University Physics IV · unit UPIV-15
Frontiers of Modern Physics
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.
Two-state systems and the Bloch sphere: infer an input with uncertainty
A qubit is a quantum system whose accessible states span a two-dimensional Hilbert space, so every pure state is a normalised superposition a|0⟩ + b|1⟩ carrying exactly two physically meaningful real parameters. Use the supplied topic model Y_out = 0.5*(1+p*p), where Y_out is PURITY Tr ρ² (dimensionless). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793. p is Bloch length |r|, dimensionless. Keep th (State polar angle θ) = 60 °; al (Analyser angle β from z) = 90 ° fixed. For this exercise use only 0.10625 ≤ p ≤ 0.89375 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 = 0.67206445, with a bounded reading uncertainty ±0.024963. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown p by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.00007875, 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.
PURITY Tr ρ² against p
Predictions from the supplied Two-state systems and the Bloch sphere 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): PURITY Tr ρ²
Horizontal axis (x): p: Bloch length |r|
Axes show the supplied calibration interval; the origin may lie outside the plot.
Read the exact data as a table
| p: Bloch length |r| | PURITY Tr ρ² |
|---|---|
| 0.10625 | 0.50564453125 |
| 0.303125 | 0.5459423828125 |
| 0.5 | 0.625 |
| 0.6968749999999999 | 0.7428173828125 |
| 0.8937499999999999 | 0.89939453125 |
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(p) = 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
- Endpoint predictions are Y_out(0.10625) = 0.505645 and Y_out(0.89375) = 0.899395. The target lies between them. On the selected branch the response is strictly increasing.
- Endpoint interpolation gives p_line = 0.10625 + (0.672064 − 0.505645)(0.89375 − 0.10625)/(0.899395 − 0.505645) = 0.43909. This estimates the inverse of the endpoint chord, rather than assuming that chord is the physical model.
- Use the actual equation, not the chord, and bracket its root. Evaluating the midpoint and retaining the sign-changing half-interval gives p = 0.586625; substituting back gives Y_out = 0.67206445.
- The interpolation error in the input is |p_line − p| = 0.147535. This is larger than the requested numerical tolerance.
- Invert both limits Y_out = 0.64710145 and 0.69702745. Sort the roots even if the response decreases: 0.542405 ≤ p ≤ 0.627738.
- The lower and upper input deviations are 0.0442203 and 0.0411129. 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 p ≈ 0.586625; compatible interval [0.542405, 0.627738]. Endpoint interpolation gives 0.43909.
Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.
Entanglement, CHSH and Bell tests: infer an input with uncertainty
Two particles are entangled when their joint state cannot be written as one state for each particle separately, so the pair carries definite properties that neither member on its own possesses. Use the supplied topic model Y_out = -v*cosd(t), where Y_out is CORRELATION E AT θ (dimensionless). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; sind/cosd/tand use degrees. t is Analyser step θ between settings, measured in °. Keep v (Source visibility V) = 1 fixed. For this exercise use only 10.8438 ° ≤ t ≤ 54.1562 ° 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 = -0.79585011, with a bounded reading uncertainty ±0.027944. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown t by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.00433125 °, 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.
CORRELATION E AT θ against t
Predictions from the supplied Entanglement, CHSH and Bell tests 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): CORRELATION E AT θ
Horizontal axis (x): t: Analyser step θ between settings / °
Axes show the supplied calibration interval; the origin may lie outside the plot.
Read the exact data as a table
| t: Analyser step θ between settings / ° | CORRELATION E AT θ |
|---|---|
| 10.84375 | -0.9821438834637756 |
| 21.671875 | -0.9293139586012596 |
| 32.5 | -0.8433914458128857 |
| 43.32812499999999 | -0.7274360196929529 |
| 54.15624999999999 | -0.585576817761498 |
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(t) = 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
- Endpoint predictions are Y_out(10.8438) = -0.982144 and Y_out(54.1562) = -0.585577. The target lies between them. On the selected branch the response is strictly increasing.
- Endpoint interpolation gives t_line = 10.8438 + (-0.79585 − -0.982144)(54.1562 − 10.8438)/(-0.585577 − -0.982144) = 31.1905 °. This estimates the inverse of the endpoint chord, rather than assuming that chord is the physical model.
- Use the actual equation, not the chord, and bracket its root. Evaluating the midpoint and retaining the sign-changing half-interval gives t = 37.2644 °; substituting back gives Y_out = -0.79585011.
- The interpolation error in the input is |t_line − t| = 6.07388 °. This is larger than the requested numerical tolerance.
- Invert both limits Y_out = -0.82379411 and -0.76790611. Sort the roots even if the response decreases: 34.5336 ° ≤ t ≤ 39.8338 °.
- The lower and upper input deviations are 2.7308 ° and 2.56939 °. 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 t ≈ 37.2644 °; compatible interval [34.5336, 39.8338] °. Endpoint interpolation gives 31.1905 °.
Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.
Gates, no-cloning, algorithms and decoherence: infer an input with uncertainty
A quantum gate is a unitary matrix acting on the 2ⁿ amplitudes of an n-qubit register, so every step of a computation is reversible and norm-preserving until a measurement is made. Use the supplied topic model Y_out = min(k/n,1), where Y_out is CLASSICAL P AT k (dimensionless). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; min(a,b) selects the smaller value. n is Search space size N, measured in items. Keep k (Oracle queries k) = 6 queries; eps (Error probability per query ε) = 0 fixed. For this exercise use only 30.775 items ≤ n ≤ 229.225 items 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 = 0.039518012, with a bounded reading uncertainty ±0.0020014. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown n by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.019845 items, 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.
CLASSICAL P AT k against n
Predictions from the supplied Gates, no-cloning, algorithms and decoherence 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): CLASSICAL P AT k
Horizontal axis (x): n: Search space size N / items
Axes show the supplied calibration interval; the origin may lie outside the plot.
Read the exact data as a table
| n: Search space size N / items | CLASSICAL P AT k |
|---|---|
| 30.775 | 0.1949634443541836 |
| 80.38749999999999 | 0.07463846991136683 |
| 130 | 0.046153846153846156 |
| 179.61249999999998 | 0.033405247407613615 |
| 229.225 | 0.026175155414985277 |
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(n) = 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
- Endpoint predictions are Y_out(30.775) = 0.194963 and Y_out(229.225) = 0.0261752. The target lies between them. On the selected branch the response is strictly decreasing.
- Endpoint interpolation gives n_line = 30.775 + (0.039518 − 0.194963)(229.225 − 30.775)/(0.0261752 − 0.194963) = 213.537 items. This estimates the inverse of the endpoint chord, rather than assuming that chord is the physical model.
- Use the actual equation, not the chord, and bracket its root. Evaluating the midpoint and retaining the sign-changing half-interval gives n = 151.829 items; substituting back gives Y_out = 0.039518012.
- The interpolation error in the input is |n_line − n| = 61.7079 items. This is larger than the requested numerical tolerance.
- Invert both limits Y_out = 0.037516612 and 0.041519412. Sort the roots even if the response decreases: 144.511 items ≤ n ≤ 159.929 items.
- The lower and upper input deviations are 7.31878 items and 8.09965 items. 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 n ≈ 151.829 items; compatible interval [144.511, 159.929] items. Endpoint interpolation gives 213.537 items.
Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.
Zero resistance, Meissner and critical fields: infer an input with uncertainty
A superconductor is a material that below its critical temperature carries direct current with no detectable dissipation and actively expels magnetic flux from its bulk, screening an applied field to zero within a few penetration depths λ of its surface. Use the supplied topic model Y_out = 2*b0/(exp(d*sqrt(ns)/106.28)+1/exp(d*sqrt(ns)/106.28)), where Y_out is MID-PLANE FIELD (mT). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; sqrt(x) means √x; exp(x) means eˣ. d is Slab thickness d, measured in nm. Keep ns (Superconducting density n_s) = 4 10²⁸ m⁻³; b0 (Applied field B₀) = 20 mT fixed. For this exercise use only 60.375 nm ≤ d ≤ 359.625 nm 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 = 0.41374661 mT, with a bounded reading uncertainty ±0.055159 mT. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown d by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.029925 nm, 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.
MID-PLANE FIELD against d
Predictions from the supplied Zero resistance, Meissner and critical fields 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): MID-PLANE FIELD / mT
Horizontal axis (x): d: Slab thickness d / nm
Axes show the supplied calibration interval; the origin may lie outside the plot.
Read the exact data as a table
| d: Slab thickness d / nm | MID-PLANE FIELD / mT |
|---|---|
| 60.375 | 11.642102318714104 |
| 135.1875 | 3.1228321554458454 |
| 210 | 0.7684995994953936 |
| 284.8125 | 0.18809543481003832 |
| 359.625 | 0.04602259485452007 |
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(d) = 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
- Endpoint predictions are Y_out(60.375) = 11.6421 mT and Y_out(359.625) = 0.0460226 mT. The target lies between them. On the selected branch the response is strictly decreasing.
- Endpoint interpolation gives d_line = 60.375 + (0.413747 − 11.6421)(359.625 − 60.375)/(0.0460226 − 11.6421) = 350.135 nm. This estimates the inverse of the endpoint chord, rather than assuming that chord is the physical model.
- Use the actual equation, not the chord, and bracket its root. Evaluating the midpoint and retaining the sign-changing half-interval gives d = 242.917 nm; substituting back gives Y_out = 0.41374661 mT.
- The interpolation error in the input is |d_line − d| = 107.218 nm. This is larger than the requested numerical tolerance.
- Invert both limits Y_out = 0.35858761 mT and 0.46890561 mT. Sort the roots even if the response decreases: 236.266 nm ≤ d ≤ 250.522 nm.
- The lower and upper input deviations are 6.65197 nm and 7.60472 nm. 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 d ≈ 242.917 nm; compatible interval [236.266, 250.522] nm. Endpoint interpolation gives 350.135 nm.
Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.