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University Quantum Mechanics I · topic UQMI-04.05

Commutators and the canonical relation [x-hat, p-hat] = i hbar

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

Commutators and the canonical relation [x̂, p̂] = iħ: predict a finite change

The canonical commutation relation says that x̂p̂ and p̂x̂, applied to any function they can act on, differ by exactly iħ times that function: [x̂, p̂] = iħ. Use the supplied topic model Y_out = q^(n-1)*(1-q)/(1-q^n), where Y_out is WEIGHT IN TOP LEVEL (dimensionless). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793. n is Matrix size N, dimensionless. Keep q (Weight ratio q between levels) = 0.5 fixed. For this exercise use only 2 ≤ n ≤ 8, at the specified test settings separated by 1. 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 n = 3 and 4. Find the signed output change ΔY_out and the average sensitivity ΔY_out/Δn across this interval. State whether the response increases or decreases; explain why a finite difference between these settings does not require interpolation between them.

Explore the question data

WEIGHT IN TOP LEVEL against n

Predictions from the supplied Commutators and the canonical relation [x̂, p̂] = iħ 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): WEIGHT IN TOP LEVEL

WEIGHT IN TOP LEVEL against nPredictions from the supplied Commutators and the canonical relation [x̂, p̂] = iħ 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: n: Matrix size N. Vertical axis: WEIGHT IN TOP LEVEL. Exact values are available in the data table below.-0.0092550.079690.16860.25760.34651.763.3856.628.24xy

Horizontal axis (x): n: Matrix size N

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

Reading 1n: Matrix size N: 2WEIGHT IN TOP LEVEL: 0.3333
Read the exact data as a table
WEIGHT IN TOP LEVEL against n — supplied values
n: Matrix size NWEIGHT IN TOP LEVEL
20.3333333333333333
40.06666666666666667
50.03225806451612903
70.007874015748031496
80.00392156862745098

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

Show a hint

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

Reveal the worked solution
  1. At n = 3, substitute into Y_out = q^(n-1)*(1-q)/(1-q^n): Y₀ = 0.142857.
  2. At n = 4, the same fixed-parameter model gives Y₁ = 0.0666667.
  3. Signed change: ΔY_out = Y₁ − Y₀ = -0.0761905; the input change is Δn = 1.
  4. Average sensitivity is ΔY_out/Δn = -0.0761905 1 per unit input. Its negative sign means that this response decreases over the specified interval.
  5. This finite difference compares two specified test settings without interpolating between them. A point derivative is not required. Any quantum numbers or particle counts must also obey their physical admissibility conditions.

Answer Y₀ = 0.142857, Y₁ = 0.0666667; ΔY_out = -0.0761905; average sensitivity = -0.0761905 1 per unit input.

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

Commutators and the canonical relation [x̂, p̂] = iħ: infer an input with uncertainty

The canonical commutation relation says that x̂p̂ and p̂x̂, applied to any function they can act on, differ by exactly iħ times that function: [x̂, p̂] = iħ. Use the supplied topic model Y_out = q^(n-1)*(1-q)/(1-q^n), where Y_out is WEIGHT IN TOP LEVEL (dimensionless). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793. n is Matrix size N, dimensionless. Keep q (Weight ratio q between levels) = 0.5 fixed. For this exercise use only 2 ≤ n ≤ 8, at the specified test settings separated by 1. 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 = 0.015873016, with a bounded reading uncertainty ±0.026353. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. Determine every allowed value of n consistent with that bound. Compare with a straight-line interpolation between the two endpoint predictions, and explain why a fractional setting may be inadmissible. Do not assume that the supplied calibration points prove monotonicity outside this branch.

Explore the question data

WEIGHT IN TOP LEVEL against n

Predictions from the supplied Commutators and the canonical relation [x̂, p̂] = iħ 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): WEIGHT IN TOP LEVEL

WEIGHT IN TOP LEVEL against nPredictions from the supplied Commutators and the canonical relation [x̂, p̂] = iħ 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: n: Matrix size N. Vertical axis: WEIGHT IN TOP LEVEL. Exact values are available in the data table below.-0.0092550.079690.16860.25760.34651.763.3856.628.24xy

Horizontal axis (x): n: Matrix size N

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

Reading 1n: Matrix size N: 2WEIGHT IN TOP LEVEL: 0.3333
Read the exact data as a table
WEIGHT IN TOP LEVEL against n — supplied values
n: Matrix size NWEIGHT IN TOP LEVEL
20.3333333333333333
40.06666666666666667
50.03225806451612903
70.007874015748031496
80.00392156862745098

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

Show a hint

Enumerate the permitted settings in the stated range, evaluate Y_out at each, and retain every setting satisfying |Y_out − Y_measured| ≤ u. The plotted predictions are supplied calibration evidence.

Reveal the worked solution
  1. Endpoint predictions are Y_out(2) = 0.333333 and Y_out(8) = 0.00392157. The target lies between them. On the selected branch the response is strictly decreasing.
  2. Endpoint interpolation gives n_line = 2 + (0.015873 − 0.333333)(8 − 2)/(0.00392157 − 0.333333) = 7.78231. This estimates the inverse of the endpoint chord, rather than assuming that chord is the physical model.
  3. Evaluate the model at each permitted setting. The central reading corresponds to n = 6; only the allowed settings 5, 6, 7, 8 lie inside the stated reading bound.
  4. The interpolation error in the input is |n_line − n| = 1.78231.
  5. The compatible allowed values span 5 to 8. Values between specified test settings are outside this exercise's input domain even if an interpolation passes through them.
  6. The quoted output bound restricts which specified settings remain compatible. It does not change physical restrictions on quantum numbers or counts, and the chosen grid alone does not establish physical quantisation.

Answer Compatible n: 5, 6, 7, 8. Endpoint interpolation predicts 7.78231; check admissibility against the discrete settings.

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