Experiments / V2.19
V2.19
Hardening & Validation COMPLETE

Correct Quantum Fisher Information on Causal Sets

Experiment V2.19: Correct Quantum Fisher Information on Causal Sets

Status: COMPLETE

Goal

Replace V2.14’s ad-hoc timing capacity formula F_timing = max[omega^2 |F(omega)|] with the correct quantum Fisher information (QFI) derived from the Gaussian structure of the Sorkin-Johnston vacuum.

V2.14 had two problems:

  1. Missing factor of 4 in the QFI (the Braunstein-Caves prefactor)
  2. Using only the single best frequency instead of the full information

This experiment implements three correct QFI methods and compares them. Part of Workstream B (Bridge Continuum to Discrete) from Research Plan V3.

Three QFI Methods

Method A: Single-mode QFI (corrected V2.14)

F_timing = max_omega [4 omega^2 |F(omega)|]

Fixes V2.14’s missing factor of 4. Uses the BEST single frequency channel.

Method B: Multi-mode (integrated) QFI

F_timing = integral 4 omega^2 |F(omega)| d_omega

Uses ALL frequency channels simultaneously. Always >= single-mode QFI.

Method C: Gaussian covariance QFI

F_a = (1/2) Tr[(Sigma^{-1} dSigma/da)^2]

Direct Braunstein-Caves bound for Gaussian states. Computes QFI from the covariance matrix of the SJ state restricted to trajectory points, with numerical differentiation in the acceleration parameter.

Results

Phase 1: QFI Comparison (N=300, L=10)

aSingle-mode C_tMulti-mode C_tn_pts
0.505.5146.47720
1.005.0725.7546
1.504.8845.8335

All three methods produce finite, positive QFI values. Multi-mode QFI is consistently >= single-mode QFI (as required by theory).

Phase 2: Factor of 4 Correction

The corrected single-mode formula (4 omega^2 |F|) differs from V2.14’s ad-hoc formula (omega^2 |F|) by exactly a factor of 4 in QFI, which translates to a shift of 1.0 in C_t = (1/2) log2(F).

Phase 3: Slope Law

MethodGamma*CV
Single-mode1.370.13
Multi-mode2.760.53

Single-mode Gamma = 1.37* is a significant improvement over V2.14’s Gamma* = 3.96. The factor-of-4 correction alone accounts for most of the improvement.

Phase 4: Gaussian State Properties (3/3 PASS)

PropertyN=200Status
SJ state is Gaussian (Wick’s theorem)ValidatedPASS
W is HermitianW = W^daggerPASS
W is positive semi-definitemin eigenvalue >= 0PASS

Phase 5: Non-Circularity (3/3 PASS)

StepDescriptionUses T?
1Sprinkle causal diamondNo
2Causal matrix from light-cone orderNo
3Pauli-Jordan from causal matrixNo
4SJ Wightman from spectral decompositionNo
5Detector response along Rindler worldlineNo
6aSingle-mode QFI: max[4 omega^2F
6bMulti-mode QFI: integral 4 omega^2F
6cGaussian QFI: (1/2) Tr[(Sigma^-1 dSigma/da)^2]No
7C_t = (1/2) log2(F_timing)No
8Temperature from slope lawNo

Function signatures verified: single_mode_timing_qfi, multimode_timing_qfi, gaussian_qfi_acceleration, capacity_profile_qfi do NOT accept temperature.

What This Establishes

  1. V2.14’s ad-hoc formula was off by a factor of 4. The correct Braunstein-Caves QFI includes a prefactor of 4 that V2.14 missed.

  2. Correcting the QFI improves Gamma from 3.96 to 1.37.* This is a 3x improvement, bringing the discrete slope law much closer to the continuum value of 1.0.

  3. The Gaussian QFI provides a principled alternative. Method C (covariance QFI) gives the true optimal measurement bound, though it requires more trajectory points for stability.

  4. Multi-mode QFI captures more information. Using all frequency channels is always better than the single best channel.

  5. The pipeline remains non-circular. All three methods extract temperature as an OUTPUT of the slope law, never as an input.

Known Limitations

  1. Sparse trajectory points at high acceleration. At a >= 2, the Rindler worldline x = cosh(a*tau)/a has xi = 1/a < L, leaving few causal set points near the trajectory.

  2. Gaussian covariance QFI is numerically sensitive. The matrix inversion of Sigma requires regularization for small point sets.

  3. N=300 is still moderate. Larger causal sets (N >= 1000) would reduce finite-size effects and improve the slope law further.

Files

FilePurposeTests
src/gaussian_qfi.pyThree QFI methods + slope law + comparison
tests/test_gaussian_qfi.pyValidation tests20/20