q-orca 0.3.1

Q-Orca — Quantum Orchestrated State Machine Language

Q-Orca — Quantum Orchestrated State Machine Language

Q-Orca is a quantum-aware dialect of Orca, a state machine language written in Markdown. It extends Orca with Dirac ket notation for quantum states, unitary gate actions, entanglement verification, and simulation via Qiskit.

All 5 example machines (Bell, GHZ, Deutsch-Jozsa, Teleportation, VQE) pass the full 5-stage verification pipeline on every commit, across Python 3.10–3.13.

---

Install

pip install q-orca[quantum]

Installs the CLI, verifier, compilers, and Qiskit/QuTiP simulation support.

pip install q-orca[all]      # + MCP server (pyyaml)
pip install q-orca           # CLI + verifier only, no quantum libs

---

Setup (development)

# Create and activate a virtual environment
python3 -m venv .venv
source .venv/bin/activate  # Linux/macOS
# .venv\Scripts\activate   # Windows

# Install Q-Orca in editable mode (with quantum libraries)
pip install -e ".[quantum]"

# Or install with MCP server support
pip install -e ".[all]"

# Or install without quantum deps first
pip install -e .
pip install qiskit
pip install qutip  # optional, for quantum verification

To exit the virtual environment: deactivate

---

Running

# Check version
q-orca --version

# Verify a quantum machine
q-orca verify examples/bell-entangler.q.orca.md
q-orca verify examples/bell-entangler.q.orca.md --json
q-orca verify examples/bell-entangler.q.orca.md --strict        # warnings → errors
q-orca verify examples/bell-entangler.q.orca.md --skip-dynamic  # skip QuTiP simulation

# Compile to Mermaid diagram
q-orca compile mermaid examples/quantum-teleportation.q.orca.md

# Compile to OpenQASM 3.0
q-orca compile qasm examples/bell-entangler.q.orca.md

# Generate Qiskit simulation script
q-orca simulate examples/bell-entangler.q.orca.md

# Run simulation immediately
q-orca simulate examples/bell-entangler.q.orca.md --run

# Noisy simulation with 2048 shots
q-orca simulate examples/bell-entangler.q.orca.md --run --shots 2048

# With QuTiP verification
q-orca simulate examples/bell-entangler.q.orca.md --run --verbose

# MCP self-description (for Claude Code integration)
q-orca --tools --json

# Read source from stdin
cat examples/bell-entangler.q.orca.md | q-orca --stdin verify

---

How Verification Works

Every machine passes through 5 stages in order. A failure in stage 1 stops the pipeline early; later stages are cumulative.

Stage	Module	What it checks
1 — Structural	structural.py	All states reachable, no deadlocks, no orphan states
2 — Completeness	completeness.py	Every (state, event) pair has at least one outgoing transition
3 — Determinism	determinism.py	Guards on competing transitions are mutually exclusive
4 — Quantum	quantum.py	Unitarity of gates, no-cloning violations, entanglement declarations, collapse probability sum = 1
4b — Dynamic	dynamic.py	QuTiP circuit simulation: actual Schmidt rank and Von Neumann entropy for every declared entangled state
5 — Superposition	superposition.py	No superposition coherence leaks across unguarded transitions

Stage 4b is a soft dependency: if QuTiP is not installed it skips gracefully and CI still passes.

Stage 4 vs 4b — static vs dynamic

Stage 4 (quantum.py) checks your declarations: does the Markdown say this state is entangled? Does a CNOT gate lead to it? These are fast structural checks that catch obvious mistakes.

Stage 4b (dynamic.py) simulates the circuit: it replays every gate in the path from the initial state, then computes the actual Von Neumann entropy and Schmidt rank of the resulting state vector using QuTiP. This catches cases where the gate sequence is present but wrong — e.g. two Hadamards that cancel, a CNOT on the wrong qubits, or a rotation angle that leaves the state separable.

For a valid Bell state (H(q0) then CNOT(q0, q1)), the dynamic verifier internally computes:

{
  "state": "|ψ>",
  "entropy_checks": { "q0": 1.0 },
  "schmidt_ranks": { "q0-q1": 2 },
  "passed": true,
  "details": {}
}

• entropy_checks.q0 = 1.0 — Von Neumann entropy of qubit 0 after tracing out q1. Exactly 1.0 = maximally entangled.
• schmidt_ranks.q0-q1 = 2 — Schmidt rank across the q0/q1 bipartition. Rank > 1 confirms entanglement; rank = 1 means separable.

If either value falls below threshold, stage 4b emits a DYNAMIC_NO_ENTANGLEMENT error (shown below).

Example failure report

A machine with a missing CNOT gate and an incomplete collapse:

$ q-orca verify broken-bell.q.orca.md

  Machine: BrokenBell
  States: |00>, |ψ>, |00_collapsed>
  Events: prepare, measure_done
  Transitions: 2
  Verification rules: unitarity, entanglement

  Result: INVALID
  [WARN] ENTANGLEMENT_WITHOUT_GATE: State '|ψ>' is declared as entangled but no entangling gate (CNOT, CZ, etc.) leads to it
        -> Add a transition with a CNOT or other entangling gate action
  [ERR]  DYNAMIC_NO_ENTANGLEMENT: State '|ψ>' should be entangled but verification failed: q0-q1: Schmidt rank 1 ≤ 1
        -> Ensure the circuit creates an entangled state with CNOT or CZ gates
  [ERR]  DYNAMIC_INCOMPLETE_COLLAPSE: Measurement branches have probabilities summing to 0.5000, expected 1.0
        -> Ensure all collapse outcomes are covered with probabilities summing to 1

Same report as JSON (--json):

{
  "machine": "BrokenBell",
  "valid": false,
  "errors": [
    {
      "code": "ENTANGLEMENT_WITHOUT_GATE",
      "message": "State '|ψ>' is declared as entangled but no entangling gate (CNOT, CZ, etc.) leads to it",
      "severity": "warning",
      "suggestion": "Add a transition with a CNOT or other entangling gate action"
    },
    {
      "code": "DYNAMIC_NO_ENTANGLEMENT",
      "message": "State '|ψ>' should be entangled but verification failed: q0-q1: Schmidt rank 1 ≤ 1",
      "severity": "error",
      "suggestion": "Ensure the circuit creates an entangled state with CNOT or CZ gates"
    },
    {
      "code": "DYNAMIC_INCOMPLETE_COLLAPSE",
      "message": "Measurement branches have probabilities summing to 0.5000, expected 1.0",
      "severity": "error",
      "suggestion": "Ensure all collapse outcomes are covered with probabilities summing to 1"
    }
  ]
}

Declaring invariants in Markdown

For precise dynamic checks, add an ## invariants section to your machine. This tells stage 4b exactly which qubit pairs to verify rather than using the default adjacent-pair heuristic:

## invariants
- entanglement(q0,q1) = True
- schmidt_rank(q0,q1) >= 2

---

Commands

q-orca verify

Parses and verifies a quantum machine definition through all 5 stages.

q-orca verify examples/bell-entangler.q.orca.md
q-orca verify examples/bell-entangler.q.orca.md --json
q-orca verify examples/bell-entangler.q.orca.md --strict
q-orca verify examples/bell-entangler.q.orca.md --skip-dynamic

Flag	Description
--json	Output as JSON
--strict	Treat warnings as errors (exit 1 on any warning)
--skip-completeness	Skip stage 2: event completeness checks
--skip-quantum	Skip stage 4: unitarity, no-cloning, entanglement
--skip-dynamic	Skip stage 4b: QuTiP circuit simulation

All 5 bundled examples pass --strict on every CI run (Python 3.10–3.13).

Example: passing strict verify with dynamic entanglement confirmed

$ q-orca verify examples/bell-entangler.q.orca.md --strict --json

{
  "machine": "BellEntangler",
  "valid": true,
  "errors": []
}

When stage 4b runs (QuTiP installed), it internally verifies entropy(q0) ≈ 1.0 and Schmidt rank(q0,q1) = 2 for the Bell state before emitting this clean result. Any failure there produces a DYNAMIC_NO_ENTANGLEMENT error — see How Verification Works for the full failure report format.

q-orca compile

Compiles a machine to a target format.

q-orca compile mermaid examples/quantum-teleportation.q.orca.md
q-orca compile qasm examples/bell-entangler.q.orca.md

q-orca simulate

Generates and optionally runs a Qiskit Python script.

# Output the Qiskit script (no execution)
q-orca simulate examples/bell-entangler.q.orca.md

# Run the simulation immediately
q-orca simulate examples/bell-entangler.q.orca.md --run

# Noisy simulation with 2048 shots
q-orca simulate examples/bell-entangler.q.orca.md --run --shots 2048

# Skip QuTiP verification
q-orca simulate examples/bell-entangler.q.orca.md --run --skip-qutip

# JSON output
q-orca simulate examples/bell-entangler.q.orca.md --run --json

---

Examples

File	Description
bell-entangler.q.orca.md	Bell state via Hadamard + CNOT
quantum-teleportation.q.orca.md	Teleports a qubit via Bell pair
deutsch-jozsa.q.orca.md	Constant vs balanced oracle detection
ghz-state.q.orca.md	3-qubit GHZ state preparation
vqe-heisenberg.q.orca.md	Variational quantum eigensolver for Heisenberg XXX Hamiltonian

---

Machine Format

The full source for every example is in examples/. Here is bell-entangler.q.orca.md:

# machine BellEntangler

## context
| Field      | Type          | Default          |
|------------|---------------|------------------|
| qubits     | list<qubit>   | [q0, q1]         |
| outcome    | int           | -1               |

## events
- prepare_H
- entangle
- measure_done

## state |00>
> Ground state, no entanglement yet

## state |+0> = (|0> + |1>)|00>/√2
> After Hadamard on qubit 0 — superposition

## state |ψ> = (|00> + |11>)/√2
> Bell state after Hadamard + CNOT

## state |00_collapsed> [final]
> Collapsed to |00> after measurement

## state |11_collapsed> [final]
> Collapsed to |11> after measurement

## transitions
| Source          | Event        | Guard                  | Target              | Action                  |
|-----------------|--------------|------------------------|---------------------|-------------------------|
| |00>            | prepare_H    |                        | |+0>                | apply_H_on_q0           |
| |+0>            | entangle     |                        | |ψ>                 | apply_CNOT_q0_to_q1     |
| |ψ>             | measure_done | prob_collapse('00')=0.5| |00_collapsed>       | set_outcome_0           |
| |ψ>             | measure_done | prob_collapse('11')=0.5| |11_collapsed>       | set_outcome_1           |

## guards
| Name                | Expression                          |
|---------------------|-------------------------------------|
| prob_collapse('00') | fidelity(|ψ>, |00>) ** 2 ≈ 0.5     |
| prob_collapse('11') | fidelity(|ψ>, |11>) ** 2 ≈ 0.5     |

## actions
| Name                | Signature                          | Effect                     |
|---------------------|------------------------------------|----------------------------|
| apply_H_on_q0       | (qs) -> qs                         | Hadamard(qs[0])            |
| apply_CNOT_q0_to_q1 | (qs) -> qs                         | CNOT(qs[0], qs[1])         |
| set_outcome_0       | (ctx, val) -> Context              | ctx.outcome = 0            |
| set_outcome_1       | (ctx, val) -> Context              | ctx.outcome = 1            |

## effects
| Name          | Input                  | Output            |
|---------------|------------------------|-------------------|
| collapse      | state vector           | classical bit     |

## verification rules
- unitarity: all gates preserve norm
- entanglement: final state must have Schmidt rank >1 before measure
- completeness: all possible collapses covered (no missing branches)
- no-cloning: no copy ops allowed

Full source: examples/bell-entangler.q.orca.md — or view all examples in examples/

---

Verify output (5-stage pipeline)

$ q-orca verify examples/bell-entangler.q.orca.md --json

{
  "machine": "BellEntangler",
  "valid": true,
  "errors": []
}

All 5 stages pass silently. To see individual stage results, use the Python API:

from q_orca.skills import verify_skill

result = verify_skill({"file": "examples/bell-entangler.q.orca.md"})
# result = {
#   "status": "valid",    ← all 5 stages passed
#   "machine": "BellEntangler",
#   "states": 5,
#   "events": 3,
#   "transitions": 4,
#   "errors": []
# }

The 5 verification stages are:

Stage	Module	Checks
1 Structural	structural.py	Reachability, deadlocks, orphan states
2 Completeness	completeness.py	Every (state, event) pair has a transition
3 Determinism	determinism.py	Guards are mutually exclusive
4 Quantum	quantum.py + dynamic.py	Unitarity, no-cloning, entanglement (QuTiP), collapse completeness
5 Superposition	superposition.py	No superposition coherence leaks

---

Compile to Mermaid diagram

$ q-orca compile mermaid examples/bell-entangler.q.orca.md

stateDiagram-v2
  direction LR

  00 : |00>
  0 : |+0> = (|0> + |1>)|00>/√2
  unnamed : |ψ> = (|00> + |11>)/√2
  00_collapsed : |00_collapsed>
  11_collapsed : |11_collapsed>

  [*] --> 00
  00_collapsed --> [*]
  11_collapsed --> [*]

  00 --> 0 : prepare_H / apply_H_on_q0
  0 --> unnamed : entangle / apply_CNOT_q0_to_q1
  unnamed --> 00_collapsed : measure_done [prob_collapse('00')] / set_outcome_0
  unnamed --> 11_collapsed : measure_done [prob_collapse('11')] / set_outcome_1

  note right of 00
    Verification Rules:
    - unitarity: all gates preserve norm
    - entanglement: final state must have Schmidt rank >1 before measure
    - completeness: all possible collapses covered (no missing branches)
    - no_cloning: no copy ops allowed
  end note

---

Compile to OpenQASM 3.0

$ q-orca compile qasm examples/bell-entangler.q.orca.md

// Generated by Q-Orca compiler
// Machine: BellEntangler
OPENQASM 3.0;
include "stdgates.inc";

qubit[2] q;
bit[2] c;

int outcome = -1;

// Gate sequence derived from state machine transitions
// |00> -> |+0> via prepare_H
h q[0];
// |+0> -> |ψ> via entangle
cx q[0], q[1];
// |ψ> -> |00_collapsed> via measure_done
// |ψ> -> |11_collapsed> via measure_done

// Measurement
c[0] = measure q[0];
c[1] = measure q[1];

---

Simulate with Qiskit

Analytic (statevector) — fidelity + entanglement verification:

$ q-orca simulate examples/bell-entangler.q.orca.md --run

  Machine: BellEntangler
  Success: True
  Probabilities:
    00: 50.00%
    01: 0.00%
    10: 0.00%
    11: 50.00%
  QuTiP Verification:
    Unitarity: VERIFIED
    Entanglement: VERIFIED
    Schmidt Rank: 2

Probabilistic (shots) — observed counts:

$ q-orca simulate examples/bell-entangler.q.orca.md --run --shots 512

  Machine: BellEntangler
  Success: True
  Counts: {'11': 269, '00': 243}

JSON output (useful for tooling):

$ q-orca simulate examples/bell-entangler.q.orca.md --run --json

{
  "machine": "BellEntangler",
  "success": true,
  "probabilities": {
    "00": 0.5,
    "01": 0.0,
    "10": 0.0,
    "11": 0.5
  },
  "counts": null,
  "qutipVerification": {
    "unitarityVerified": true,
    "entanglementVerified": true,
    "schmidtRank": 2,
    "errors": []
  }
}

---

Generated Qiskit script snippet

# Generated by Q-Orca compiler
# Machine: BellEntangler

from qiskit import QuantumCircuit
from qiskit.quantum_info import Statevector, Operator
from qiskit.providers.basic_provider import BasicSimulator

qubit_count = 2
qc = QuantumCircuit(2)

# Gate sequence from state machine
qc.h(0)        # |00> --prepare_H--> |+0>
qc.cx(0, 1)    # |+0> --entangle--> |ψ>

# Simulation (analytic)
sv = Statevector(qc)
probs = sv.probabilities()
# ...

# QuTiP Verification
unitary_matrix = Operator(qc).data.tolist()
U = np.array(unitary_matrix)
# Unitarity: U U† ≈ I
# Entanglement: Schmidt rank across Bell partition

---

MCP Server

Q-Orca includes an MCP (Model Context Protocol) server that exposes all skills as tools for AI clients like Claude Code.

Setup

# Install with MCP dependencies
pip install -e ".[mcp]"

# Or install with all dependencies (quantum + MCP)
pip install -e ".[all]"

Running the MCP Server

# Start the MCP server (uses stdio transport)
q-orca-mcp

# Or via Python module
python -m q_orca.mcp_server

Claude Code Configuration

Add to your Claude Code settings (~/.claude/settings.json or project .claude.json):

{
  "mcpServers": {
    "q-orca": {
      "command": "q-orca-mcp",
      "cwd": "/path/to/your/project"
    }
  }
}

Available MCP Tools

Tool	Description
parse_machine	Parse a Q-Orca machine and return structure as JSON
verify_machine	Run 5-stage verification pipeline
compile_machine	Compile to Mermaid, QASM, or Qiskit
generate_machine	Generate quantum machine from natural language spec
refine_machine	Fix verification errors using LLM
simulate_machine	Run Qiskit simulation
server_status	Get server version and LLM config

LLM Provider Configuration

ORCA_API_KEY is the universal key — it works for any provider:

# Universal API key (works for any provider)
export ORCA_API_KEY=your-api-key

# Optional overrides
export ORCA_PROVIDER=anthropic   # anthropic, openai, minimax, ollama, grok
export ORCA_MODEL=claude-sonnet-4-6
export ORCA_MAX_TOKENS=4096
export ORCA_TEMPERATURE=0.7

Or via a YAML config file (orca.yaml or .orca.yaml in your project):

# Anthropic (default)
provider: anthropic
model: claude-sonnet-4-6
api_key: ${ORCA_API_KEY}

# MiniMax
provider: minimax
model: MiniMax-M2.7
api_key: ${ORCA_API_KEY}

Provider-specific keys (ANTHROPIC_API_KEY, MINIMAX_API_KEY, OPENAI_API_KEY) are also supported as fallbacks.

---

Architecture

flowchart TD
    subgraph Input
        MD[".q.orca.md file"]
        NL[Natural Language]
    end

    MD --> Parser

    subgraph Parser
        MP[markdown_parser.py<br/>Two-phase parse]
    end

    Parser --> AST[AST: QMachineDef]

    subgraph "Verifier (5 stages)"
        V1[structural.py<br/>Reachability, deadlocks, orphans]
        V2[completeness.py<br/>(state, event) coverage]
        V3[determinism.py<br/>Guard mutual exclusion]
        V4[quantum.py<br/>Unitarity, no-cloning, entanglement]
        V4D[dynamic.py<br/>QuTiP: Schmidt rank, entropy]
        V5[superposition.py<br/>Superposition coherence leak]
        V1 --> V2 --> V3 --> V4 --> V4D --> V5
    end

    AST --> Verifier
    Verifier --> VResult{Valid?}

    VResult -->|Yes| Compiler
    VResult -->|No| Refine[refine_skill<br/>LLM fix loop]

    Refine -->|Fixed source| Parser
    NL --> Generate[generate_skill<br/>LLM generation]
    Generate -->|Raw .q.orca.md| Parser

    subgraph Compiler
        CM[Mermaid]
        CQ[QASM 3.0]
        CK[Qiskit script]
    end

    Compiler --> MermaidDiagram[Rendered state diagram]
    Compiler --> QASMCode[Quantum circuit code]
    Compiler --> QiskitScript[Python simulation]

    QiskitScript --> Runtime[Python runtime]
    Runtime --> SimResult[Counts, Probabilities, Fidelity]

    style Verifier fill:#1b4f72,color:#fff
    style Compiler fill:#27ae60,color:#fff
    style Runtime fill:#8e44ad,color:#fff
    style NL fill:#f39c12,color:#fff

Directory structure

q_orca/
├── __init__.py            # Package exports
├── ast.py                 # AST dataclasses
├── cli.py                 # CLI entrypoint
├── skills.py              # Skill functions (parse, verify, compile, generate, refine)
├── tools.py               # MCP tool JSON schemas
├── mcp_server.py          # MCP server (stdio JSON-RPC)
├── parser/
│   └── markdown_parser.py # Two-phase markdown parser
├── verifier/
│   ├── types.py           # Verification result types
│   ├── structural.py      # Reachability, deadlocks, orphans
│   ├── completeness.py    # (state, event) coverage
│   ├── determinism.py     # Guard mutual exclusion
│   ├── quantum.py         # Unitarity, no-cloning, entanglement
│   ├── superposition.py   # Superposition coherence leak
│   └── dynamic.py         # QuTiP circuit simulation
├── compiler/
│   ├── mermaid.py         # Mermaid state diagram
│   ├── qasm.py            # OpenQASM 3.0
│   └── qiskit.py          # Qiskit Python script
├── llm/
│   ├── provider.py        # Abstract LLM provider interface
│   ├── anthropic.py       # Anthropic provider
│   ├── openai.py          # OpenAI provider
│   ├── minimax.py         # MiniMax provider
│   ├── ollama.py          # Ollama provider
│   └── grok.py            # Grok provider
├── config/
│   ├── loader.py          # YAML/env config loader
│   └── types.py           # Config types
└── runtime/
    ├── types.py           # Simulation result types
    └── python.py          # Python subprocess runner + simulation

---

Roadmap

Near-term

• Parameterized gates — Rx(θ), Ry(θ), Rz(θ) with symbolic angles in the Markdown action table and invariant expressions (e.g. theta = pi/4)
• Noise models — depolarizing, amplitude damping, thermal noise in ## context; propagate into Qiskit noise simulation
• QASM 3.0 import — parse existing .qasm files and lift them into Q-Orca state machines

Longer-term

• Hybrid classical/quantum transitions — classical control flow between quantum sub-circuits (mid-circuit measurement + feedforward)
• Multi-machine composition — link two machines via shared qubits or classical channels, verified jointly
• VS Code extension — syntax highlighting, inline verification on save, Mermaid preview