University Physics IV · topic UPIV-07.03
Superpositions, measurement statistics, and time evolution in a box
Connect the ideas, make your working visible, and test the result. Use the hints when you need a starting point; open the full solution after your attempt.
Further practice
Work through a fresh case before tackling the extension.
Superpositions and time evolution in a box: predict a finite change
A superposition in a box is a normalised sum Ψ = Σ cₙψₙ over the well's energy eigenfunctions, in which |cₙ|² is the probability that an energy measurement returns Eₙ and each term evolves by its own phase factor e^(−iEₙt/ħ). Use the supplied topic model Y_out = cosd(a)^2 + n^2*sind(a)^2, where Y_out is MEAN ENERGY ⟨H⟩ (E₁). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; sind/cosd/tand use degrees. a is Mixing angle θ, measured in °. Keep n (Upper level n) = 2; t (Elapsed time in beat periods) = 0 fixed. For this exercise use only 9.5625 ° ≤ a ≤ 80.4375 ° on this continuous branch. A grid of test settings is an input restriction for this exercise, not by itself a claim of physical quantisation. This calculation retains the topic model's stated assumptions; it does not extend them to a different physical setup. Calculate Y_out at a = 25.155 ° and 35.0775 °. Find the signed output change ΔY_out and the average sensitivity ΔY_out/Δa across this interval. State whether the response increases or decreases; distinguish this interval average from a derivative at one point.
MEAN ENERGY ⟨H⟩ against a
Predictions from the supplied Superpositions and time evolution in a box model, with all other inputs fixed as stated in the question. 5 calibration points support a trend comparison; they are not measured data or a straight-line law.
Vertical axis (y): MEAN ENERGY ⟨H⟩ / E₁
Horizontal axis (x): a: Mixing angle θ / °
Axes show the supplied calibration interval; the origin may lie outside the plot.
Read the exact data as a table
| a: Mixing angle θ / ° | MEAN ENERGY ⟨H⟩ / E₁ |
|---|---|
| 9.5625 | 1.082790930299683 |
| 27.28125 | 1.6302781778102293 |
| 45 | 2.4999999999999996 |
| 62.71875 | 3.3697218221897702 |
| 80.4375 | 3.9172090697003172 |
These are the supplied values, not a worked answer. No fitted trend is drawn.
Show a hint
Substitute each value of a separately while keeping every other input fixed, then subtract in the same order.
Reveal the worked solution
- At a = 25.155 °, substitute into Y_out = cosd(a)^2 + n^2*sind(a)^2: Y₀ = 1.54205 E₁.
- At a = 35.0775 °, the same fixed-parameter model gives Y₁ = 1.99078 E₁.
- Signed change: ΔY_out = Y₁ − Y₀ = 0.448735 E₁; the input change is Δa = 9.9225 °.
- Average sensitivity is ΔY_out/Δa = 0.045224 E₁ per °. Its positive sign means that this response increases over the specified interval.
- This is a secant slope between two settings. It equals a point derivative only for a locally linear relation or in a justified small-interval limit.
Answer Y₀ = 1.54205 E₁, Y₁ = 1.99078 E₁; ΔY_out = 0.448735 E₁; average sensitivity = 0.045224 E₁ per °.
Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.
Extra challenging questions
Work through the linked stages and explain what each result means.
Superpositions and time evolution in a box: infer an input with uncertainty
A superposition in a box is a normalised sum Ψ = Σ cₙψₙ over the well's energy eigenfunctions, in which |cₙ|² is the probability that an energy measurement returns Eₙ and each term evolves by its own phase factor e^(−iEₙt/ħ). Use the supplied topic model Y_out = cosd(a)^2 + n^2*sind(a)^2, where Y_out is MEAN ENERGY ⟨H⟩ (E₁). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; sind/cosd/tand use degrees. a is Mixing angle θ, measured in °. Keep n (Upper level n) = 2; t (Elapsed time in beat periods) = 0 fixed. For this exercise use only 9.5625 ° ≤ a ≤ 80.4375 ° on this continuous branch. A grid of test settings is an input restriction for this exercise, not by itself a claim of physical quantisation. This calculation retains the topic model's stated assumptions; it does not extend them to a different physical setup. A reading gives Y_out = 2.9031906 E₁, with a bounded reading uncertainty ±0.1521 E₁. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown a by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.0070875 °, using a numerical bracket if required. Find the full input interval consistent with the bounded reading uncertainty and compare it with a symmetric input-error assumption. Do not assume that the supplied calibration points prove monotonicity outside this branch.
MEAN ENERGY ⟨H⟩ against a
Predictions from the supplied Superpositions and time evolution in a box model, with all other inputs fixed as stated in the question. 5 calibration points support a trend comparison; they are not measured data or a straight-line law.
Vertical axis (y): MEAN ENERGY ⟨H⟩ / E₁
Horizontal axis (x): a: Mixing angle θ / °
Axes show the supplied calibration interval; the origin may lie outside the plot.
Read the exact data as a table
| a: Mixing angle θ / ° | MEAN ENERGY ⟨H⟩ / E₁ |
|---|---|
| 9.5625 | 1.082790930299683 |
| 27.28125 | 1.6302781778102293 |
| 45 | 2.4999999999999996 |
| 62.71875 | 3.3697218221897702 |
| 80.4375 | 3.9172090697003172 |
These are the supplied values, not a worked answer. No fitted trend is drawn.
Show a hint
The model's endpoint predictions bracket the reading. Solve Y_out(a) = Y_measured and then solve again at Y_measured − u and Y_measured + u; a nonlinear inverse can give unequal input deviations.
Reveal the worked solution
- Endpoint predictions are Y_out(9.5625) = 1.08279 E₁ and Y_out(80.4375) = 3.91721 E₁. The target lies between them. On the selected branch the response is strictly increasing.
- Endpoint interpolation gives a_line = 9.5625 + (2.90319 − 1.08279)(80.4375 − 9.5625)/(3.91721 − 1.08279) = 55.0818 °. This estimates the inverse of the endpoint chord, rather than assuming that chord is the physical model.
- Use the actual equation, not the chord, and bracket its root. Evaluating the midpoint and retaining the sign-changing half-interval gives a = 52.7962 °; substituting back gives Y_out = 2.9031906 E₁.
- The interpolation error in the input is |a_line − a| = 2.28558 °. This is larger than the requested numerical tolerance.
- Invert both limits Y_out = 2.7510906 E₁ and 3.0552906 E₁. Sort the roots even if the response decreases: 49.8182 ° ≤ a ≤ 55.8638 °.
- The lower and upper input deviations are 2.97809 ° and 3.06753 °. They differ because the inverse response is nonlinear; a symmetric error bar would only be a local approximation. This interval includes reading uncertainty only, not model error or uncertainty in fixed inputs.
Answer a ≈ 52.7962 °; compatible interval [49.8182, 55.8638] °. Endpoint interpolation gives 55.0818 °.
Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.