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University Physics V · unit UPV-07

The Quantum Harmonic Oscillator

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.

Extra challenging questions

A selection from this pathway. Open a topic below for its complete practice and challenge set.

Extra challenging · 01

The oscillator Hamiltonian and its quadratures: infer an input with uncertainty

The quantum harmonic oscillator is the Hamiltonian p̂²/2m + ½mω²x̂² — the leading surviving term when any potential is expanded about a stable minimum — which in its own natural units becomes ½ħω(X² + P²) with [X, P] = i. Use the supplied topic model Y_out = 1/(2*s*s), where Y_out is WELL DEPTH D (ħω). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793. s is Anharmonicity a⋅x₀, dimensionless. Keep eps (Level energy E) = 2.5 ħω fixed. For this exercise use only 0.060375 ≤ s ≤ 0.359625 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 = 8.4732964 ħω, with a bounded reading uncertainty ±0.69108 ħω. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown s by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.000029925, 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

WELL DEPTH D against s

Predictions from the supplied The oscillator Hamiltonian and its quadratures 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): WELL DEPTH D / ħω

WELL DEPTH D against sPredictions from the supplied The oscillator Hamiltonian and its quadratures 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: s: Anharmonicity a⋅x₀. Vertical axis: WELL DEPTH D / ħω. Exact values are available in the data table below.-1.46634.5370.52106.5142.50.048410.12920.210.29080.3716xy

Horizontal axis (x): s: Anharmonicity a⋅x₀

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

Reading 1s: Anharmonicity a⋅x₀: 0.06037WELL DEPTH D / ħω: 137.2
Read the exact data as a table
WELL DEPTH D against s — supplied values
s: Anharmonicity a⋅x₀WELL DEPTH D / ħω
0.060375137.16891923751228
0.135187527.35879282746498
0.2099999999999999611.337868480725627
0.284812499999999976.163847886057806
0.3596253.866074818937823

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(s) = 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(0.060375) = 137.169 ħω and Y_out(0.359625) = 3.86607 ħω. The target lies between them. On the selected branch the response is strictly decreasing.
  2. Endpoint interpolation gives s_line = 0.060375 + (8.4733 − 137.169)(0.359625 − 0.060375)/(3.86607 − 137.169) = 0.349282. 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 s = 0.242918; substituting back gives Y_out = 8.4732964 ħω.
  4. The interpolation error in the input is |s_line − s| = 0.106365. This is larger than the requested numerical tolerance.
  5. Invert both limits Y_out = 7.7822164 ħω and 9.1643764 ħω. Sort the roots even if the response decreases: 0.233579 ≤ s ≤ 0.253474.
  6. The lower and upper input deviations are 0.00933864 and 0.0105565. 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 s ≈ 0.242918; compatible interval [0.233579, 0.253474]. Endpoint interpolation gives 0.349282.

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

Extra challenging · 02

Annihilation and creation operators: infer an input with uncertainty

The annihilation operator a and the creation operator a† are the non-Hermitian combinations a = (X + iP)/√2 and a† = (X − iP)/√2 of the dimensionless quadratures, chosen so that [a, a†] = 1 and Ĥ = ℏω(a†a + ½). Use the supplied topic model Y_out = n, where Y_out is ‖a|n⟩‖² = ⟨a†a⟩ (dimensionless). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793. n is Rung n on the ladder, dimensionless. Keep k (Rungs kept above n) = 2 fixed. For this exercise use only 0 ≤ n ≤ 6, 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 = 4, with a bounded reading uncertainty ±0.48. 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

‖a|n⟩‖² = ⟨a†a⟩ against n

Predictions from the supplied Annihilation and creation operators 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): ‖a|n⟩‖² = ⟨a†a⟩

‖a|n⟩‖² = ⟨a†a⟩ against nPredictions from the supplied Annihilation and creation operators 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: Rung n on the ladder. Vertical axis: ‖a|n⟩‖² = ⟨a†a⟩. Exact values are available in the data table below.-0.241.3834.626.24-0.241.3834.626.24xy

Horizontal axis (x): n: Rung n on the ladder

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

Reading 1n: Rung n on the ladder: 0‖a|n⟩‖² = ⟨a†a⟩: 0
Read the exact data as a table
‖a|n⟩‖² = ⟨a†a⟩ against n — supplied values
n: Rung n on the ladder‖a|n⟩‖² = ⟨a†a⟩
00
22
33
55
66

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(0) = 0 and Y_out(6) = 6. The target lies between them. On the selected branch the response is strictly increasing.
  2. Endpoint interpolation gives n_line = 0 + (4 − 0)(6 − 0)/(6 − 0) = 4. 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 = 4; only the allowed settings 4 lie inside the stated reading bound.
  4. The interpolation error in the input is |n_line − n| = 0.
  5. The compatible allowed values span 4 to 4. 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: 4. Endpoint interpolation predicts 4; check admissibility against the discrete settings.

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

Extra challenging · 03

The number operator and the ladder's floor: infer an input with uncertainty

The number operator N = a†a is the Hermitian, positive operator whose eigenvalue n counts the quanta in a mode; H = ħω(N + ½) then makes energy a matter of counting rungs rather than solving a differential equation. Use the supplied topic model Y_out = v, where Y_out is TRIAL ν (dimensionless). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793. v is Trial eigenvalue ν of N, dimensionless. Keep s (Lowering steps k applied) = 4 fixed. For this exercise use only 0.371875 ≤ v ≤ 3.12812 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 = 2.0531875, with a bounded reading uncertainty ±0.16124. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown v by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.000275625, 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

TRIAL ν against v

Predictions from the supplied The number operator and the ladder's floor 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): TRIAL ν

TRIAL ν against vPredictions from the supplied The number operator and the ladder's floor 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: v: Trial eigenvalue ν of N. Vertical axis: TRIAL ν. Exact values are available in the data table below.0.26161.0061.752.4943.2380.26161.0061.752.4943.238xy

Horizontal axis (x): v: Trial eigenvalue ν of N

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

Reading 1v: Trial eigenvalue ν of N: 0.3719TRIAL ν: 0.3719
Read the exact data as a table
TRIAL ν against v — supplied values
v: Trial eigenvalue ν of NTRIAL ν
0.3718750.371875
1.06093749999999991.0609374999999999
1.74999999999999981.7499999999999998
2.43906252.4390625
3.1281253.128125

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(v) = 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(0.371875) = 0.371875 and Y_out(3.12812) = 3.12812. The target lies between them. On the selected branch the response is strictly increasing.
  2. Endpoint interpolation gives v_line = 0.371875 + (2.05319 − 0.371875)(3.12812 − 0.371875)/(3.12812 − 0.371875) = 2.05319. 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 v = 2.05319; substituting back gives Y_out = 2.0531875.
  4. The interpolation error in the input is |v_line − v| = 0. This is within the requested numerical tolerance.
  5. Invert both limits Y_out = 1.8919475 and 2.2144275. Sort the roots even if the response decreases: 1.89195 ≤ v ≤ 2.21443.
  6. The lower and upper input deviations are 0.16124 and 0.16124. They agree on this branch; this does not establish linearity for changes in other inputs or outside the interval. This interval includes reading uncertainty only, not model error or uncertainty in fixed inputs.

Answer v ≈ 2.05319; compatible interval [1.89195, 2.21443]. Endpoint interpolation gives 2.05319.

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

Extra challenging · 04

Eₙ = (n + ½)ℏω and zero-point energy: infer an input with uncertainty

The oscillator's energy eigenvalues are Eₙ = (n + ½)ℏω: an infinite ladder of equally spaced rungs whose lowest, the zero-point energy ℏω/2, lies above the bottom of the well and cannot be lowered at fixed ω. Use the supplied topic model Y_out = 2913.1/sqrt(max(u,0.2)), where Y_out is WAVENUMBER ω̃ (cm⁻¹). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; sqrt(x) means √x; max(a,b) selects the larger value. u is Reduced mass μ, measured in u. Keep n (Lower rung n of the marked gap) = 0 fixed. For this exercise use only 1.03812 u ≤ u ≤ 2.06188 u 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 = 2259.227 cm⁻¹, with a bounded reading uncertainty ±34.575 cm⁻¹. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown u by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.000102375 u, 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

WAVENUMBER ω̃ against u

Predictions from the supplied Eₙ = (n + ½)ℏω and zero-point energy 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): WAVENUMBER ω̃ / cm⁻¹

WAVENUMBER ω̃ against uPredictions from the supplied Eₙ = (n + ½)ℏω and zero-point energy 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: u: Reduced mass μ / u. Vertical axis: WAVENUMBER ω̃ / cm⁻¹. Exact values are available in the data table below.199622202444266828920.99721.2741.551.8262.103xy

Horizontal axis (x): u: Reduced mass μ / u

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

Reading 1u: Reduced mass μ / u: 1.038WAVENUMBER ω̃ / cm⁻¹: 2859
Read the exact data as a table
WAVENUMBER ω̃ against u — supplied values
u: Reduced mass μ / uWAVENUMBER ω̃ / cm⁻¹
1.0381252859.108050015188
1.29406252560.8124178775106
1.552339.8582270258694
1.80593750000000022167.7242455889873
2.0618752028.7298848642058

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(u) = 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(1.03812) = 2859.11 cm⁻¹ and Y_out(2.06188) = 2028.73 cm⁻¹. The target lies between them. On the selected branch the response is strictly decreasing.
  2. Endpoint interpolation gives u_line = 1.03812 + (2259.23 − 2859.11)(2.06188 − 1.03812)/(2028.73 − 2859.11) = 1.7777 u. 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 u = 1.66261 u; substituting back gives Y_out = 2259.227 cm⁻¹.
  4. The interpolation error in the input is |u_line − u| = 0.115089 u. This is larger than the requested numerical tolerance.
  5. Invert both limits Y_out = 2224.652 cm⁻¹ and 2293.802 cm⁻¹. Sort the roots even if the response decreases: 1.61287 u ≤ u ≤ 1.71469 u.
  6. The lower and upper input deviations are 0.0497441 u and 0.0520814 u. 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 u ≈ 1.66261 u; compatible interval [1.61287, 1.71469] u. Endpoint interpolation gives 1.7777 u.

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

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