limen-compiler 0.8.3

A physics-aware compiler stack for translating classical optimization problems into native quantum and analog substrates.

LIMEN

Where classical problems cross into physical computation.

LIMEN is a physics aware compiler stack for translating classical optimization problems into native quantum and analog substrates. It sits at the threshold between human readable problem specifications and the physical hardware that computes them providing a deterministic, reproducible compilation pipeline with measurable confidence in every result.

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The Problem

Physics native hardware exists. Quantum annealers, gate-model processors, and neutral-atom arrays are accessible today. But using them requires deep expertise in Hamiltonian formulation, hardware-specific embedding, and penalty coefficient engineering knowledge that lives in physics departments, not software teams.

The result: the same problem formulated by two different engineers produces two different encodings. Results vary between hardware generations. Confidence in outputs is largely taken on faith. And every new hardware vendor requires rebuilding the entire toolchain from scratch.

LIMEN fixes this.

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What LIMEN Does

LIMEN provides five things that don't exist together anywhere today:

1. A deterministic logical graph IR A hardware-agnostic intermediate representation for optimization problems. The same problem always compiles the same way. Swap the backend — D-Wave, IBM, classical simulator — without touching your formulation.

2. A lexicographic compiler Single-pass, deterministic compilation from logical IR to physical encoding. No heuristic randomness. No undocumented magic. Reproducible output every time.

3. Hardware adapters Clean interfaces to D-Wave (via Ocean SDK) and IBM Quantum (via Qiskit) out of the box, plus Open Quantum cross-vendor submission and a dormant Azure Quantum adapter for Atom Computing devices (limen.backends.azure_atom — unit-tested but not yet hardware-validated). New backends are first-class citizens: write an adapter, plug it in.

4. A gate-model pipeline with error-correction certification An end-to-end path from QUBO to a certified result: QAOA compilation, exact statevector simulation, classical optimality verification, and a gate-executed surface-code ECC round-trip — all offline, no QPU required. Returns a single EndToEndCertificate with solution, optimality flag, logical error rate, and ECC metadata.

5. A probabilistic validator For every compiled problem, LIMEN produces a confidence bound — not a proof of correctness, but a measurable signal. Small-instance classical verification, cross-architecture sampling, and distribution analysis tell you how much to trust the result.

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Workspace Organization

To help navigate the files and components of LIMEN, see the Directory Map which labels and defines the role of every directory, script, and source file in the repository.

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Architecture

                         [ Domain Problem ]
                                 │
                                 ▼
                 ┌───────────────────────────────┐
                 │ SEMANTIC FRONTEND             │
                 │ PyQUBO · OpenFermion · PDE    │
                 └──────────────┬────────────────┘
                                │  LogicalGraph IR
             ┌──────────────────┴──────────────────┐
             │  analog / annealing track            │  gate-model track
             ▼                                      ▼
┌────────────────────────────┐        ┌─────────────────────────────┐
│ LEXICOGRAPHIC COMPILER     │        │ QAOA COMPILER               │
│ Deterministic · fixed seed │        │ QUBO → Ising → CircuitIR    │
│ Greedy minor-embedding     │        │ Grid-search parameter opt.  │
└────────────┬───────────────┘        └──────────────┬──────────────┘
             │  PhysicalEncoding                      │  CircuitIR
             ▼                                        ▼
┌────────────────────────────┐        ┌─────────────────────────────┐
│ PROBABILISTIC VALIDATOR    │        │ STATEVECTOR SIMULATOR       │
│ Brute-force ≤ 20 vars      │        │ Exact · Rust-backed         │
│ Confidence bounds          │        │ No sampling noise           │
└────┬───────────────────────┘        └──────────────┬──────────────┘
     │                                               │  probs + solution
     ├── open-loop ──► D-Wave · IBM · Simulator      ▼
     │                                ┌─────────────────────────────┐
     └── co-design ──►                │ SURFACE-CODE ECC            │
         ┌──────────────────────┐     │ d=3 rotated patch           │
         │ STACKELBERG LOOP     │     │ Gate-executed syndrome      │
         │ κ scoring · Rust     │     │ Lookup decoder              │
         │ Adaptive learning    │     └──────────────┬──────────────┘
         └──────────┬───────────┘                    │  LogicalErrorCertificate
                    ▼                                 │
         ┌──────────────────────┐                    │
         │ PORTFOLIO COMPILER   │                    │
         │ Multi-backend rank   │                    │
         │ Runtime switching    │                    │
         └──────────┬───────────┘                    │
                    ▼                                 │
         ┌──────────────────────┐                    │
         │ HAMILTONIAN IR       │◄───────────────────┘
         │ Z-basis Ising        │  EndToEndCertificate
         │ Substrate-agnostic   │  (solution · optimality ·
         └──────┬───────────────┘   logical error rate ·
                │                   distributed compilation)
       ┌────────┴────────┐
       ▼                 ▼
┌─────────────┐    ┌───────────────┐
│ NEUTRAL-ATOM│    │ PHOTONIC (GBS)│
│ Rydberg     │    │ CV optical    │
│ [Thm 2,3 OK]│    │ [Thm 4 OK]    │
└─────────────┘    └───────────────┘

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Quantum Communication

LIMEN's quantum-channel module also runs and certifies real-hardware protocols, not just optimization circuits. As a cross-layout fidelity case study, the standard 3-qubit teleportation circuit was submitted to ibm_kingston on two physical qubit mappings that share the state and Alice qubits but differ in the Bob relay qubit: q[10,11,12] (job d8u5qqkbp3hs7385g0mg) and q[10,11,18] (job d8u6b6kbp3hs7385gil0), 1000 shots each. The q[10,11,12] layout achieved fidelity 0.954 versus 0.938 for q[10,11,18] — a 1.6 percentage-point gap — isolating the effect of Bob relay qubit choice on end-to-end teleportation fidelity while holding the state and Alice qubits fixed, and demonstrating LIMEN's ability to certify protocol performance sensitivity to physical qubit mapping on real NISQ hardware. Full counts, timestamps, and verified job layouts: results/teleport_summary.json.

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Multi-Node Coordination

LIMEN instances can discover each other and exchange hardware calibration state over a gRPC Coordination service (limen.distributed): each node advertises its node_id and the device IDs it serves, registers with a static list of known peers on startup, and can pull a peer's HardwareDeltaModel on demand via SyncCalibration. On top of this substrate, a QUBO can be compiled across nodes: the logical graph is partitioned, each partition is dispatched to a peer via the CompilePartition RPC, and the returned encodings are merged into a single encoding energetically equivalent to a one-shot local compile. The end-to-end pipeline exposes this directly — pass server_addresses=[...] to run_pipeline (see Getting Started). See limen/docs/architecture.md for the design and limen/distributed/ for the implementation. Requires the distributed extra: pip install limen[distributed].

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What LIMEN Is Not

LIMEN is not a general-purpose quantum computing framework or a user-facing circuit runner. It does not replace Qiskit or Cirq. Its job is translation and certification, not general quantum execution.

LIMEN does include a pure-Python statevector simulator (limen.gates.simulator), but its purpose is narrow: it provides an exact, dependency-free backend for offline ECC certification and QAOA parameter validation — without requiring a QPU or an SDK installation. It is capped at roughly 14 logical qubits before memory pressure becomes significant. It is not intended for production circuit workloads.

LIMEN does not claim to solve NP-hard problems. It claims to translate them correctly and reproducibly onto hardware that attempts to solve them, and to give you a measurable signal about how much to trust the result.

The analog substrate layer (BEC, photonic, continuous-variable) is defined as an interface not yet fully implemented. The mathematics required for a constructive universality theorem on those substrates does not yet exist. When it does, LIMEN will be ready to receive it.

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Roadmap

Phase 1 — Core ✓

• Logical graph IR schema
• PyQUBO frontend adapter
• Lexicographic compiler (D-Wave backend)
• Probabilistic validator (small-instance classical verification)
• IBM Qiskit backend adapter

Phase 2 — Co-Design Loop (Complete)

• Stackelberg co-design loop (joint penalty + embedding optimization)
• Robust equilibrium selection with calibration margin
• Portfolio compilation with runtime switching conditions

Phase 3 — Analog Interface

• Hamiltonian IR interface specification
• Classical Ising simulation backend (exact diagonalisation, ≤ 20 sites)
• Neutral-atom heuristic backend (van der Waals layout, Rydberg parameters)
• Neutral-atom LHZ parity-encoding fallback (Theorem 3 — automatic for non-natively-realizable targets)
• Photonic GBS-inspired backend (Arrazola-Bromley adjacency encoding)
• Restricted-class universality results documented and implemented (Theorems 1-5, limen/docs/universality_theorem.md); a fully general universality theorem for arbitrary (non-diagonal, time-dependent) analog Hamiltonians remains open research and is not claimed

Phase 4 — Multi-Node Distributed Architecture (Complete)

• Milestone 1: node identity, registry, and gRPC coordination service (discovery, heartbeat, calibration sync)
• Milestone 2: distributed QUBO partitioning across nodes (CompilePartition RPC, wired into run_pipeline)
• Milestone 3: classical feedforward transport over QuantumChannel — TransportFeedforward RPC carries Alice's Bell-measurement bits to a peer node, which applies Bob's correction and echoes it back; run_distributed_feedforward_teleport measures real round-trip latency and feeds it into ChannelDeltaModel

Phase 5 — Gate-Model Track & ECC Certification (Complete)

• QAOA compiler: QUBO → Ising → CircuitIR with grid-search parameter optimisation
• Pure-Python statevector simulator (exact, no external SDK, ~14-qubit ceiling)
• Gate-model synthesis: arbitrary 1-qubit unitary decomposition into (rz, ry, rz) + cx
• Distance-3 rotated surface-code ECC: stabilisers, syndrome extraction, lookup decoder
• Gate-executed ECC round-trip: syndrome circuit run on the statevector simulator
• EndToEndCertificate: solution + optimality + QAOA success rate + logical error rate + ECC metadata
• run_pipeline end-to-end orchestrator (local and distributed)
• Physics-validation test suite (random QUBO vs brute-force, error-rate sweep, distance scaling, weight-2 uncorrectable boundary, BB84 eavesdrop detection)
• QPU backend integration for pipeline execution step: backend="aer"/"qpu" (real IBM hardware via Qiskit Runtime) and backend="dwave" (real D-Wave QPU or simulated annealer) are all wired into run_pipeline
• Classical feedforward transport over QuantumChannel (Milestone 3, see Phase 4 above)

Phase 6 — Budget Router & Fleet Cost Model (In Progress)

• Budget router (limen.router): deterministic fidelity-tier planning — tier, backend, cutting strategy, ECC allocation, and shot count chosen against an explicit credit budget before submission
• Cost-model seeding from run history: scan_results()/apply_history() fold cached results/*.json records into fleet backend profiles offline
• Calibration seeding from live hardware: fetch_backend_calibration() (IBM Runtime gate/readout error) + offline scan_calibration()/apply_calibration(); route() prefers calibrated physical_error_rate over hardcoded defaults
• Crash-resilient QPU job lifecycle: submission decoupled from result-waiting, on-disk state machine (limen.router.job_state), transient-error-only retry, no auto-resubmission of errored/cancelled jobs
• First hardware validation: ibm_kingston Tier 2 run (job d965qgotcv6s73djc1l0, results/router_tier2_kingston_d965qgotcv6s73djc1l0.json)
• Router-driven multi-backend dispatch: run_route_request() (limen.pipeline) is the zero-manual-steps entry point — QUBO + budget in, certified answer out. Builds the informed fleet, routes, and executes; IBM QPU plans go through the decoupled submit → poll → certify chain
• Cut-circuit bridge: RoutePlan.use_cutting plans (problem too large for any single backend) dispatch through run_cut_route_request() (limen.cutting), returning a CuttingCertificate reconstructed from per-qubit <Z_i> marginals and certified with the same ECC term run_pipeline uses
• gRPC peer auto-discovery: run_route_request() falls back to LIMEN_KNOWN_PEERS for distributed compilation when server_addresses is omitted
• Substrate-aware routing: ProblemProfile/BackendProfile.substrate_affinity break ties between backends already tied on cost/capacity/validation
• Co-design history loop: codesign_from_history() (limen.codesign) seeds a fresh Stackelberg run from the best prior chain-strength in results/; standalone by design (see docs/ROADMAP.md)
• Azure Quantum / Atom Computing adapter hardware validation (limen.backends.azure_atom is import-clean and unit-tested but dormant — never run against a live Azure workspace; excluded from DEFAULT_FLEET until validated)

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Getting Started

pip install limen

from limen import from_qubo_dict, compile_lexicographic, default_hardware_graph, validate

# Define your problem as a QUBO dict
graph = from_qubo_dict({
    ('x0', 'x0'): -1.0,
    ('x1', 'x1'): -1.0,
    ('x0', 'x1'):  2.0,
})

# Compile to a physical encoding
encoding = compile_lexicographic(graph, default_hardware_graph(4))

# Validate confidence
result = validate(encoding, runs=1000)
print(result.confidence)  # 0.0 – 1.0

End-to-end pipeline (gate-model + ECC, optionally distributed)

run_pipeline threads a QUBO through the whole stack — QAOA compilation, statevector execution, classical optimality check, and a surface-code logical-error certificate — and returns a single EndToEndCertificate:

import limen

cert = limen.run_pipeline(
    {('x0', 'x0'): -1.0, ('x1', 'x1'): -1.0, ('x0', 'x1'): 2.0},
    physical_error_rate=0.01,
)
print(cert.solution, cert.is_optimal, cert.logical_error_rate)

# Compile across peer nodes over the Coordination CompilePartition RPC
# (requires the `distributed` extra and reachable peers):
cert = limen.run_pipeline(
    {('x0', 'x0'): -1.0, ('x1', 'x1'): -1.0, ('x0', 'x1'): 2.0},
    server_addresses=["127.0.0.1:50051"],
    num_partitions=2,
)
print(cert.distributed_compilation)

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Design Principles

Determinism first. The same input always produces the same encoding. Reproducibility is not optional for scientific infrastructure.

Honest about limits. LIMEN reports confidence bounds, not correctness proofs. The validation loop is probabilistic by design, not by accident.

Hardware agnostic formulation, hardware explicit compilation. The logical IR knows nothing about hardware topology. The compiler knows everything. The boundary between them is hard and intentional.

Open core. The compiler, IR, validator, and hardware adapters are source-available under the Elastic License 2.0. Production features (Stackelberg co-design, portfolio compilation, drift-aware margin selection) will be offered under a commercial license for enterprise deployments.

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Contributing

LIMEN is early. The most valuable contributions right now are:

• Problem formulations — real optimization problems encoded in the logical IR
• Hardware adapter implementations for new backends
• Validator improvements — better small-instance solvers, additional confidence metrics
• Benchmarking data from real hardware runs

See CONTRIBUTING.md for details.

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License

Elastic License 2.0 (ELv2) — see LICENSE for details.

LIMEN is source-available: anyone can read, fork, and use the code for research and internal purposes. ELv2 prohibits offering LIMEN as a hosted or managed service without a commercial license. Use is also subject to the restrictions in ACCEPTABLE_USE.md, which prohibits weapons development, mass surveillance, unauthorized cryptanalysis, and attacks on critical infrastructure.

As of v0.3.1, LIMEN changed from Apache 2.0 to ELv2. Copies obtained under Apache 2.0 retain their Apache 2.0 rights; all new versions are licensed under ELv2.

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Status

v0.8.3 — Phase 1 complete · Phase 2 complete · Phase 3 substantially implemented · Phase 4 complete · Phase 5 complete · Phase 6 in progress · Hardware validated. Core IR, compiler, validator, PyQUBO frontend, D-Wave and Qiskit backend adapters shipped and tested. Stackelberg co-design loop operational with Rust-backed κ scoring, stability-penalised learning rate, and portfolio compilation across backend slots. Chain-break-fraction feedback is wired into the loop on both hardware paths: ibm_noise_fn (real-hardware total variation distance) drives examples/ibm_codesign_qpu.py, validated against ibm_kingston, and dwave_chain_break_fn (real chain-break fraction) now drives the equivalent closed loop against a live D-Wave QPU in examples/dwave_codesign_qpu.py. With no callback supplied, chain-break fraction defaults to 0.0 (simulation mode) — this is documented behaviour, not an inactive feature. Analog interface layer implemented: classical Ising simulator (exact diagonalisation, ≤20 sites), neutral-atom heuristic backend (van der Waals layout, Rydberg parameters), photonic GBS backend (Arrazola-Bromley adjacency encoding), HardwareDeltaModel calibration layer (per-site detuning, per-pair coupling, and global Rabi-drive correction, each with a pure-Python fallback alongside the Rust-backed implementation). The neutral-atom geometric embeddability check (Theorem 2 geometry condition) likewise always runs — numpy when available, a pure-Python Jacobi eigenvalue fallback otherwise — consistent with LIMEN's zero-mandatory-dependency core (dependencies = [] in pyproject.toml). Restricted-class universality results (Theorems 1-5) documented in limen/docs/universality_theorem.md; a fully general universality theorem for arbitrary (non-diagonal, time-dependent) analog Hamiltonians remains open research and is not claimed — nor is off-diagonal (X/Y) or time-dependent Hamiltonian support yet implemented in any backend.

Gate-model track fully operational: QUBO → QAOA compilation → exact statevector simulation → surface-code (d=3) ECC syndrome extraction and logical-error certification, end-to-end with no external SDK required. run_pipeline returns a single EndToEndCertificate with solution, optimality flag, success probability, per-qubit logical error rate, and ECC roundtrip verification. Distributed compilation over gRPC (CompilePartition RPC) wired into run_pipeline(server_addresses=[...]). The pipeline's execution step now also routes to real backends — backend="aer"/"qpu" (IBM Quantum hardware) and backend="dwave" (real D-Wave QPU or simulated annealer) — alongside the default offline statevector simulator.

Multi-node coordination is now end-to-end: beyond discovery and calibration sync, nodes can transport the classical feedforward bits of a teleportation protocol to a peer over gRPC (TransportFeedforward RPC) and measure real round-trip latency against the qubit's T2 coherence time (run_distributed_feedforward_teleport).

First real hardware validation: IBM ibm_kingston (Heron R2, 156 qubits), June 9 2026. Benchmark results in benchmarks/RESULTS.md.

Rust acceleration layer (v0.8.x): every enumeration- or sampling-shaped hot loop in the library is now Rust-backed via the limen_core PyO3 extension, each behind a try: from limen.limen_core import ... fast path with an equivalent pure-Python fallback: QUBO spectrum enumeration, the validator's noisy-run simulation (simulate_qubo_runs, rayon-parallel), surface-code lookup-table construction and logical-error certification (build_ecc_lookup_table / logical_failure_probability — distance-5 decoding drops from intractable-in-Python to seconds), the VRP one-hot QUBO builder (vrp_qubo_terms), statevector simulation, κ scoring, and the analog delta corrections. The full test suite runs ~8x faster with the extension built; hardware SDK adapters, the gRPC distributed layer, and pipeline orchestration deliberately stay Python (I/O-bound glue, no compute of their own). Build with maturin develop --release.

Circuit cutting (gate-model): limen/cutting + the Rust limen_core::cutting module split a wide circuit into sub-circuits via quasi-probability decomposition and reconstruct the original expectation value from real per-subcircuit sampler counts (limen/cutting/dispatch.py, limen/cutting/reconstruct.py, src/cutting/reconstruct.rs). Validated end-to-end against a plain AerSimulator run in examples/cutting_smoke_test.py.

Budget router (v0.8.3): limen.router plans a QUBO run before any credit is spent — deterministic fidelity-tier selection (backend, cutting strategy, ECC allocation, shot count) against an explicit budget. Backend profiles are seeded from real run history (limen.router.history) and from live IBM calibration snapshots (limen.router.calibration); the first ibm_kingston calibration (physical error rate 2.586e-2) is 25x closer to measured Tier 2 behavior than the previous hardcoded 1e-3. QPU job submission is decoupled from result-waiting with an on-disk state machine (limen.router.job_state), so a crashed terminal or a long maintenance-window queue can't strand a completed job. Hardware-validated on ibm_kingston (job d965qgotcv6s73djc1l0).

387 tests passing, 3 skipped (skips are environment-gated: optional SDKs not installed in this dev environment). Zero regressions.

If you are building in this space and want to collaborate, open an issue.

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LIMEN — Latin for threshold. The boundary between two states.