Implement layer inference

[EarlMilktea]

This PR implements infer_layer, which infers flow layer from input flow mapping.
This function is expected to replace xflow_from_pattern features in graphix .

Minor Changes

• Add some validations
• Add suboptimal flow/gflow verification

[codecov-commenter]

Codecov Report

❌ Patch coverage is 99.59677% with 1 line in your changes missing coverage. Please review.
✅ Project coverage is 95.99%. Comparing base (62ff724) to head (43c1bce).

Files with missing lines	Patch %	Lines
python/swiflow/common.py	98.50%	0 Missing and 1 partial ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master      #18      +/-   ##
==========================================
+ Coverage   95.35%   95.99%   +0.64%     
==========================================
  Files          16       16              
  Lines         925     1024      +99     
  Branches       27       48      +21     
==========================================
+ Hits          882      983     +101     
+ Misses         43       40       -3     
- Partials        0        1       +1     

Flag	Coverage Δ
python	99.73% <99.41%> (-0.27%)	⬇️
rust	93.78% <100.00%> (+0.64%)	⬆️

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[Copilot]

Pull Request Overview

This PR implements layer inference functionality that allows computing flow layers from input flow mappings, replacing the need for xflow_from_pattern features in graphix. The implementation includes layer inference for all flow types (flow, gflow, pflow) with validation and suboptimal flow verification capabilities.

Key changes:

• Adds infer_layer function to automatically compute layers from flow mappings
• Modifies verification functions to support both optimal and suboptimal flows
• Updates test cases to verify both optimal and suboptimal scenarios

Reviewed Changes

Copilot reviewed 23 out of 24 changed files in this pull request and generated 2 comments.

Show a summary per file

File	Description
python/swiflow/_common.py	Implements core infer_layer function and helper utilities for layer computation
python/swiflow/flow.py	Updates flow verification to support layer inference and optional optimality checks
python/swiflow/gflow.py	Updates gflow verification to support layer inference and optional optimality checks
python/swiflow/pflow.py	Updates pflow verification to support layer inference and optional optimality checks
python/swiflow/common.py	Simplifies type definitions by removing dataclass wrappers and using type aliases
src/flow.rs	Adds optimal parameter to verification function
src/gflow.rs	Adds optimal parameter to verification function
src/pflow.rs	Adds optimal parameter to verification function
tests/test_*.py	Updates tests to use new API and adds layer inference verification tests
tests/assets.py	Updates test data format and adds new test cases

Comments suppressed due to low confidence (2)

python/swiflow/_common.py:53

• The parameter name plike is ambiguous. Consider using a more descriptive name like plane_mapping or measurement_planes to clarify its purpose.

def check_planelike(vset: AbstractSet[_V], oset: AbstractSet[_V], plike: Mapping[_V, _P]) -> None:

tests/test_common.py:157

• The test expects a specific error message pattern with match=r".*determine.*" but the actual error message from the function is "Failed to determine layer for all nodes." Ensure the test regex pattern accurately matches the expected error message.

            _common.infer_layer(g, flow)

[thierry-martinez]

I propose a more efficient implementation for infer_layers.

[thierry-martinez]

In the definition of a gflow, we should have $f(i) \in \mathcal P(I^c)$, and this condition should be checked. (Courtesy of @matulni for the counter-example.)

import math

import networkx as nx
import numpy as np

from graphix.fundamentals import Plane
from graphix.states import PlanarState
from graphix.generator import _gflow2pattern

import swiflow.gflow
import swiflow.common


def test_gflow() -> None:
    graph = nx.Graph([(0, 1)])
    inputs = {0}
    outputs = {1}
    planes_swiflow = {0: swiflow.common.Plane.XZ}
    # Expecting `None`: this graph does not admit a flow
    g, l_k = swiflow.gflow.find(graph, inputs, outputs, planes=planes_swiflow)
    planes = {0: Plane.XZ}
    angles = {0: 0.1}
    pattern = _gflow2pattern(graph, angles, inputs, planes, g, l_k)
    input_state = PlanarState(Plane.XY, 0.7)
    # Checks that the pattern is deterministic (as a pattern with a gflow should be)
    state_ref = pattern.simulate_pattern(input_state=input_state)
    for _ in range(25):
        state = pattern.simulate_pattern(input_state=input_state)
        assert math.isclose(np.abs(np.dot(state.flatten().conjugate(), state_ref.flatten())), 1)

[EarlMilktea]

Fixed it locally: will push later.
By the way, I suspect pflow finding algorithm introduced in https://arxiv.org/abs/2109.05654 could violate $f(i) \in \mathcal P(I^c)$ .
For example, in the first iteration of PauliFlowAux(V,Γ,I,λ,A,B,k) with B = O, forall u visits u in I and thus u can be included in Kxz and Kxy , possibly leading to $f(i) \notin \mathcal P(I^c)$ .
I welcome your comments on this observation.

[thierry-martinez]

Yes, this side condition is not checked by the published algorithm and should be checked in addition.

[EarlMilktea]

Is it still necessary for "special edges" (see my python impl. in this PR for the terminology)?
I suppose we could safely apply C after some specific Pauli M inside I , as we do for V \ I .

[EarlMilktea]

I'm now trying to port infer_layers and related stuff to Rust, as at least special edge detection must be made available in the binding layer.