with the observable $A = 3\lvert+\rangle\langle+| - 1\lvert-\rangle\langle-|$
in state $\lvert0\rangle$. Find the outcome probabilities, $\langle A\rangle$, and the post-measurement
states.

<details><summary>Solution</summary>

$\lvert0\rangle = \tfrac1{\sqrt2}(\lvert+\rangle+\lvert-\rangle)$, so $p(3) = |\langle+|0\rangle|^2 = 1/2$
and $p(-1) = |\langle-|0\rangle|^2 = 1/2$. $\langle A\rangle = 3(\tfrac12) + (-1)(\tfrac12) = 1$.
Post-measurement: outcome $3 \Rightarrow \lvert+\rangle$; outcome $-1 \Rightarrow \lvert-\rangle$.
</details>

**E5 (hard).** Show that if $\lvert\psi\rangle$ is an eigenvector of observable $A$, the measurement of
$A$ is deterministic and leaves the state unchanged. Conversely, argue that zero variance implies the
state is an eigenvector.

<details><summary>Solution</summary>

If $A\lvert\psi\rangle = \lambda_j\lvert\psi\rangle$ then $P_k\lvert\psi\rangle = \delta_{kj}\lvert\psi\rangle$,
so $p(\lambda_j) = 1$ and all others $0$; the collapse $P_j\lvert\psi\rangle/\sqrt1 = \lvert\psi\rangle$
is unchanged. Conversely the variance $(\Delta A)^2 = \langle A^2\rangle - \langle A\rangle^2 = \sum_k(\lambda_k-\langle A\rangle)^2 p(\lambda_k)$;
being a sum of nonnegative terms, it is $0$ iff all probability sits on a single eigenvalue, i.e.
$\lvert\psi\rangle$ lies in one eigenspace — an eigenvector. ∎ (Formalized in [1.3.2](../c03-measurement/02-expectation-uncertainty.md).)
</details>

---

## Checkpoint

1. State Postulate 2, including the role of the spectral decomposition.
2. Write the Born rule and prove the probabilities sum to $1$.
3. Give two equivalent formulas for the expectation value $\langle A\rangle$.
4. What is the post-measurement state, and what does repeatability mean?
5. Why must observables be Hermitian?

<details><summary>Answers</summary>

1. Observables are Hermitian $A=\sum_k\lambda_k P_k$; measuring gives outcome $\lambda_k$ w.p.
   $\langle\psi|P_k|\psi\rangle$ and collapses the state to $P_k\lvert\psi\rangle/\sqrt{p_k}$.
2. $p(\lambda_k)=\langle\psi|P_k|\psi\rangle$; $\sum_k p_k = \langle\psi|\sum_k P_k|\psi\rangle = \langle\psi|\psi\rangle = 1$.
3. $\langle A\rangle = \sum_k\lambda_k p(\lambda_k) = \langle\psi|A|\psi\rangle$.
4. $P_k\lvert\psi\rangle/\sqrt{p_k}$, lying in the $\lambda_k$-eigenspace; an immediate re-measurement
   returns $\lambda_k$ with certainty.
5. Measured values are real and outcomes must be perfectly distinguishable; Hermitian operators have
   real eigenvalues and an orthonormal eigenbasis.

</details>

---

## Further Reading

- **[NC]** Nielsen & Chuang, §2.2.3 (Postulate 3, measurement) and §2.2.5 (projective measurements).
- **[Sak]** Sakurai & Napolitano, §1.4 — measurements, observables, expectation values.
- **[Pre]** Preskill, *Ph219*, Ch. 2 — the measurement axiom.

---

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