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University Quantum Mechanics II · unit UQMII-10

The Variational Principle

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 variational theorem: ⟨H⟩ is never below E₀: infer an input with uncertainty

The variational theorem says that for any admissible trial state ψ the Rayleigh quotient ⟨ψ|H|ψ⟩/⟨ψ|ψ⟩ is at least the ground-state energy E₀, with equality only when ψ is itself a ground state. Use the supplied topic model Y_out = (cosd(th) + sin(n*pi/2)*sind(th))^2, where Y_out is CENTRE DENSITY ÷ EXACT (dimensionless). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; sind/cosd/tand use degrees; unmarked trigonometric functions use radians. th is Mixing angle θ, measured in °. Keep n (Admixed level n (odd, same symmetry)) = 3 fixed. For this exercise use only 47.3625 ° ≤ th ≤ 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 = 0.29383796, with a bounded reading uncertainty ±0.043566. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown th by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.0033075 °, 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

CENTRE DENSITY ÷ EXACT against th

Predictions from the supplied The variational theorem: ⟨H⟩ is never below E₀ 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): CENTRE DENSITY ÷ EXACT

CENTRE DENSITY ÷ EXACT against thPredictions from the supplied The variational theorem: ⟨H⟩ is never below E₀ 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: th: Mixing angle θ / °. Vertical axis: CENTRE DENSITY ÷ EXACT. Exact values are available in the data table below.-0.023360.15730.33790.51850.699146.0454.9763.972.8381.76xy

Horizontal axis (x): th: Mixing angle θ / °

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

Reading 1th: Mixing angle θ / °: 47.36CENTRE DENSITY ÷ EXACT: 0.003398
Read the exact data as a table
CENTRE DENSITY ÷ EXACT against th — supplied values
th: Mixing angle θ / °CENTRE DENSITY ÷ EXACT
47.36250.00339846047266826
55.6312499999999940.06807122461049873
63.90.20984498762430945
72.168750.41699042101664907
80.43750.6723698204383063

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(th) = 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(47.3625) = 0.00339846 and Y_out(80.4375) = 0.67237. The target lies between them. On the selected branch the response is strictly increasing.
  2. Endpoint interpolation gives th_line = 47.3625 + (0.293838 − 0.00339846)(80.4375 − 47.3625)/(0.67237 − 0.00339846) = 61.7223 °. 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 th = 67.5382 °; substituting back gives Y_out = 0.29383796.
  4. The interpolation error in the input is |th_line − th| = 5.81596 °. This is larger than the requested numerical tolerance.
  5. Invert both limits Y_out = 0.25027196 and 0.33740396. Sort the roots even if the response decreases: 65.7166 ° ≤ th ≤ 69.2509 °.
  6. The lower and upper input deviations are 1.82166 ° and 1.71267 °. 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 th ≈ 67.5382 °; compatible interval [65.7166, 69.2509] °. Endpoint interpolation gives 61.7223 °.

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

Extra challenging · 02

One-parameter Gaussian trials tested against exact answers: infer an input with uncertainty

A one-parameter Gaussian trial is the normalised function (2b/π)^(1/4) e^(−bx²) whose width parameter b is varied to minimise ⟨H⟩; the minimum is an upper bound on the ground-state energy, and comparing it with an exact answer measures how well the Gaussian shape fits. Use the supplied topic model Y_out = 100*(b/2+1/(8*b)-0.5)/0.5, where Y_out is ½x²: E ABOVE EXACT (% of |E₀|). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793. b is Gaussian width parameter b, dimensionless. Keep no other adjustable input fixed. For this exercise use only 0.261562 ≤ b ≤ 0.482062 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 = 2.7273039 % of |E₀|, with a bounded reading uncertainty ±0.39908 % of |E₀|. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown b by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.00002205, 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

½x²: E ABOVE EXACT against b

Predictions from the supplied One-parameter Gaussian trials tested against exact answers 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): ½x²: E ABOVE EXACT / % of |E₀|

½x²: E ABOVE EXACT against bPredictions from the supplied One-parameter Gaussian trials tested against exact answers 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: b: Gaussian width parameter b. Vertical axis: ½x²: E ABOVE EXACT / % of |E₀|. Exact values are available in the data table below.-0.85.05110.916.7522.60.25270.31230.37180.43130.4909xy

Horizontal axis (x): b: Gaussian width parameter b

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

Reading 1b: Gaussian width parameter b: 0.2616½x²: E ABOVE EXACT / % of |E₀|: 21.74
Read the exact data as a table
½x²: E ABOVE EXACT against b — supplied values
b: Gaussian width parameter b½x²: E ABOVE EXACT / % of |E₀|
0.261562521.735700418160107
0.3166874999999999610.610924856917325
0.37181254.419441292654236
0.426937499999999971.2503302957107332
0.482062499999999950.06674526772980638

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(b) = 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(0.261562) = 21.7357 % of |E₀| and Y_out(0.482062) = 0.0667453 % of |E₀|. The target lies between them. On the selected branch the response is strictly decreasing.
  2. Endpoint interpolation gives b_line = 0.261562 + (2.7273 − 21.7357)(0.482062 − 0.261562)/(0.0667453 − 21.7357) = 0.454989. 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 b = 0.396068; substituting back gives Y_out = 2.7273039 % of |E₀|.
  4. The interpolation error in the input is |b_line − b| = 0.0589216. This is larger than the requested numerical tolerance.
  5. Invert both limits Y_out = 2.3282239 % of |E₀| and 3.1263839 % of |E₀|. Sort the roots even if the response decreases: 0.389631 ≤ b ≤ 0.403121.
  6. The lower and upper input deviations are 0.00643668 and 0.00705341. 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 b ≈ 0.396068; compatible interval [0.389631, 0.403121]. Endpoint interpolation gives 0.454989.

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

Extra challenging · 03

Bounds on excited states from orthogonality and symmetry: infer an input with uncertainty

If a trial state has exactly zero overlap with the true ground state, its ⟨H⟩ is an upper bound on the first excited energy E₁; a symmetry of H is the usual way to guarantee that zero without knowing the ground state. Use the supplied topic model Y_out = ((nu-1)*(1.5*beta+0.375/beta)+(2-nu)*(3*beta+0.797885/sqrt(beta))), where Y_out is ODD TRIAL ⟨H⟩ (ε). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; sqrt(x) means √x. beta is Width parameter β = bℓ², dimensionless. Keep w (Even-trial probability w) = 0; nu (Power ν: 1 for F|x|, 2 for ½mω²x²) = 1 fixed. For this exercise use only 0.3035 ≤ beta ≤ 1.08312 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 = 3.2411789 ε, with a bounded reading uncertainty ±0.11623 ε. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown beta by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.0000779625, 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

ODD TRIAL ⟨H⟩ against beta

Predictions from the supplied Bounds on excited states from orthogonality and symmetry 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): ODD TRIAL ⟨H⟩ / ε

ODD TRIAL ⟨H⟩ against betaPredictions from the supplied Bounds on excited states from orthogonality and symmetry 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: beta: Width parameter β = bℓ². Vertical axis: ODD TRIAL ⟨H⟩ / ε. Exact values are available in the data table below.2.2932.743.1873.6354.0820.27230.48280.69330.90381.114xy

Horizontal axis (x): beta: Width parameter β = bℓ²

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

Reading 1beta: Width parameter β = bℓ²: 0.3035ODD TRIAL ⟨H⟩ / ε: 2.359
Read the exact data as a table
ODD TRIAL ⟨H⟩ against beta — supplied values
beta: Width parameter β = bℓ²ODD TRIAL ⟨H⟩ / ε
0.30352.358808077515635
0.498406249999999942.6254012041463057
0.69331253.0381807867829353
0.888218753.5112602813670195
1.0831254.016031832540188

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(beta) = 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(0.3035) = 2.35881 ε and Y_out(1.08312) = 4.01603 ε. The target lies between them. On the selected branch the response is strictly increasing.
  2. Endpoint interpolation gives beta_line = 0.3035 + (3.24118 − 2.35881)(1.08312 − 0.3035)/(4.01603 − 2.35881) = 0.718603. 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 beta = 0.779071; substituting back gives Y_out = 3.2411789 ε.
  4. The interpolation error in the input is |beta_line − beta| = 0.0604684. This is larger than the requested numerical tolerance.
  5. Invert both limits Y_out = 3.1249489 ε and 3.3574089 ε. Sort the roots even if the response decreases: 0.730464 ≤ beta ≤ 0.826607.
  6. The lower and upper input deviations are 0.0486073 and 0.0475357. 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 beta ≈ 0.779071; compatible interval [0.730464, 0.826607]. Endpoint interpolation gives 0.718603.

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

Extra challenging · 04

Helium's ground state: the repulsion ignored, then averaged: infer an input with uncertainty

First-order helium replaces the electron–electron repulsion by its average over the state in which both electrons occupy the unscreened hydrogenic 1s orbital of the full nuclear charge, giving E ≈ E⁽⁰⁾ + ⟨V_ee⟩ = −(Z² − 5Z/8) e²/(4πε₀a₀). Use the supplied topic model Y_out = -27.211*z^2 + 17.007*z, where Y_out is E⁽⁰⁾ + E⁽¹⁾, UPPER BOUND (eV). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793. z is Nuclear charge Z, dimensionless. Keep r1 (Radius of electron 1, r₁) = 0.5 a₀ fixed. For this exercise use only 1 ≤ z ≤ 3, 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 = -74.83 eV, with a bounded reading uncertainty ±14.694 eV. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. Determine every allowed value of z 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

E⁽⁰⁾ + E⁽¹⁾, UPPER BOUND against z

Predictions from the supplied Helium's ground state: the repulsion ignored, then averaged 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): E⁽⁰⁾ + E⁽¹⁾, UPPER BOUND / eV

E⁽⁰⁾ + E⁽¹⁾, UPPER BOUND against zPredictions from the supplied Helium's ground state: the repulsion ignored, then averaged 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: z: Nuclear charge Z. Vertical axis: E⁽⁰⁾ + E⁽¹⁾, UPPER BOUND / eV. Exact values are available in the data table below.-201.2-151.6-102-52.45-2.8570.921.4622.543.08xy

Horizontal axis (x): z: Nuclear charge Z

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

Reading 1z: Nuclear charge Z: 1E⁽⁰⁾ + E⁽¹⁾, UPPER BOUND / eV: -10.2
Read the exact data as a table
E⁽⁰⁾ + E⁽¹⁾, UPPER BOUND against z — supplied values
z: Nuclear charge ZE⁽⁰⁾ + E⁽¹⁾, UPPER BOUND / eV
1-10.203999999999997
2-74.82999999999998
3-193.878

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(1) = -10.204 eV and Y_out(3) = -193.878 eV. The target lies between them. On the selected branch the response is strictly decreasing.
  2. Endpoint interpolation gives z_line = 1 + (-74.83 − -10.204)(3 − 1)/(-193.878 − -10.204) = 1.7037. 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 z = 2; only the allowed settings 2 lie inside the stated reading bound.
  4. The interpolation error in the input is |z_line − z| = 0.296297.
  5. The compatible allowed values span 2 to 2. 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 z: 2. Endpoint interpolation predicts 1.7037; check admissibility against the discrete settings.

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

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