rqm-entanglement

rqm-entanglement is the two-qubit / nonlocal layer of the RQM quantum stack. It provides tensor-product state and operator helpers, arbitrary two-qubit quaternion–Cartan decomposition, Weyl classification and local-equivalence fingerprints, the canonical commuting nonlocal gate family, pure-state entanglement measures, and operator Schmidt-rank classification. rqm-core remains the authority for canonical quaternion/SU(2) conversion.

It uses ordinary complex tensor products and standard entanglement measures. Local quaternion adapters provide equivalent SU(2) coordinates; they do not define native quaternionic composite mechanics. See RQM_TECHNICAL_CANON_V2.md.

Architecture boundary with rqm-core

All rqm-core integration lives in src/rqm_entanglement/adapters/rqm_core_adapter.py. That adapter is the only place Entanglement imports Core, and the measured circuit analyzers use it for canonical u1q quaternion gates emitted by rqm-compiler.

Basis ordering

Computational basis: |00⟩, |01⟩, |10⟩, |11⟩
Qubit 0 is the more-significant (left) index.
CNOT: control = qubit 0, target = qubit 1.

The verified optional-Qiskit factor order is qiskit-Klr:kron(q1=Kl,q0=Kr);circuit-little-endian.

Canonical entangler

U_ent(c1, c2, c3) = exp[-i/2 (c1 XX + c2 YY + c3 ZZ)]

Because XX, YY, ZZ mutually commute, this is computed analytically as

U_ent = xx_rotation(c1) @ yy_rotation(c2) @ zz_rotation(c3)

where exp[-i θ/2 P] = cos(θ/2) I4 - i sin(θ/2) P for P ∈ {XX, YY, ZZ}.

Quaternion–Cartan SU(4)

Version 0.2 promotes the tested EXP-012 representation:

from rqm_entanglement import QuaternionCartanBlock, classify_su4

block = QuaternionCartanBlock.from_unitary(unitary)  # requires [qiskit]
assert block.validate()["valid"]
reconstructed = block.to_unitary()
classification = classify_su4(block)

Stored Weyl coordinates use exp[i(a XX + b YY + c ZZ)]. Existing canonical_entangler(c1,c2,c3) uses exp[-i/2(c1 XX + c2 YY + c3 ZZ)]; use weyl_to_rotation_coordinates and rotation_to_weyl_coordinates for the exact conversion.

Reconstruction, serialization, hashes, classification of an existing block, and canonical-entangler construction do not import Qiskit. Generic arbitrary SU(4) decomposition uses the optional public Qiskit Weyl authority:

pip install -e ".[qiskit]"

The classifier distinguishes nonlocal operator, entangling-gate, perfect-entangler, and SWAP-like status. SWAP remains truthfully nonlocal while not being labeled as a gate that entangles product inputs.

Quick start

import numpy as np
from rqm_entanglement import (
    I2, Y, CNOT,
    ket00, local_unitary, apply_unitary,
    concurrence_pure, entanglement_entropy_pure
)

def ry(theta: float) -> np.ndarray:
    return np.cos(theta / 2) * I2 - 1j * np.sin(theta / 2) * Y

psi0 = ket00()
U = CNOT @ local_unitary(ry(np.pi / 2), I2)
psi = apply_unitary(U, psi0)

print(concurrence_pure(psi))           # ~1.0
print(entanglement_entropy_pure(psi))  # ~1.0

Stable entanglement analysis API (for rqm-api / Studio)

For integration points like rqm-api endpoint /v1/entanglement/analyze, use the stable exported function:

from rqm_entanglement import analyze_entanglement

from rqm_entanglement import CNOT, I2, Y, ket00, local_unitary, apply_unitary, analyze_entanglement
import numpy as np

def ry(theta: float) -> np.ndarray:
    return np.cos(theta / 2) * I2 - 1j * np.sin(theta / 2) * Y

psi0 = ket00()
psi_bell = apply_unitary(CNOT @ local_unitary(ry(np.pi / 2), I2), psi0)

result = analyze_entanglement(psi_bell)
print(result)

Result schema:

{
  "has_entangling_gates": bool,
  "entangled_pairs": [
    {
      "pair": [0, 1],
      "metric_name": str,
      "metric_value": float,
      "interpretation": str,  # optional
    }
  ],
  "last_entangling_gate": str,  # optional
  "fidelity_preserved": float | None,
  "notes": [str],
}

Example (Bell-state-like input):

{
  "has_entangling_gates": True,
  "entangled_pairs": [
    {"pair": [0, 1], "metric_name": "Concurrence", "metric_value": 1.0},
    {"pair": [0, 1], "metric_name": "Entropy", "metric_value": 1.0},
    {"pair": [0, 1], "metric_name": "Mutual Information", "metric_value": 2.0},
    {"pair": [0, 1], "metric_name": "RQM Correlation", "metric_value": 1.0},
  ],
  "fidelity_preserved": None,
  "notes": [],
}

Input forms accepted by analyze_entanglement:

• pure state vector (4,)
• unitary / SU(4) matrix (4,4)
• gate sequence (list of (4,4) matrices), including:
  • unnamed gates: [U0, U1, ...]
  • named gates: [("gate_name", U), ...]
  • dict-like entries: [{"name": "...", "unitary": U}, ...]
• RQM circuit payload (Studio/API contract style), e.g.:

  {
    "schema_version": "0.1",
    "num_qubits": 2,
    "instructions": [
      {
        "gate": { "name": "h", "arity": 1 },
        "targets": [{ "index": 0, "type": "qubit" }]
      },
      {
        "gate": { "name": "cx", "arity": 2 },
        "targets": [
          { "index": 0, "type": "qubit" },
          { "index": 1, "type": "qubit" }
        ]
      }
    ]
  }
  

  Supported contract gate names currently include:
  • single-qubit: i, x, y, z, h, s, sdg, t, tdg, rx, ry, rz, u1q
  • two-qubit: cx/cnot, cz, swap, iswap Unsupported instructions are skipped with explanatory notes while preserving the stable result schema.

For a finite unitary (4,4) input, the result additively includes su4 with Cartan coordinates, Weyl class, both hashes, operator Schmidt rank, local quaternion shells, global phase, reconstruction error, and convention version. When the optional Qiskit dependency is absent, all legacy fields remain available and notes explains why decomposition was omitted.

Metric formulas and conventions

Basis ordering is always |00>, |01>, |10>, |11> with qubit 0 as the more-significant (left) index.

For a pure state |psi> = [a00, a01, a10, a11]^T:

• Concurrence
  • C = 2 * |a00*a11 - a01*a10|
  • Range: [0, 1]
  • 0 for product states, 1 for maximally entangled states
• Entanglement Entropy
  • S(rho_A) = -Tr(rho_A log2 rho_A) (bits), where rho_A is reduced density matrix of either qubit for pure two-qubit states
  • Range: [0, 1]
• Mutual Information
  • I(A:B) = S(rho_A) + S(rho_B) - S(rho_AB) (bits)
  • For pure two-qubit states, S(rho_AB)=0, so I(A:B)=2*S(rho_A) and range is [0, 2]
• RQM Correlation (optional software summary metric, not new physics)
  • 0.5 * (clamped_concurrence + clamped_entropy)
  • Range: [0, 1]

Numerical conventions:

• tiny negative eigenvalues from floating-point roundoff are clipped to 0
• output metrics are clamped to physically valid ranges
• non-finite values (NaN/Inf) are sanitized to stable values with notes
• metric ordering is deterministic for identical input

Scope and current limits

• Primary support: 2-qubit SU(4)/state analysis
• Inputs that appear to represent >2 qubits are handled gracefully:
  • no crash
  • stable output schema
  • explanatory notes
• Full generic multi-qubit entanglement analysis is out of scope.

The tested quaternion–Cartan representation is standard complex quantum mechanics. It is not unique quantum information or native quaternionic composite mechanics, and it carries no general synthesis, runtime, or IBM hardware superiority claim. See docs/EXP012_PROMOTION_PROVENANCE.md.

Installation

pip install -e ".[dev]"

Run tests:

pytest