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University Quantum Mechanics I · topic UQMI-10.01

What the solved systems were already telling us

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.

Further practice · 01

What the solved systems were already telling us: predict a finite change

Reading a solved system as a vector space means treating its orthonormal eigenfunctions ψₙ as perpendicular unit axes, the overlap integral ∫φ*ψ dx as the dot product, and the coefficients cₙ = ∫ψₙ*ψ dx as the components of a state along those axes. Use the supplied topic model Y_out = (100*r*cosd(theta-beta))/100, where Y_out is OVERLAP ∫ΦΨ dx = b₁c₁ + b₂c₂ (before display rounding) (dimensionless). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; sind/cosd/tand use degrees. theta is Direction of Ψ, θ, measured in °. Keep r (Length of Ψ, r) = 1; beta (Direction of Φ, β) = 0 ° fixed. For this exercise use only -70.875 ° ≤ theta ≤ -4.725 ° 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 theta = -56.322 ° and -47.061 °. Find the signed output change ΔY_out and the average sensitivity ΔY_out/Δtheta across this interval. State whether the response increases or decreases; distinguish this interval average from a derivative at one point.

Explore the question data

OVERLAP ∫ΦΨ dx = b₁c₁ + b₂c₂ (before display rounding) against theta

Predictions from the supplied What the solved systems were already telling us 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): OVERLAP ∫ΦΨ dx = b₁c₁ + b₂c₂ (before display rounding)

OVERLAP ∫ΦΨ dx = b₁c₁ + b₂c₂ (before display rounding) against thetaPredictions from the supplied What the solved systems were already telling us 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. Horizontal axis: theta: Direction of Ψ, θ / °. Vertical axis: OVERLAP ∫ΦΨ dx = b₁c₁ + b₂c₂ (before display rounding). Exact values are available in the data table below.0.30090.48150.66210.84271.023-73.52-55.66-37.8-19.94-2.079xy

Horizontal axis (x): theta: Direction of Ψ, θ / °

Axes show the supplied calibration interval; the origin may lie outside the plot.

Reading 1theta: Direction of Ψ, θ / °: -70.88OVERLAP ∫ΦΨ dx = b₁c₁ + b₂c₂ (before display rounding): 0.3276
Read the exact data as a table
OVERLAP ∫ΦΨ dx = b₁c₁ + b₂c₂ (before display rounding) against theta — supplied values
theta: Direction of Ψ, θ / °OVERLAP ∫ΦΨ dx = b₁c₁ + b₂c₂ (before display rounding)
-70.8750.32763017956169366
-54.33750.5830095789833509
-37.80.7901550123756904
-21.2624999999999960.9319287753895013
-4.7249999999999940.9966015395273318

These are the supplied values, not a worked answer. No fitted trend is drawn.

Show a hint

Substitute each value of theta separately while keeping every other input fixed, then subtract in the same order.

Reveal the worked solution
  1. At theta = -56.322 °, substitute into Y_out = (100*r*cosd(theta-beta))/100: Y₀ = 0.554525.
  2. At theta = -47.061 °, the same fixed-parameter model gives Y₁ = 0.681219.
  3. Signed change: ΔY_out = Y₁ − Y₀ = 0.126694; the input change is Δtheta = 9.261 °.
  4. Average sensitivity is ΔY_out/Δtheta = 0.0136804 1 per °. Its positive sign means that this response increases over the specified interval.
  5. 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 = 0.554525, Y₁ = 0.681219; ΔY_out = 0.126694; average sensitivity = 0.0136804 1 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.

Extra challenging · 01

What the solved systems were already telling us: infer an input with uncertainty

Reading a solved system as a vector space means treating its orthonormal eigenfunctions ψₙ as perpendicular unit axes, the overlap integral ∫φ*ψ dx as the dot product, and the coefficients cₙ = ∫ψₙ*ψ dx as the components of a state along those axes. Use the supplied topic model Y_out = (100*r*cosd(theta-beta))/100, where Y_out is OVERLAP ∫ΦΨ dx = b₁c₁ + b₂c₂ (before display rounding) (dimensionless). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; sind/cosd/tand use degrees. theta is Direction of Ψ, θ, measured in °. Keep r (Length of Ψ, r) = 1; beta (Direction of Φ, β) = 0 ° fixed. For this exercise use only -70.875 ° ≤ theta ≤ -4.725 ° 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 = 0.86142092, with a bounded reading uncertainty ±0.020277. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown theta by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.006615 °, 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.

Explore the question data

OVERLAP ∫ΦΨ dx = b₁c₁ + b₂c₂ (before display rounding) against theta

Predictions from the supplied What the solved systems were already telling us 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): OVERLAP ∫ΦΨ dx = b₁c₁ + b₂c₂ (before display rounding)

OVERLAP ∫ΦΨ dx = b₁c₁ + b₂c₂ (before display rounding) against thetaPredictions from the supplied What the solved systems were already telling us 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. Horizontal axis: theta: Direction of Ψ, θ / °. Vertical axis: OVERLAP ∫ΦΨ dx = b₁c₁ + b₂c₂ (before display rounding). Exact values are available in the data table below.0.30090.48150.66210.84271.023-73.52-55.66-37.8-19.94-2.079xy

Horizontal axis (x): theta: Direction of Ψ, θ / °

Axes show the supplied calibration interval; the origin may lie outside the plot.

Reading 1theta: Direction of Ψ, θ / °: -70.88OVERLAP ∫ΦΨ dx = b₁c₁ + b₂c₂ (before display rounding): 0.3276
Read the exact data as a table
OVERLAP ∫ΦΨ dx = b₁c₁ + b₂c₂ (before display rounding) against theta — supplied values
theta: Direction of Ψ, θ / °OVERLAP ∫ΦΨ dx = b₁c₁ + b₂c₂ (before display rounding)
-70.8750.32763017956169366
-54.33750.5830095789833509
-37.80.7901550123756904
-21.2624999999999960.9319287753895013
-4.7249999999999940.9966015395273318

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(theta) = 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
  1. Endpoint predictions are Y_out(-70.875) = 0.32763 and Y_out(-4.725) = 0.996602. The target lies between them. On the selected branch the response is strictly increasing.
  2. Endpoint interpolation gives theta_line = -70.875 + (0.861421 − 0.32763)(-4.725 − -70.875)/(0.996602 − 0.32763) = -18.0921 °. This estimates the inverse of the endpoint chord, rather than assuming that chord is the physical model.
  3. Use the actual equation, not the chord, and bracket its root. Evaluating the midpoint and retaining the sign-changing half-interval gives theta = -30.5235 °; substituting back gives Y_out = 0.86142092.
  4. The interpolation error in the input is |theta_line − theta| = 12.4314 °. This is larger than the requested numerical tolerance.
  5. Invert both limits Y_out = 0.84114392 and 0.88169792. Sort the roots even if the response decreases: -32.7389 ° ≤ theta ≤ -28.1521 °.
  6. The lower and upper input deviations are 2.21539 ° and 2.37137 °. 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 theta ≈ -30.5235 °; compatible interval [-32.7389, -28.1521] °. Endpoint interpolation gives -18.0921 °.

Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.