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University Physics V · topic UPV-07.05

The Fock basis and operators as matrices

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

The Fock basis and operators as matrices: predict a finite change

The Fock basis is the orthonormal set of number eigenvectors |0⟩, |1⟩, |2⟩, … generated by applying a† to the vacuum, in which every oscillator operator is stored as an array of matrix elements ⟨m|O|n⟩ rather than as a differential operator. Use the supplied topic model Y_out = clamp(d-n,0,1)*(n+(n+1)*clamp(d-1-n,0,1)), where Y_out is N×N MATRIX PRODUCT (x_zpf²). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; clamp(x,a,b) confines x to [a,b]. n is Level read off n, dimensionless. Keep d (Fock states kept N) = 4 states fixed. For this exercise use only 2 ≤ n ≤ 4, 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 = 2 and 3. 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

N×N MATRIX PRODUCT against n

Predictions from the supplied The Fock basis and operators as matrices model, with all other inputs fixed as stated in the question. 3 calibration points support a trend comparison; they are not measured data or a straight-line law.

Vertical axis (y): N×N MATRIX PRODUCT / x_zpf²

N×N MATRIX PRODUCT against nPredictions from the supplied The Fock basis and operators as matrices model, with all other inputs fixed as stated in the question. 3 calibration points support a trend comparison; they are not measured data or a straight-line law. Horizontal axis: n: Level read off n. Vertical axis: N×N MATRIX PRODUCT / x_zpf². Exact values are available in the data table below.-0.21.152.53.855.21.922.4633.544.08xy

Horizontal axis (x): n: Level read off n

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

Reading 1n: Level read off n: 2N×N MATRIX PRODUCT / x_zpf²: 5
Read the exact data as a table
N×N MATRIX PRODUCT against n — supplied values
n: Level read off nN×N MATRIX PRODUCT / x_zpf²
25
33
40

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 = 2, substitute into Y_out = clamp(d-n,0,1)*(n+(n+1)*clamp(d-1-n,0,1)): Y₀ = 5 x_zpf².
  2. At n = 3, the same fixed-parameter model gives Y₁ = 3 x_zpf².
  3. Signed change: ΔY_out = Y₁ − Y₀ = -2 x_zpf²; the input change is Δn = 1.
  4. Average sensitivity is ΔY_out/Δn = -2 x_zpf² 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 = 5 x_zpf², Y₁ = 3 x_zpf²; ΔY_out = -2 x_zpf²; average sensitivity = -2 x_zpf² 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

The Fock basis and operators as matrices: infer an input with uncertainty

The Fock basis is the orthonormal set of number eigenvectors |0⟩, |1⟩, |2⟩, … generated by applying a† to the vacuum, in which every oscillator operator is stored as an array of matrix elements ⟨m|O|n⟩ rather than as a differential operator. Use the supplied topic model Y_out = clamp(d-n,0,1)*(n+(n+1)*clamp(d-1-n,0,1)), where Y_out is N×N MATRIX PRODUCT (x_zpf²). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; clamp(x,a,b) confines x to [a,b]. n is Level read off n, dimensionless. Keep d (Fock states kept N) = 4 states fixed. For this exercise use only 2 ≤ n ≤ 4, 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 = 3 x_zpf², with a bounded reading uncertainty ±0.4 x_zpf². 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

N×N MATRIX PRODUCT against n

Predictions from the supplied The Fock basis and operators as matrices model, with all other inputs fixed as stated in the question. 3 calibration points support a trend comparison; they are not measured data or a straight-line law.

Vertical axis (y): N×N MATRIX PRODUCT / x_zpf²

N×N MATRIX PRODUCT against nPredictions from the supplied The Fock basis and operators as matrices model, with all other inputs fixed as stated in the question. 3 calibration points support a trend comparison; they are not measured data or a straight-line law. Horizontal axis: n: Level read off n. Vertical axis: N×N MATRIX PRODUCT / x_zpf². Exact values are available in the data table below.-0.21.152.53.855.21.922.4633.544.08xy

Horizontal axis (x): n: Level read off n

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

Reading 1n: Level read off n: 2N×N MATRIX PRODUCT / x_zpf²: 5
Read the exact data as a table
N×N MATRIX PRODUCT against n — supplied values
n: Level read off nN×N MATRIX PRODUCT / x_zpf²
25
33
40

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) = 5 x_zpf² and Y_out(4) = 0 x_zpf². The target lies between them. On the selected branch the response is strictly decreasing.
  2. Endpoint interpolation gives n_line = 2 + (3 − 5)(4 − 2)/(0 − 5) = 2.8. 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 = 3; only the allowed settings 3 lie inside the stated reading bound.
  4. The interpolation error in the input is |n_line − n| = 0.2.
  5. The compatible allowed values span 3 to 3. 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: 3. Endpoint interpolation predicts 2.8; check admissibility against the discrete settings.

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