Measurement and uncertainty
Calculate projective measurement updates and distinguish quantum outcome spread from instrument noise and ignorance.
On this page
The question for today
What you’ll understand
- Use projectors to calculate ideal outcome probabilities and conditional states.
- Explain an uncertainty relation as a statement about state-dependent spreads.
- Predict how an intervening incompatible measurement changes later statistics.
Quantum measurement has two jobs in a calculation: predicting a recorded result and specifying the conditional state used for later predictions. Those jobs are easy to confuse with an observer learning an already definite classical value. Here we use an explicitly ideal projective model, identify its assumptions, and separate preparation uncertainty from practical measurement imperfections.
Probabilities and conditional updates
For an ideal projective measurement, the outcome corresponds to an orthogonal projector . Its probability is . If that outcome is recorded, the conditional state is the projected component divided by its norm. This is the state used to predict subsequent measurements on that selected subensemble. The division is essential: projection alone generally produces a vector with norm less than one.
A degenerate outcome may contain several independent states. An ideal minimally disturbing projective measurement keeps the part inside that entire eigenspace; it need not select one arbitrarily chosen eigenvector. Real instruments can introduce additional disturbance, inefficiency, or unrecorded outcomes, requiring a more general measurement model. Neither the projector rule nor its laboratory use requires consciousness. The relevant ingredients are physical interactions, recorded alternatives, and which data the prediction is conditioned on.
Uncertainty is a property of a preparation
The Robertson inequality bounds the product of standard deviations of two observables in the same preparation. For position and momentum it becomes . Imagine preparing many equivalent systems and measuring position on one collection and momentum on another. Even ideal instruments cannot make both distributions arbitrarily narrow for that state. This statement is not merely a claim that clumsy equipment kicks the particle.
The more general bound contains the expectation of a commutator and can depend on the state. A zero right-hand side does not establish that both spreads vanish; it may simply make that particular lower bound uninformative. Standard deviations must also exist for the stated distributions. Energy–time relations require their own operational definitions because time is usually the evolution parameter here, not an operator interchangeable with position.
Why the order of questions changes the experiment
For a two-level system, define the basis as and the basis as . An ideal measurement of returns zero with certainty and leaves that state unchanged. A later identical ideal measurement remains certain if no intervening dynamics changes the preparation. Repeatability is thus conditional on both the measurement model and what happens between readings.
Insert an measurement between those two measurements. The intermediate outcome is plus or minus with equal probability, and each corresponding state yields either outcome with equal probability. Even when the intermediate result is ignored, the final zero probability falls to one half. This is a change in the physical measurement sequence, not a contradiction or a dependence on whether a person chooses to inspect the intermediate display.
Work it through
A measurement between two repeatable readings
Prepare , perform an ideal measurement, and then measure . Find the probability of the final outcome without selecting either intermediate result.
Compute the intermediate overlaps: . There are two recorded alternatives, each producing its corresponding conditional state.
From either or , the probability of final is . The relative sign changes an amplitude but not this particular final squared magnitude.
Average over the exclusive intermediate records: . Add probabilities here because the intermediate outcomes are distinguished records.
The final zero probability is 1/2. Omitting the intermediate measurement would give probability 1, so the two procedures are experimentally different.
The living notebook
Explore the model
Interactive lab · probability
Compare the predicted outcome distribution
The lab plots the exact binomial count distribution for n independent trials with success probability p. Compare p=1 with p=1/2 as an analogy for definite versus uncertain repeated outcomes. It neither samples actual measurements nor represents joint position–momentum statistics, so it cannot test the uncertainty product.
- Mean · np
- 5
- Variance · np(1−p)
- 2.5
- Total probability
- 1
These are exact binomial probabilities for independent trials with one fixed success probability. Playback tours the distribution; it does not resample outcomes. Parameter changes pause playback and recompute the model at the current elapsed time. Reset restores both parameters and returns time to zero. Playback starts paused. Adjust either parameter, then choose Play or Step.
Look forA quantum standard deviation describes the spread across repeated preparations, even for an ideal measurement.
Pause & reason
Check your understanding
Your turn
Try it yourself
A normalized state is . Measure and select the result, then immediately repeat the ideal measurement. Find the selection probability and the repeated result probability.
Take a moment to make a prediction or work it out on paper. Open a hint when you need one.
Give me a hint
The first probability uses the original amplitude. The second uses the normalized conditional state.
Compare with the solution
The selected result occurs with probability . Conditional on it, the state is , so the immediate repeat gives the same result with probability 1. Confusing conditional and unconditional probabilities would incorrectly carry the original factor of into the selected subensemble.
Keep the idea
Take it with you
- Projectors specify ideal outcome probabilities and normalized conditional states.
- Quantum uncertainty describes preparation-dependent distributions, not consciousness or merely poor instruments.
- An intervening incompatible measurement can change the statistics of a later reading.
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