# ruQu: Quantum Execution Intelligence Engine

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ruv.io RuVector

Modules Lines Backends P99 Latency License Rust

A full-stack quantum computing platform in pure Rust: simulate, optimize, execute, correct, and verify quantum workloads across heterogeneous backends.

From circuit construction to hardware dispatch. From noise modeling to error correction. From approximate simulation to auditable science.

Overview • Layers • Modules • Try It • Coherence Gate • Tutorials • ruv.io

--- ## Platform Overview ruQu is not a simulator. It is a **quantum execution intelligence engine** -- a layered operating stack that decides *how*, *where*, and *whether* to run quantum workloads. Most quantum frameworks do one thing: simulate circuits. ruQu does five: | Capability | What It Means | How It Works | |------------|--------------|--------------| | **Simulate** | Run circuits on the right backend | Cost-model planner selects StateVector, Stabilizer, TensorNetwork, or Clifford+T based on circuit structure | | **Optimize** | Compile circuits for real hardware | Transpiler decomposes to native gate sets, routes qubits to physical topology, cancels redundant gates | | **Execute** | Dispatch to IBM, IonQ, Rigetti, Braket | Hardware abstraction layer with automatic fallback to local simulation | | **Correct** | Decode errors in real time | Union-find and subpolynomial partitioned decoders with adaptive code distance | | **Verify** | Prove results are correct | Cross-backend comparison, statistical certification, tamper-evident audit trails | ### What Makes It Different **Hybrid decomposition.** Large circuits are partitioned by entanglement structure -- Clifford-heavy regions run on the stabilizer backend (millions of qubits), low-entanglement regions run on tensor networks, and only the dense entangled core hits the exponential statevector. One 200-qubit circuit becomes three tractable simulations stitched probabilistically. **No mocks.** Every module runs real math. Noise channels apply real Kraus operators. Decoders run real union-find with path compression. The Clifford+T backend performs genuine Bravyi-Gosset stabilizer rank decomposition. The benchmark suite doesn't assert "it works" -- it proves quantitative advantages. **Coherence gating.** ruQu's original innovation: real-time structural health monitoring using boundary-to-boundary min-cut analysis. Before any operation, the system answers: "Is it safe to act?" This turns quantum computers from fragile experiments into self-aware machines. --- ## The Five Layers ``` Layer 5: Proof Suite benchmark.rs | Layer 4: Theory subpoly_decoder.rs, control_theory.rs | Layer 3: QEC Control Plane decoder.rs, qec_scheduler.rs | Layer 2: SOTA Differentiation planner.rs, clifford_t.rs, decomposition.rs | Layer 1: Scientific Instrument noise.rs, mitigation.rs, transpiler.rs, (9 modules) hardware.rs, qasm.rs, replay.rs, witness.rs, confidence.rs, verification.rs | Layer 0: Core Engine circuit.rs, gate.rs, state.rs, backend.rs, (existing) stabilizer.rs, tensor_network.rs, simulator.rs, simd.rs, optimizer.rs, types.rs, error.rs, mixed_precision.rs, circuit_analyzer.rs ``` --- ## Module Reference ### Layer 0: Core Engine (13 modules) The foundation: circuit construction, state evolution, and backend dispatch. | Module | Lines | Description | |--------|------:|-------------| | `circuit.rs` | 185 | Quantum circuit builder with fluent API | | `gate.rs` | 204 | Universal gate set: H, X, Y, Z, S, T, CNOT, CZ, SWAP, Rx, Ry, Rz, arbitrary unitaries | | `state.rs` | 453 | Complex128 statevector with measurement and partial trace | | `backend.rs` | 462 | Backend trait + auto-selector across StateVector, Stabilizer, TensorNetwork | | `stabilizer.rs` | 774 | Gottesman-Knill tableau simulator for Clifford circuits (unlimited qubits) | | `tensor_network.rs` | 863 | MPS-based tensor network with configurable bond dimension | | `simulator.rs` | 221 | Unified execution entry point | | `simd.rs` | 469 | AVX2/NEON vectorized gate kernels | | `optimizer.rs` | 94 | Gate fusion and cancellation passes | | `mixed_precision.rs` | 756 | f32/f64 adaptive precision for memory/speed tradeoff | | `circuit_analyzer.rs` | 446 | Static analysis: gate counts, Clifford fraction, entanglement profile | | `types.rs` | 263 | Shared type definitions | | `error.rs` | -- | Error types | ### Layer 1: Scientific Instrument (9 modules) Everything needed to run quantum circuits as rigorous science. | Module | Lines | Description | |--------|------:|-------------| | `noise.rs` | 1,174 | Kraus channel noise: depolarizing, amplitude damping (T1), phase damping (T2), readout error, thermal relaxation, crosstalk (ZZ coupling) | | `mitigation.rs` | 1,275 | Zero-Noise Extrapolation via gate folding + Richardson extrapolation; measurement error correction via confusion matrix inversion; Clifford Data Regression | | `transpiler.rs` | 1,210 | Basis gate decomposition (IBM/IonQ/Rigetti gate sets), BFS qubit routing on hardware topology, gate cancellation optimization | | `hardware.rs` | 1,764 | Provider trait HAL with adapters for IBM Quantum, IonQ, Rigetti, Amazon Braket + local simulator fallback | | `qasm.rs` | 967 | OpenQASM 3.0 export with ZYZ Euler decomposition for arbitrary single-qubit unitaries | | `replay.rs` | 556 | Deterministic replay engine -- seeded RNG, state checkpoints, circuit hashing for exact reproducibility | | `witness.rs` | 724 | SHA-256 hash-chain witness logging -- tamper-evident audit trail with JSON export and chain verification | | `confidence.rs` | 932 | Wilson score intervals, Clopper-Pearson exact bounds, chi-squared goodness-of-fit, total variation distance, shot budget calculator | | `verification.rs` | 1,190 | Automatic cross-backend comparison with statistical certification (exact/statistical/trend match levels) | ### Layer 2: SOTA Differentiation (3 modules) Where ruQu separates from every other framework. | Module | Lines | Description | |--------|------:|-------------| | `planner.rs` | 1,393 | **Cost-model circuit router** -- predicts memory, runtime, fidelity for each backend. Selects optimal execution plan with verification policy and mitigation strategy. Entanglement budget estimation. | | `clifford_t.rs` | 996 | **Extended stabilizer simulation** via Bravyi-Gosset low-rank decomposition. T-gates double stabilizer terms (2^t scaling). Bridges the gap between Clifford-only (unlimited qubits) and statevector (32 qubits). | | `decomposition.rs` | 1,409 | **Hybrid circuit partitioning** -- builds interaction graph, finds connected components, applies spatial/temporal decomposition. Classifies segments by gate composition. Probabilistic result stitching. | ### Layer 3: QEC Control Plane (2 modules) Real-time quantum error correction infrastructure. | Module | Lines | Description | |--------|------:|-------------| | `decoder.rs` | 1,923 | **Union-find decoder** O(n*alpha(n)) + partitioned tiled decoder for sublinear wall-clock scaling. Adaptive code distance controller. Logical qubit allocator for surface code patches. Built-in benchmarking. | | `qec_scheduler.rs` | 1,443 | Surface code syndrome extraction scheduling, feed-forward optimization (eliminates unnecessary classical dependencies), dependency graph with critical path analysis. | ### Layer 4: Theoretical Foundations (2 modules) Provable complexity results and formal analysis. | Module | Lines | Description | |--------|------:|-------------| | `subpoly_decoder.rs` | 1,207 | **HierarchicalTiledDecoder**: recursive multi-scale tiling achieving O(d^(2-epsilon) * polylog(d)). **RenormalizationDecoder**: coarse-grain syndrome lattice across log(d) scales. **SlidingWindowDecoder**: streaming decode for real-time QEC. **ComplexityAnalyzer**: provable complexity certificates. | | `control_theory.rs` | 433 | QEC as discrete-time control system -- stability conditions, resource optimization, latency budget planning, backlog simulation, scaling laws for classical overhead and logical error suppression. | ### Layer 5: Proof Suite (1 module) Quantitative evidence that the architecture delivers measurable advantages. | Module | Lines | Description | |--------|------:|-------------| | `benchmark.rs` | 790 | **Proof 1**: cost-model routing beats naive and heuristic selectors. **Proof 2**: entanglement budgeting enforced as compiler constraint. **Proof 3**: partitioned decoder shows measurable latency gains vs union-find. **Proof 4**: cross-backend certification with bounded TVD error guarantees. | ### Totals | Metric | Value | |--------|-------| | Total modules | 30 | | Total lines of Rust | 24,676 | | New modules (execution engine) | 20 | | New lines (execution engine) | ~20,000 | | Simulation backends | 5 (StateVector, Stabilizer, TensorNetwork, Clifford+T, Hardware) | | Hardware providers | 4 (IBM Quantum, IonQ, Rigetti, Amazon Braket) | | Noise channels | 6 (depolarizing, amplitude damping, phase damping, readout, thermal, crosstalk) | | Mitigation strategies | 3 (ZNE, MEC, CDR) | | Decoder algorithms | 5 (union-find, tiled, hierarchical, renormalization, sliding-window) | --- ## Coherence Gating ruQu's original capability: a **classical nervous system** for quantum machines. Real-time structural health monitoring that answers one question before every operation: *"Is it safe to act?"* ``` Syndrome Stream --> [Min-Cut Analysis] --> PERMIT / DEFER / DENY | "Is the error pattern structurally safe?" ``` | Decision | Meaning | Action | |----------|---------|--------| | **PERMIT** | Errors scattered, structure healthy | Full-speed operation | | **DEFER** | Borderline, uncertain | Proceed with caution, reduce workload | | **DENY** | Correlated errors, structural collapse risk | Quarantine region, isolate failure | ### Why Coherence Gating Matters **Without ruQu**: Quantum computer runs blind until logical failure -> full reset -> lose all progress. **With ruQu**: Quantum computer detects structural degradation *before* failure -> isolates damaged region -> healthy regions keep running. ### Validated Results | Metric | Result (d=5, p=0.1%) | |--------|---------------------| | Median lead time | 4 cycles before failure | | Recall | 85.7% | | False alarms | 2.0 per 10k cycles | | Actionable (2-cycle mitigation) | 100% | ### Performance | Metric | Target | Measured | |--------|--------|----------| | Tick P99 | <4,000 ns | 468 ns | | Tick Average | <2,000 ns | 260 ns | | Merge P99 | <10,000 ns | 3,133 ns | | Min-cut query | <5,000 ns | 1,026 ns | | Throughput | 1M/sec | 3.8M/sec | | Popcount (1024 bits) | -- | 13 ns (SIMD) | --- ## Try It in 5 Minutes ### Option 1: Add to Your Project ```bash cargo add ruqu --features structural ``` ```rust use ruqu::{QuantumFabric, FabricBuilder, GateDecision}; fn main() -> Result<(), ruqu::RuQuError> { let mut fabric = FabricBuilder::new() .num_tiles(256) .syndrome_buffer_depth(1024) .build()?; let syndrome_data = [0u8; 64]; // From your quantum hardware let decision = fabric.process_cycle(&syndrome_data)?; match decision { GateDecision::Permit => println!("Safe to proceed"), GateDecision::Defer => println!("Proceed with caution"), GateDecision::Deny => println!("Region unsafe"), } Ok(()) } ``` ### Option 2: Run the Interactive Demo ```bash git clone https://github.com/ruvnet/ruvector cd ruvector cargo run -p ruqu --bin ruqu_demo --release -- --distance 5 --rounds 1000 --error-rate 0.01 ``` ### Option 3: Use the Quantum Execution Engine (ruqu-core) ```rust use ruqu_core::circuit::QuantumCircuit; use ruqu_core::planner::{plan_execution, PlannerConfig}; use ruqu_core::decomposition::decompose; // Build a circuit let mut circ = QuantumCircuit::new(10); circ.h(0); for i in 0..9 { circ.cnot(i, i + 1); } // Plan: auto-selects optimal backend let plan = plan_execution(&circ, &PlannerConfig::default()); // Or decompose for multi-backend execution let partition = decompose(&circ, 25); ``` --- ## Feature Flags | Feature | What It Enables | When to Use | |---------|----------------|-------------| | `structural` | Real O(n^{o(1)}) min-cut algorithm | Default -- always recommended | | `decoder` | Fusion-blossom MWPM decoder | Surface code error correction | | `attention` | 50% FLOPs reduction via coherence routing | High-throughput systems | | `simd` | AVX2 vectorized bitmap operations | x86_64 performance | | `full` | All features enabled | Production deployments | --- ## Ecosystem | Crate | Description | |-------|-------------| | [`ruqu`](https://crates.io/crates/ruqu) | Coherence gating + top-level API | | [`ruqu-core`](https://crates.io/crates/ruqu-core) | Quantum execution engine (30 modules, 24K lines) | | [`ruqu-algorithms`](https://crates.io/crates/ruqu-algorithms) | VQE, Grover, QAOA, surface code algorithms | | [`ruqu-exotic`](https://crates.io/crates/ruqu-exotic) | Quantum-classical hybrid algorithms | | [`ruqu-wasm`](https://crates.io/crates/ruqu-wasm) | WebAssembly bindings | --- ## Tutorials
Tutorial 1: Your First Coherence Gate ### Setting Up a Basic Gate ```rust use ruqu::{ tile::{WorkerTile, TileZero, TileReport, GateDecision}, syndrome::DetectorBitmap, }; fn main() { // Create a worker tile (ID 1-255) let mut worker = WorkerTile::new(1); // Create TileZero (the coordinator) let mut coordinator = TileZero::new(); // Simulate a syndrome measurement let mut detectors = DetectorBitmap::new(64); detectors.set(5, true); // Detector 5 fired detectors.set(12, true); // Detector 12 fired println!("Detectors fired: {}", detectors.fired_count()); // Worker processes the syndrome let report = worker.tick(&detectors); println!("Worker report - cut_value: {}", report.local_cut); // Coordinator merges reports and decides let decision = coordinator.merge(&[report]); match decision { GateDecision::Permit => println!("System coherent, proceed"), GateDecision::Defer => println!("Borderline, use caution"), GateDecision::Deny => println!("Structural issue detected"), } } ``` **Key Concepts:** - **WorkerTile**: Processes local patch of qubits - **TileZero**: Coordinates all workers, makes global decision - **DetectorBitmap**: Efficient representation of which detectors fired
Tutorial 2: Understanding the Three-Filter Pipeline ### How Decisions Are Made ruQu uses three filters that must all pass for a PERMIT decision: ``` Syndrome Data -> [Structural] -> [Shift] -> [Evidence] -> Decision | | | Min-cut OK? Distribution E-value stable? accumulated? ``` | Filter | Purpose | Passes When | |--------|---------|-------------| | **Structural** | Graph connectivity | Min-cut value > threshold | | **Shift** | Distribution stability | Recent stats match baseline | | **Evidence** | Accumulated confidence | E-value in safe range |
Tutorial 3: Cryptographic Audit Trail ### Tamper-Evident Decision Logging Every gate decision is logged in a Blake3 hash chain for audit compliance. ```rust use ruqu::tile::{ReceiptLog, GateDecision}; fn main() { let mut log = ReceiptLog::new(); log.append(GateDecision::Permit, 1, 1000000, [0u8; 32]); log.append(GateDecision::Permit, 2, 2000000, [1u8; 32]); log.append(GateDecision::Deny, 3, 3000000, [2u8; 32]); // Verify chain integrity assert!(log.verify_chain(), "Chain should be valid"); // Retrieve specific entry if let Some(entry) = log.get(2) { println!("Decision at seq 2: {:?}", entry.decision); println!("Hash: {:x?}", &entry.hash[..8]); } } ``` **Security Properties:** - Blake3 hashing: fast, cryptographically secure - Chain integrity: each entry links to previous - Constant-time verification: prevents timing attacks
Tutorial 4: Drift Detection for Noise Characterization ### Detecting Changes in Error Rates Over Time Based on arXiv:2511.09491, ruQu can detect when noise characteristics change without direct hardware access. ```rust use ruqu::adaptive::{DriftDetector, DriftProfile}; let mut detector = DriftDetector::new(100); // 100-sample window for sample in samples { detector.push(sample); if let Some(profile) = detector.detect() { match profile { DriftProfile::Stable => { /* Normal operation */ } DriftProfile::Linear { slope, .. } => { /* Compensate for trend */ } DriftProfile::StepChange { magnitude, .. } => { /* Alert: sudden shift */ } DriftProfile::Oscillating { .. } => { /* Periodic noise source */ } DriftProfile::VarianceExpansion { ratio } => { /* Increasing noise */ } } } } ``` | Profile | Indicates | Typical Cause | |---------|-----------|---------------| | **Stable** | Normal | -- | | **Linear** | Gradual degradation | Qubit aging, thermal drift | | **StepChange** | Sudden event | TLS defect, cosmic ray, cable fault | | **Oscillating** | Periodic interference | Cryocooler, 60Hz, mechanical vibration | | **VarianceExpansion** | Increasing chaos | Multi-source interference |
Tutorial 5: Model Export/Import for Reproducibility ### Save and Load Learned Parameters Export trained models as a compact 105-byte binary for reproducibility, testing, and deployment. ``` Offset Size Field ------------------------------ 0 4 Magic "RUQU" 4 1 Version (1) 5 8 Seed (u64) 13 4 Code distance (u32) 17 8 Error rate (f64) 25 40 Learned thresholds (5 x f64) 65 40 Statistics (5 x f64) ------------------------------ Total: 105 bytes ``` ```rust // Export let model_bytes = simulation_model.export(); // 105 bytes std::fs::write("model.ruqu", &model_bytes)?; // Import and reproduce let imported = SimulationModel::import(&model_bytes)?; assert_eq!(imported.seed, original.seed); ```
--- ## Architecture ### System Diagram ``` +----------------------------+ | Quantum Algorithms | (VQE, Grover, QAOA) +-------------+--------------+ | +-----------------------+------------------------+ | | | +-----v------+ +-----------v----------+ +----------v--------+ | Planner | | Decomposition | | Clifford+T | | cost-model | | hybrid partition | | stabilizer rank | | routing | | graph min-cut | | decomposition | +-----+------+ +-----------+-----------+ +----------+--------+ | | | +-----v-----------------------v------------------------v--------+ | Core Backends (existing + enhanced) | | StateVector | Stabilizer | TensorNetwork | MixedPrecision | +-----+-----------------------+------------------------+--------+ | | | +-----v------+ +-----------v----------+ +----------v--------+ | Noise | | Mitigation | | Transpiler | | channels | | ZNE / CDR / MEC | | routing + opt | +------------+ +----------------------+ +-------------------+ | | | +-----v-----------------------v------------------------v--------+ | Scientific Instrument Layer | | Replay | Witness | Confidence | Verification | QASM | +-----------------------------+--------------------------------+ | +-----------------------------v--------------------------------+ | QEC Control Plane | | Decoder | Scheduler | SubpolyDecoder | ControlTheory | +-----------------------------+--------------------------------+ | +-------------v--------------+ | Hardware Providers | | IBM | IonQ | Rigetti | | | Braket | Local Sim | +----------------------------+ ``` ### 256-Tile Fabric (Coherence Gating) ``` +---------------+ | TileZero | | (Coordinator) | +-------+-------+ | +----------------+----------------+ | | | +------+------+ +------+------+ +------+------+ | WorkerTile 1| | WorkerTile 2| |WorkerTile255| | (64KB) | | (64KB) | | (64KB) | +-------------+ +-------------+ +-------------+ | | | [Patch Graph] [Patch Graph] [Patch Graph] [Syndrome Buf] [Syndrome Buf] [Syndrome Buf] [Evidence Acc] [Evidence Acc] [Evidence Acc] ``` --- ## Security | Component | Algorithm | Purpose | |-----------|-----------|---------| | Hash chain | Blake3 | Tamper-evident audit trail | | Token signing | Ed25519 | Unforgeable permit tokens | | Witness log | SHA-256 chain | Execution provenance | | Comparisons | Constant-time | Timing attack prevention | --- ## Application Domains | Domain | How ruQu Helps | |--------|---------------| | **Healthcare** | Longer, patient-specific quantum simulations for protein folding and drug interactions. Coherence gating prevents silent corruption in clinical-grade computation. | | **Finance** | Continuous portfolio risk modeling with real-time stability monitoring. Auditable execution trails for regulated environments. | | **QEC Research** | Full decoder pipeline with 5 algorithms from union-find to subpolynomial partitioned decoding. Benchmarkable scaling claims. | | **Cloud Quantum** | Multi-backend workload routing. Automatic degraded-mode operation via coherence-aware scheduling. | | **Hardware Vendors** | Transpiler targets IBM/IonQ/Rigetti/Braket gate sets. Noise characterization and drift detection without direct hardware access. | --- ## Limitations | Limitation | Impact | Path Forward | |------------|--------|--------------| | Simulation-only validation | Hardware behavior may differ | Hardware partner integration | | Greedy spatial partitioning | Not optimal min-cut | Stoer-Wagner / spectral bisection | | No end-to-end pipeline | Modules exist independently | Compose decompose -> execute -> stitch -> certify | | CliffordT not in classifier | Bridge layer disconnected from auto-routing | Integrate T-rank into planner decisions | | No fidelity-aware stitching | Cut error unbounded | Model Schmidt coefficient loss at partition boundaries | --- ## Roadmap | Phase | Goal | Status | |-------|------|--------| | v0.1 | Core coherence gate with min-cut | Done | | v0.2 | Predictive early warning, drift detection | Done | | v0.3 | Quantum execution engine (20 modules) | Done | | v0.4 | Formal hybrid decomposition with scaling proof | Next | | v0.5 | Hardware integration + end-to-end pipeline | Planned | | v1.0 | Production-ready with hardware validation | Planned | --- ## References ### Academic - [El-Hayek, Henzinger, Li. "Dynamic Min-Cut with Subpolynomial Update Time." arXiv:2512.13105, 2025](https://arxiv.org/abs/2512.13105) - [Bravyi, Gosset. "Improved Classical Simulation of Quantum Circuits Dominated by Clifford Gates." PRL, 2016](https://arxiv.org/abs/1601.07601) - [Google Quantum AI. "Quantum error correction below the surface code threshold." Nature, 2024](https://www.nature.com/articles/s41586-024-08449-y) - [Riverlane. "Collision Clustering Decoder." Nature Communications, 2025](https://www.nature.com/articles/s41467-024-54738-z) - [arXiv:2511.09491 -- Window-based drift estimation for QEC](https://arxiv.org/abs/2511.09491) ### Project - [ADR-QE-001: Quantum Engine Core Architecture](https://github.com/ruvnet/ruvector/blob/main/docs/adr/quantum-engine/ADR-QE-001-quantum-engine-core-architecture.md) - [ADR-QE-015: Execution Engine Module Map](https://github.com/ruvnet/ruvector/blob/main/docs/adr/quantum-engine/) --- ## License MIT OR Apache-2.0 ---

ruQu -- Quantum execution intelligence in pure Rust.

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