quantum-shadow-maps_v2/scripts/dps_hierarchy

Entanglement-detection scripts from this chat

This is the code from a conversation that started with the "symmetric shadow maps" paper (symmetric_shadow_maps_formal.tex) and worked outward through a chain of entanglement-detection techniques on the two-qutrit Tiles bound-entangled state (Bennett-DiVincenzo-Mor-Shor-Smolin-Terhal UPB state, already used as a benchmark in the paper): symmetric-state sanity checks, DPS symmetric-extension SDPs, noise-robustness thresholds, local filtering, and (in progress) a third DPS extension level.

Note: none of this has been re-run/verified after being assembled into this package (per your request) -- it's a straight extraction of the code from the chat. The numbers quoted in each docstring/comment are what the sandbox actually produced during the conversation; treat them as "expected results to check against" rather than guaranteed.

Setup

pip install -r requirements.txt

scs is the default (open-source, first-order) SDP solver used throughout. If you have a MOSEK license (free for academics), it is very likely much faster for these problem sizes -- just change SOLVER = cp.SCS to SOLVER = cp.MOSEK near the top of scripts 03, 04, 06, 07, 08.

Files, in the order they came up in the conversation

File	What it does	Expected result	Rough runtime
common.py	Shared utilities: Gell-Mann generators (paper convention), Tiles state, qutrit Werner state, correlation-matrix/shadow-map criterion, plain PPT check, operator-Sinkhorn filter. Imported by scripts 03-06.	--	--
dps_hierarchy.py	General, level-k-parametrized DPS symmetric-extension SDP builder (used for levels 2 and 3, and usable for higher k).	--	--
01_werner_qubit_symbolic.py	Exact (sympy) check: 2-qubit Werner state, SU(2) symmetry forces the correlation matrix ∝ I.	Shadow-map threshold exactly matches PPT: p_c = 1/3 both ways.	seconds
02_werner_qutrit_symbolic.py	Same, generalized to qutrits (antisymmetric-subspace Werner state).	Shadow-map threshold p_c = 1/2, but true threshold (Werner 1989) is p_c = 1/4 -- the order-1 criterion is valid but NOT tight in d=3 (unlike d=2).	under a minute
03_dps_level2_demo.py	DPS level 2 via dps_hierarchy. Sanity check on Werner qutrit; then the interesting case: Tiles state, where plain PPT is exactly blind (min eigenvalue ≈ 0) but DPS-2 detects it.	Werner: consistent with p=1/4 away from the boundary. Tiles: PPT feasible=True, DPS-2 feasible=False (detected). Robust across solver tolerances 1e-5..1e-8.	under a minute
04_tiles_noise_scan.py	Noise-threshold bisection for (a) the plain shadow-map criterion and (b) DPS level 2, on the noisy Tiles family.	Shadow-map p_c ≈ 0.9493 (5.07% tolerance); DPS-2 p_c ≈ 0.951 (4.9%) -- i.e. DPS-2 barely improves on the much cheaper order-1 criterion for this state.	a few minutes (DPS-2 bisection)
05_local_filtering.py	Operator-Sinkhorn local filtering (SLOCC normal form) + the plain shadow-map criterion on the filtered state.	p_c ≈ 0.8722 (12.78% tolerance) -- matches the literature's "Filter Covariance Matrix Criterion" (Gittsovich, Gühne, Hyllus, Eisert, arXiv:0803.0757, Prop. IV.13: p_c = 0.8723, 12.77%) to ~4 decimal places.	seconds
06_dps_level2_filtered.py	DPS level 2 applied to the filtered state (combining both levers).	p_c ≈ 0.9426 (5.74%) -- filtering helps DPS-2 only marginally, much less than it helps the plain shadow-map (05). Filtering and DPS-extension-order are not equally powerful levers here, and don't simply stack.	a few minutes
07_dps_level3_single.py	A single DPS level-3 feasibility check on the pure Tiles state, to confirm level 3 is tractable at all.	status = infeasible (detected). Took ~137s with SCS in the original sandbox.	~1-3 minutes
08_dps_level_k_bisection.py	General, resumable bisection for the DPS level-k noise threshold, one step per invocation, progress saved to JSON. Defaults to LEVEL=3.	See below -- in progress.	~2-4 min per step with SCS (k=3)
dps_level3_bisection_state.json	Saved progress for the level-3 bisection from the original session (8 SCS solves already spent).	Current bracket: [0.90982, 0.91080], i.e. p_c ≈ 0.910-0.911.	--
09_dps_level3_filtered_bisection.py	Combines local filtering (05) with DPS level 3 (07/08): filter the state, then run the level-3 SDP on it. Resumable, same pattern as 08.	Untested/in progress -- based on the level-2 precedent (06), expect only a modest improvement over plain level 3, not a jump to ~13%. Starting bracket is an educated guess, not a proven bound (see the script's docstring).	most expensive script here: ~85-140s per solve, ~12-16 solves for a full bisection

Where the level-3 bisection currently stands

{"lo": 0.90982, "hi": 0.91080, "iter": 8}

So DPS level 3 detects entanglement for p ≳ 0.910, i.e. roughly 9.0% noise tolerance -- already better than level 2's 4.9-5.7%, but still well short of the 12.77-12.78% that local filtering alone achieves. Just re-run 08_dps_level_k_bisection.py (it picks up the saved state automatically) to narrow this further.

The overall picture that emerged (for reference)

Method	p_c	Noise tolerance
plain PPT	~1.0	~0% (knife-edge)
shadow-map / de Vicente Bloch criterion (order 1)	0.9493	5.07%
DPS level 2	0.9510	4.90%
DPS level 2 + filtering	0.9426	5.74%
DPS level 3 (partial result so far)	~0.910	~9.0% (narrowing)
local filtering + shadow-map (order 1)	0.8722	12.78%
literature: Filter-CMC (Prop. IV.13)	0.8723	12.77%
literature: best known positive map	0.8744	12.56%

Headline takeaway: for this particular state, local filtering (a SLOCC pre-processing step) is a far bigger lever than increasing the DPS extension order, and the two don't stack additively -- filtering the state and then applying the cheapest possible (order-1) criterion already matches a specialized literature result almost exactly, while adding DPS levels on top gives comparatively little.

A performance note on why level 3+ gets slow

The DPS SDP variable is parametrized on A ⊗ Sym^k(B), dimension d · C(d+k-1, k) -- polynomial in k (this is the "exploit the built-in Bose symmetry of the extension copies" trick). But the PPT constraint itself has to be checked on the full, unsymmetrized embedding A ⊗ B_1 ⊗ ... ⊗ B_k, dimension d^(k+1) -- exponential in k. Since SDP solver cost is governed by the size of the PSD cone (the PPT constraint), not by the number of free variables, this is why level 3 (81×81 cone) is already much slower than level 2 (27×27 cone), and level 4 (243×243) would be slower still. A proper fix would exploit representation-theoretic structure of the PPT constraint itself, not just of the extension -- that's a bigger undertaking than what's implemented here.

If you have MOSEK (or another interior-point solver): try it first for levels 3-4. Interior-point methods are usually much faster than SCS on small/medium, dense SDPs like these -- SCS is tuned for large sparse problems and is likely the main reason level 3 took ~137s-227s per solve here rather than a fraction of a second.