RQM Core

Core quaternion, spinor, SU(2), and Bloch mathematics for the RQM Python ecosystem.

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🌐 RQM Platform

This repository is part of the RQM Technologies ecosystem.

→ Website: https://rqmtechnologies.com
→ Documentation: https://docs.rqmtechnologies.com

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Better Coordinates for Better Measurement

This project uses quaternions because they preserve more of what physical systems are doing: phase, rotation, orientation, polarization, and coherence. Standard complex-number methods are powerful, but they can flatten these relationships too early. Quaternionic coordinates keep them together as one structured object, giving software a richer view of the measured system.

For RQM Technologies, better coordinates mean better measurement: more informative diagnostics, cleaner transformations, and more precise control across quantum, wave, sensing, imaging, and communications workflows.

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Install

pip install rqm-core

rqm-core is the mathematical foundation layer. It has no RQM-level dependencies — only numpy at runtime.

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Where This Fits

rqm-core  →  rqm-circuits  →  rqm-compiler  →  rqm-qiskit / rqm-braket
                                                        ↓ (optional)
                                                   rqm-optimize

rqm-core provides the canonical mathematical layer of the RQM ecosystem — quaternion, spinor, Bloch, and SU(2) foundations. It owns no circuit schema, no compiler passes, and no backend execution logic.

Layer responsibilities:

Package	Role
rqm-core	Mathematical spine — quaternion, spinor, SU(2), Bloch
rqm-circuits	Canonical external circuit IR / wire format (API, Studio, interchange)
rqm-compiler	Optimization and rewriting engine — consumes rqm-circuits programs
rqm-entanglement	Canonical nonlocal layer — two-qubit tensor structure, coupling analysis, entanglement metrics
rqm-qiskit	IBM / Qiskit lowering and execution bridge
rqm-braket	AWS / Braket lowering and execution bridge
rqm-optimize	Optional backend-adjacent optimization / compression layer

rqm-circuits 0.2 is the canonical external circuit format for all API and Studio workflows. rqm-compiler consumes and optimizes circuit programs but is not the primary public wire format. Backend packages (rqm-qiskit, rqm-braket) translate optimized circuits into vendor-native objects. rqm-optimize is optional and sits later in the execution flow, above the backend bridges.

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Next Steps

• Documentation: https://docs.rqmtechnologies.com
• Website: https://rqmtechnologies.com
• Next package in the stack: rqm-circuits — the canonical external circuit IR for API and Studio workflows (rqm-circuits 0.2+)
• After circuits: rqm-compiler — optimization and rewriting engine

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Why This Package Exists

Higher-level RQM libraries (simulators, compilers, hardware adapters) all require a common, reliable layer of linear algebra and quantum geometry. rqm-core is that layer.

It provides a single, versioned source of truth for the mathematical primitives shared across the whole ecosystem: no duplication, no conflicting conventions, and no framework lock-in.

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Design Principles

Principle	What it means in practice
Tiny	Only implement primitives that are needed by ≥2 packages
Stable	Slow to change; breaking changes require a major version bump
Dependency-light	Only numpy at runtime
Canonical	One correct convention, clearly documented, used everywhere
Well-tested	Strong test coverage from the first commit

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What Is Included

• Quaternion primitives – Hamilton product, conjugate, inverse, axis-angle construction, SO(3) and SU(2) conversions
• Spinor helpers – normalization, norm, fidelity, spinor↔quaternion/SU(2) mappings
• SU(2) conversions – construction from quaternions and axis-angle, validation, round-trips
• Bloch sphere mappings – state↔Bloch, Bloch↔state, quaternion rotation to Bloch vector
• Matrix helpers – trace, determinant, conjugate transpose (dagger), norm, closeness checks
• Validation utilities – axis, complex pair, matrix shape, real number, tolerance checks
• Compatibility coupling analysis – legacy qualitative/measured helpers retained for existing imports; canonical nonlocal analysis now lives in rqm-entanglement

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Mathematical Conventions

The full reference lives in CONVENTIONS.md. The five items every downstream package needs to know:

1 · SU(2) Convention

A unit quaternion q = w + xi + yj + zk maps to SU(2) as:

U(q) = [[ w − iz ,  −y − ix ],
         [ y − ix ,   w + iz ]]

Implemented in Quaternion.to_su2_matrix(); inverted by su2_to_quaternion().

2 · Spinor Convention

States are written |ψ⟩ = α|0⟩ + β|1⟩ with |0⟩ as the north-pole computational-basis ground state. Amplitudes are always passed as the ordered pair (alpha, beta). Functions that require unit norm normalize internally.

3 · Bloch Sphere Parameterization

|ψ⟩ = cos(θ/2)|0⟩ + e^{iφ} sin(θ/2)|1⟩

theta ∈ [0, π] (polar/colatitude), phi ∈ [0, 2π) (azimuthal). |0⟩ → north pole (0, 0, +1); |1⟩ → south pole (0, 0, −1).

4 · Global Phase

q and −q represent the same SO(3)/Bloch rotation, while their SU(2) matrices differ by a global phase of −1. spinor_to_quaternion encodes the rotation up to global phase — never rely on the sign of the scalar part.

5 · Default Tolerance and Axis Labels

All closeness checks default to atol = 1e-9 (absolute, no relative component). Axis labels are "x", "y", "z" (case-insensitive); all angles are in radians.

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Coupling / Entanglement Analysis

rqm_core retains a compatibility analysis layer for existing imports, but it is no longer the canonical owner of coupling and entanglement analysis. New API and Studio integrations should call rqm-entanglement.

Architecture

The two layers are mathematically complementary and do not compete:

Layer	Scope	Technology
Single-qubit local structure	Individual qubit rotations	Quaternionic / SU(2) (quaternion optimizer)
Multi-qubit entanglement	Cross-qubit coupling / correlation	rqm-entanglement state analysis, concurrence, entropy

The quaternionic single-qubit optimizer is the correct route for local SU(2) operations. The coupling analysis layer adds truthful multi-qubit analysis beside it.

Quickstart

from rqm_core import Circuit, GateOp, analyze_circuit_coupling

# Bell state: H q0, CNOT q0→q1
circuit = Circuit(
    num_qubits=2,
    operations=[
        GateOp(name="H",    qubits=[0]),
        GateOp(name="CNOT", qubits=[0, 1]),
    ],
)

result = analyze_circuit_coupling(circuit)
print(result.mode)          # "measured"
print(result.is_entangled)  # True
print(result.pair_metrics[0].value)  # 1.0  (concurrence)

Measured Analysis Scope (first implementation)

Criterion	Scope
Qubits	Exactly 2
Initial state	|00⟩
Single-qubit gates	I, X, Y, Z, H, S, T, Rx(θ), Ry(θ), Rz(θ), U(θ,φ,λ) / U3
Two-qubit gates	CNOT / CX, CZ, SWAP
Metrics	Concurrence, von Neumann entropy

Circuits outside this scope receive an honest qualitative fallback (gate detection only) with explicit limitations in the result — no fabricated measured values.

Result Contract

@dataclass
class CouplingAnalysisResult:
    mode: str                          # "measured" | "qualitative"
    provenance: str                    # "rqm-core" | "parser"
    qubit_count: int
    analyzed_pairs: list[tuple[int, int]]
    has_entangling_gates: bool
    entangling_gate_count: int
    entangling_gates_seen: list[str]
    last_entangling_gate: str | None
    is_entangled: bool | None          # None in qualitative mode
    pair_metrics: list[PairMetric]     # empty in qualitative mode
    fidelity_preserved: float | None
    notes: list[str]
    limitations: list[str]

Compiler Verification

from rqm_core import analyze_optimization_preservation

result = analyze_optimization_preservation(original_circuit, optimized_circuit)
print(result.fidelity_preserved)              # e.g. 1.0
print(result.preserved_entanglement_structure) # True / False / None

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What Is Not Included

rqm-core intentionally does not own:

• Qiskit / PennyLane / Cirq adapters
• Backend execution or hardware drivers
• Circuit transpilation or compilation
• Plotting or visualisation
• Cloud workflow integration
• Notebook tooling
• Algorithm frameworks or optimisation workflows

Those belong in higher-level packages.

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Installation

pip install rqm-core

Development install (includes pytest and pytest-cov):

pip install "rqm-core[dev]"

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Ecosystem Role

rqm-core is the mathematical spine of the RQM Python ecosystem. It provides the single canonical implementation of all shared mathematical primitives so that downstream packages never need to re-implement or copy them.

What rqm-core owns:

• Quaternion primitives (Hamilton product, conjugate, inverse, axis-angle, SO(3)/SU(2) conversions)
• SU(2) / Bloch / spinor mathematics
• Shared linear algebra helpers
• Coupling / entanglement analysis primitives

What rqm-core does NOT own:

• Circuit IR or schema — that is rqm-circuits
• Compiler rewrites or optimization passes — that is rqm-compiler
• Backend execution or vendor-native objects — that is rqm-qiskit / rqm-braket
• API service boundaries or Studio wire formats — those live above rqm-circuits

How downstream packages declare the dependency (example pyproject.toml excerpt):

[project]
dependencies = [
    "rqm-core>=0.1.0",
]

How downstream packages import:

# rqm-qiskit, rqm-circuits, rqm-compiler, etc.
from rqm_core import Quaternion, axis_angle_to_su2, state_to_bloch

Contract for downstream maintainers:

• All conventions are defined in CONVENTIONS.md and must not be re-defined or overridden locally.
• Breaking changes to rqm-core require a major version bump (1.0.0, …).
• Any primitive needed by two or more packages belongs here, not in the individual packages.

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Quickstart

from rqm_core import Quaternion, state_to_bloch, axis_angle_to_su2
import math

# 90° rotation around Y
q = Quaternion.from_axis_angle("y", math.pi / 2)
print(q)
# Quaternion(0.7071..., 0.0, 0.7071..., 0.0)

# SU(2) matrix directly from axis-angle
print(axis_angle_to_su2("y", math.pi / 2))

# |+⟩ state on the Bloch sphere → equator at (1, 0, 0)
c = 1 / math.sqrt(2)
x, y, z = state_to_bloch(c, c)
print(x, y, z)  # 1.0  0.0  0.0

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Package Structure

rqm-core/
  CONVENTIONS.md            – canonical mathematical conventions reference
  pyproject.toml            – package metadata and build config
  src/rqm_core/
    __init__.py      – canonical public API (import everything from here)
    py.typed         – PEP 561 marker (enables type checking in downstream packages)
    quaternion.py    – Quaternion class (Hamilton algebra, SO(3)/SU(2) conversions)
    spinor.py        – spinor normalization, fidelity, spinor↔quaternion/SU(2)
    su2.py           – SU(2) construction, validation, quaternion round-trips
    bloch.py         – Bloch sphere mappings and validation
    linalg.py        – matrix helpers (dagger, trace, determinant, closeness)
    validation.py    – shared validation helpers (axis, matrix shape, tolerances)
    types.py         – shared type aliases (ComplexVector2, BlochVector, SU2Matrix, …)
    utils.py         – small math utilities (angle_wrap, safe_norm, is_finite_*)
    analysis/
      coupling/
        types.py                          – Circuit IR + result contract dataclasses
        detect_entangling_structure.py    – qualitative gate-based detection
        simulate_two_qubit_state.py       – ideal 2-qubit pure-state simulator
        metrics.py                        – concurrence, entropy, fidelity helpers
        analyze_circuit_coupling.py       – main public entry point
        analyze_optimization_preservation.py – before/after compiler verification

  tests/
    test_quaternion.py
    test_spinor.py
    test_su2.py
    test_bloch.py
    test_linalg.py
    test_utils.py
    test_validation.py
    test_public_api.py
    analysis/
      coupling/
        test_detect_entangling_structure.py
        test_simulate_two_qubit_state.py
        test_metrics.py
        test_analyze_circuit_coupling.py
        test_analyze_optimization_preservation.py

  examples/
    quaternion_basics.py    – quaternion construction, composition, conversion
    spinor_basics.py        – spinor normalization, Bloch mapping, fidelity
    su2_bloch_demo.py       – axis-angle → SU(2) → Bloch pipeline
    bloch_mapping_demo.py   – canonical Bloch vectors and round-trip checks
    su2_rotation_demo.py    – SU(2) construction and quaternion round-trip

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Testing

# Install the package in editable mode with test dependencies
pip install -e ".[dev]"

# Run the full test suite
pytest

# Run with coverage report
pytest --cov=rqm_core --cov-report=term-missing

# Run a single test file
pytest tests/test_quaternion.py -v

# Run only the public-API contract tests
pytest tests/test_public_api.py -v

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Architectural Notes

• su2.is_unitary is a thin domain-scoped shim over linalg.is_unitary. Both exist intentionally: linalg.is_unitary is the general-purpose helper; su2.is_unitary is the public entry point for SU(2)-context callers and is what rqm_core.is_unitary resolves to in the top-level API.

• q and −q represent the same SO(3)/Bloch rotation, not the same SU(2) matrix. Round-trip conversions q → SU(2) → q are tested against both q and −q because the sign is a global phase. Downstream code must never compare quaternion scalar parts for sign equality.

• The src/ layout means rqm_core is only importable after installation (pip install -e .). Running tests directly without installing first will produce ModuleNotFoundError.

• py.typed is included in the installed wheel (via [tool.setuptools.package-data]), enabling mypy and pyright in any package that depends on rqm-core.

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Roadmap

rqm-core is intended to remain small and stable while higher-level packages evolve around it.

Planned additions for future minor versions:

• Quaternion SLERP (spherical linear interpolation)
• SO(3) rotation-matrix ↔ quaternion round-trip helpers
• Mixed-state density matrix utilities
• SU(2) Lie-algebra generators and exponential map
• Type stub files (.pyi) for IDE completions
• Coupling analysis for >2 qubits (partial trace for n-qubit systems, mutual information)
• Support for additional gate sets in the 2-qubit simulator

No Qiskit or framework dependencies will ever be added to this package.