ADR-QE-008: Surface Code Error Correction Simulation

Status: Proposed Date: 2026-02-06 Authors: ruv.io, RuVector Team Deciders: Architecture Review Board

Version History

Version	Date	Author	Changes
0.1	2026-02-06	ruv.io	Initial surface code QEC simulation proposal

Context

The Importance of QEC Simulation

Quantum Error Correction (QEC) is the bridge between noisy intermediate-scale quantum (NISQ) devices and fault-tolerant quantum computing. Before deploying error correction on real hardware, every aspect of the QEC stack must be validated through simulation:

Decoder validation: Verify that decoding algorithms (MWPM, Union-Find, neural decoders) produce correct corrections under various noise models
Threshold estimation: Determine the physical error rate below which logical error rate decreases with increasing code distance
Architecture exploration: Compare surface code layouts, flag qubit placements, and scheduling strategies
Noise model development: Test decoder robustness against realistic noise (correlated errors, leakage, crosstalk)

Surface Codes as the Leading Architecture

The surface code is the most promising QEC architecture for superconducting qubit platforms due to:

Property	Value
Error threshold	~1% (highest among practical codes)
Connectivity	Nearest-neighbor only (matches hardware)
Syndrome extraction	Local stabilizer measurements
Decoding	Efficient MWPM, Union-Find in O(n * alpha(n))

Surface Code Layout (Distance-3)

Distance-3 Rotated Surface Code:

Data qubits: D0..D8 (9 total)
X-stabilizers: X0..X3 (4 ancilla qubits)
Z-stabilizers: Z0..Z3 (4 ancilla qubits)

    Z0          Z1
  /    \      /    \
D0 ──── D1 ──── D2
|  X0   |  X1   |
D3 ──── D4 ──── D5
|  X2   |  X3   |
D6 ──── D7 ──── D8
  \    /      \    /
    Z2          Z3

Qubit count: 9 data + 8 ancilla = 17 total qubits
State vector: 2^17 = 131,072 complex amplitudes
Memory: 2 MB per state vector

What ruQu Provides Today

The existing ruQu crate already implements key components for error correction:

Component	Module	Status
Syndrome processing	`syndrome.rs`	Production-ready (1M rounds/sec)
MWPM decoder	`decoder.rs`	Integrated via fusion-blossom
Min-cut coherence	`mincut.rs`	El-Hayek/Henzinger/Li algorithm
Three-filter pipeline	`filters.rs`	Structural + Shift + Evidence
Tile architecture	`tile.rs`, `fabric.rs`	256-tile WASM fabric
Stim integration	`stim.rs`	Syndrome generation

What is missing is the ability to simulate the full quantum state evolution of a surface code cycle: ancilla initialization, stabilizer circuits, projective measurement, state collapse, decoder feedback, and correction application. This ADR fills that gap.

Requirements

Requirement	Description	Priority
Mid-circuit measurement	Projective measurement of individual qubits	P0
Qubit reset	Reinitialize ancilla qubits to	0> each cycle
Conditional operations	Apply gates conditioned on measurement outcomes	P0
Noise injection	Depolarizing, bit-flip, phase-flip channels	P0
Syndrome extraction	Extract syndrome bits from ancilla measurements	P0
Decoder integration	Feed syndromes to MWPM/min-cut decoder	P0
Logical error tracking	Determine if logical error occurred	P1
Multi-cycle simulation	Run thousands of QEC cycles efficiently	P1
Leakage modeling	Simulate qubit leakage to non-computational states	P2

Decision

1. Mid-Circuit Measurement

Mid-circuit measurement is the most critical new capability. Unlike final-state measurement (which collapses the entire state), mid-circuit measurement collapses a single qubit while preserving the rest of the system for continued evolution.

Mathematical formulation:

For measuring qubit q in the computational basis:

Split the state into two subspaces:
- |psi_0>: amplitudes where qubit q = 0
- |psi_1>: amplitudes where qubit q = 1
Compute probabilities:
- P(0) = ||psi_0||^2 = sum_{k: bit_q(k)=0} |amp_k|^2
- P(1) = ||psi_1||^2 = sum_{k: bit_q(k)=1} |amp_k|^2
Sample outcome m in {0, 1} according to P(0), P(1)
Collapse: zero out amplitudes in the non-selected subspace
Renormalize: divide remaining amplitudes by sqrt(P(m))

/// Result of a mid-circuit measurement.
pub struct MeasurementResult {
    /// The measured qubit index
    pub qubit: usize,
    /// The measurement outcome (0 or 1)
    pub outcome: u8,
    /// The probability of this outcome
    pub probability: f64,
}

impl QuantumState {
    /// Perform a projective measurement on a single qubit.
    ///
    /// This collapses the qubit to |0> or |1> based on Born probabilities,
    /// zeroes out amplitudes in the rejected subspace, and renormalizes.
    ///
    /// The remaining qubits are left in a valid quantum state for continued
    /// simulation (essential for mid-circuit measurement in QEC).
    ///
    /// Complexity: O(2^n) -- two passes over the state vector.
    ///   Pass 1: Compute probabilities P(0), P(1)
    ///   Pass 2: Collapse and renormalize
    pub fn measure_qubit(
        &mut self,
        qubit: usize,
        rng: &mut impl Rng,
    ) -> MeasurementResult {
        let mask = 1_usize << qubit;
        let n = self.amplitudes.len();

        // Pass 1: Compute P(0) and P(1)
        let mut prob_0 = 0.0_f64;
        let mut prob_1 = 0.0_f64;

        for k in 0..n {
            let p = self.amplitudes[k].norm_sqr();
            if (k & mask) == 0 {
                prob_0 += p;
            } else {
                prob_1 += p;
            }
        }

        // Sample outcome
        let outcome = if rng.gen::<f64>() < prob_0 { 0_u8 } else { 1_u8 };
        let prob_selected = if outcome == 0 { prob_0 } else { prob_1 };
        let norm_factor = 1.0 / prob_selected.sqrt();

        // Pass 2: Collapse and renormalize
        for k in 0..n {
            let bit = ((k & mask) >> qubit) as u8;
            if bit == outcome {
                self.amplitudes[k] *= norm_factor;
            } else {
                self.amplitudes[k] = Complex64::zero();
            }
        }

        MeasurementResult {
            qubit,
            outcome,
            probability: prob_selected,
        }
    }

    /// Measure multiple qubits (ancilla register).
    ///
    /// Measures each qubit sequentially. The order matters because each
    /// measurement collapses the state before the next measurement.
    /// For stabilizer measurements, this correctly handles correlated outcomes.
    pub fn measure_qubits(
        &mut self,
        qubits: &[usize],
        rng: &mut impl Rng,
    ) -> Vec<MeasurementResult> {
        qubits.iter()
            .map(|&q| self.measure_qubit(q, rng))
            .collect()
    }
}

2. Qubit Reset

Ancilla qubits must be reinitialized to |0> at the start of each syndrome extraction cycle. The reset operation projects onto the |0> subspace and renormalizes:

impl QuantumState {
    /// Reset a qubit to |0>.
    ///
    /// Zeroes out all amplitudes where qubit q = 1, then renormalizes.
    /// This is equivalent to measuring the qubit and, if the outcome is |1>,
    /// applying an X gate to flip it back to |0>.
    ///
    /// Complexity: O(2^n) -- single pass over state vector.
    ///
    /// Used for ancilla reinitialization in each QEC cycle.
    pub fn reset_qubit(&mut self, qubit: usize) {
        let mask = 1_usize << qubit;
        let partner_mask = !mask;
        let n = self.amplitudes.len();

        // For each pair of states (k, k XOR mask), move amplitude from
        // the |1> component to the |0> component.
        // This implements: |0><0| + |0><1| (measure-then-flip).
        //
        // Simpler approach: zero out |1> subspace, renormalize.
        let mut norm_sq = 0.0_f64;

        for k in 0..n {
            if (k & mask) != 0 {
                // Qubit q is |1> in this basis state
                // Transfer amplitude to partner state with q = |0>
                let partner = k & partner_mask;
                // Coherent reset: add amplitudes
                // For incoherent reset (thermal): would zero out instead
                self.amplitudes[partner] += self.amplitudes[k];
                self.amplitudes[k] = Complex64::zero();
            }
        }

        // Renormalize
        for k in 0..n {
            norm_sq += self.amplitudes[k].norm_sqr();
        }
        let norm_factor = 1.0 / norm_sq.sqrt();
        for amp in self.amplitudes.iter_mut() {
            *amp *= norm_factor;
        }
    }
}

3. Noise Model

We implement three standard noise channels plus a combined depolarizing model. Noise is applied by stochastically inserting Pauli gates after specified operations.

Noise Channels:

Bit-flip (X):     rho -> (1-p) * rho + p * X * rho * X
Phase-flip (Z):   rho -> (1-p) * rho + p * Z * rho * Z
Depolarizing:     rho -> (1-p) * rho + p/3 * (X*rho*X + Y*rho*Y + Z*rho*Z)

For state vector simulation, noise is applied via stochastic Pauli insertion:

/// Noise model configuration.
#[derive(Debug, Clone)]
pub struct NoiseModel {
    /// Single-qubit gate error rate
    pub single_qubit_error: f64,
    /// Two-qubit gate error rate
    pub two_qubit_error: f64,
    /// Measurement error rate (readout bit-flip)
    pub measurement_error: f64,
    /// Idle error rate (per qubit per cycle)
    pub idle_error: f64,
    /// Noise type
    pub noise_type: NoiseType,
}

#[derive(Debug, Clone, Copy)]
pub enum NoiseType {
    /// Random X errors with probability p
    BitFlip,
    /// Random Z errors with probability p
    PhaseFlip,
    /// Random X, Y, or Z errors each with probability p/3
    Depolarizing,
    /// Independent bit-flip (p_x) and phase-flip (p_z)
    Independent { p_x: f64, p_z: f64 },
}

impl QuantumState {
    /// Apply a noise channel to a single qubit.
    ///
    /// For depolarizing noise with probability p:
    ///   - With probability 1-p: do nothing
    ///   - With probability p/3: apply X
    ///   - With probability p/3: apply Y
    ///   - With probability p/3: apply Z
    ///
    /// This stochastic Pauli insertion is exact for Pauli channels
    /// and a good approximation for general noise (Pauli twirl).
    pub fn apply_noise(
        &mut self,
        qubit: usize,
        error_rate: f64,
        noise_type: NoiseType,
        rng: &mut impl Rng,
    ) {
        match noise_type {
            NoiseType::BitFlip => {
                if rng.gen::<f64>() < error_rate {
                    self.apply_x(qubit);
                }
            }
            NoiseType::PhaseFlip => {
                if rng.gen::<f64>() < error_rate {
                    self.apply_z(qubit);
                }
            }
            NoiseType::Depolarizing => {
                let r = rng.gen::<f64>();
                if r < error_rate / 3.0 {
                    self.apply_x(qubit);
                } else if r < 2.0 * error_rate / 3.0 {
                    self.apply_y(qubit);
                } else if r < error_rate {
                    self.apply_z(qubit);
                }
                // else: no error (identity)
            }
            NoiseType::Independent { p_x, p_z } => {
                if rng.gen::<f64>() < p_x {
                    self.apply_x(qubit);
                }
                if rng.gen::<f64>() < p_z {
                    self.apply_z(qubit);
                }
            }
        }
    }

    /// Apply idle noise to all data qubits.
    ///
    /// Called once per QEC cycle to model decoherence during idle periods.
    pub fn apply_idle_noise(
        &mut self,
        data_qubits: &[usize],
        noise: &NoiseModel,
        rng: &mut impl Rng,
    ) {
        for &q in data_qubits {
            self.apply_noise(q, noise.idle_error, noise.noise_type, rng);
        }
    }
}

4. Syndrome Extraction Circuit

A complete surface code syndrome extraction cycle consists of:

Reset ancilla qubits to |0>
Apply CNOT chains from data qubits to ancilla (stabilizer circuits)
Measure ancilla qubits to extract syndrome bits
(Optionally) apply noise after each gate

Syndrome Extraction for X-Stabilizer X0 = X_D0 * X_D1 * X_D3 * X_D4:

  D0: ────────●───────────────────────────
              │
  D1: ────────┼──────●────────────────────
              │      │
  D3: ────────┼──────┼──────●─────────────
              │      │      │
  D4: ────────┼──────┼──────┼──────●──────
              │      │      │      │
  X0: ──|0>──[H]──CNOT──CNOT──CNOT──CNOT──[H]──[M]── syndrome bit

  (For X-stabilizers: Hadamard on ancilla before and after CNOTs)
  (For Z-stabilizers: CNOTs in opposite direction, no Hadamards)

/// Surface code layout definition.
pub struct SurfaceCodeLayout {
    /// Code distance
    pub distance: usize,
    /// Data qubit indices
    pub data_qubits: Vec<usize>,
    /// X-stabilizer definitions: (ancilla_qubit, [data_qubits])
    pub x_stabilizers: Vec<(usize, Vec<usize>)>,
    /// Z-stabilizer definitions: (ancilla_qubit, [data_qubits])
    pub z_stabilizers: Vec<(usize, Vec<usize>)>,
    /// Total qubit count (data + ancilla)
    pub total_qubits: usize,
}

impl SurfaceCodeLayout {
    /// Generate a distance-d rotated surface code layout.
    pub fn rotated(distance: usize) -> Self {
        let n_data = distance * distance;
        let n_x_stab = (distance * distance - 1) / 2;
        let n_z_stab = (distance * distance - 1) / 2;
        let total = n_data + n_x_stab + n_z_stab;

        // Assign qubit indices:
        // 0..n_data: data qubits
        // n_data..n_data+n_x_stab: X-stabilizer ancillae
        // n_data+n_x_stab..total: Z-stabilizer ancillae

        let data_qubits: Vec<usize> = (0..n_data).collect();

        // Build stabilizer mappings based on rotated surface code geometry
        let (x_stabilizers, z_stabilizers) =
            build_rotated_stabilizers(distance, n_data);

        Self {
            distance,
            data_qubits,
            x_stabilizers,
            z_stabilizers,
            total_qubits: total,
        }
    }
}

/// One complete syndrome extraction cycle.
///
/// Returns the syndrome bitstring (one bit per stabilizer).
pub fn extract_syndrome(
    state: &mut QuantumState,
    layout: &SurfaceCodeLayout,
    noise: &Option<NoiseModel>,
    rng: &mut impl Rng,
) -> SyndromeBits {
    let mut syndrome = SyndromeBits::new(
        layout.x_stabilizers.len() + layout.z_stabilizers.len()
    );

    // Step 1: Reset all ancilla qubits
    for &(ancilla, _) in layout.x_stabilizers.iter()
        .chain(layout.z_stabilizers.iter())
    {
        state.reset_qubit(ancilla);
    }

    // Step 2: X-stabilizer circuits
    for (stab_idx, &(ancilla, ref data)) in layout.x_stabilizers.iter().enumerate() {
        // Hadamard on ancilla (transforms Z-basis CNOT to X-basis measurement)
        state.apply_hadamard(ancilla);
        if let Some(ref n) = noise {
            state.apply_noise(ancilla, n.single_qubit_error, n.noise_type, rng);
        }

        // CNOT from each data qubit to ancilla
        for &d in data {
            state.apply_cnot(d, ancilla);
            if let Some(ref n) = noise {
                state.apply_noise(d, n.two_qubit_error, n.noise_type, rng);
                state.apply_noise(ancilla, n.two_qubit_error, n.noise_type, rng);
            }
        }

        // Hadamard on ancilla
        state.apply_hadamard(ancilla);
        if let Some(ref n) = noise {
            state.apply_noise(ancilla, n.single_qubit_error, n.noise_type, rng);
        }

        // Measure ancilla
        let result = state.measure_qubit(ancilla, rng);

        // Apply measurement error
        let mut outcome = result.outcome;
        if let Some(ref n) = noise {
            if rng.gen::<f64>() < n.measurement_error {
                outcome ^= 1; // Flip the classical bit
            }
        }

        syndrome.set(stab_idx, outcome);
    }

    // Step 3: Z-stabilizer circuits
    let offset = layout.x_stabilizers.len();
    for (stab_idx, &(ancilla, ref data)) in layout.z_stabilizers.iter().enumerate() {
        // No Hadamard for Z-stabilizers

        // CNOT from ancilla to each data qubit
        for &d in data {
            state.apply_cnot(ancilla, d);
            if let Some(ref n) = noise {
                state.apply_noise(d, n.two_qubit_error, n.noise_type, rng);
                state.apply_noise(ancilla, n.two_qubit_error, n.noise_type, rng);
            }
        }

        // Measure ancilla
        let result = state.measure_qubit(ancilla, rng);

        let mut outcome = result.outcome;
        if let Some(ref n) = noise {
            if rng.gen::<f64>() < n.measurement_error {
                outcome ^= 1;
            }
        }

        syndrome.set(offset + stab_idx, outcome);
    }

    // Step 4: Apply idle noise to data qubits
    if let Some(ref n) = noise {
        state.apply_idle_noise(&layout.data_qubits, n, rng);
    }

    syndrome
}

5. Decoder Integration

The syndrome bits feed into ruQu's existing decoder infrastructure:

Decoder Pipeline:

  Syndrome Bits ──> SyndromeFilter ──> MWPM Decoder ──> Correction ──> Apply to State
        │                                    │
        │                              ┌─────▼─────┐
        │                              │ ruvector-  │
        │                              │ mincut     │
        └──────────────────────────────│ coherence  │
                                       │ validation │
                                       └────────────┘

/// Decode syndrome and apply corrections.
///
/// This function bridges the quantum simulation (state vector) with
/// ruQu's classical decoder infrastructure.
pub fn decode_and_correct(
    state: &mut QuantumState,
    syndrome: &SyndromeBits,
    layout: &SurfaceCodeLayout,
    decoder: &mut MWPMDecoder,
) -> DecoderResult {
    // Convert syndrome bits to DetectorBitmap (ruQu format)
    let mut bitmap = DetectorBitmap::new(syndrome.len());
    for i in 0..syndrome.len() {
        bitmap.set(i, syndrome.get(i) == 1);
    }

    // Decode using MWPM
    let correction = decoder.decode(&bitmap);

    // Apply X corrections to data qubits
    for &qubit in &correction.x_corrections {
        state.apply_x(qubit);
    }

    // Apply Z corrections to data qubits
    for &qubit in &correction.z_corrections {
        state.apply_z(qubit);
    }

    DecoderResult {
        correction,
        syndrome: bitmap,
        applied: true,
    }
}

Integration with ruvector-mincut for coherence validation:

/// Validate decoder correction using min-cut coherence analysis.
///
/// Uses ruQu's existing DynamicMinCutEngine to assess whether the
/// post-correction state maintains structural coherence.
pub fn validate_correction(
    syndrome: &SyndromeBits,
    correction: &Correction,
    mincut_engine: &mut DynamicMinCutEngine,
) -> CoherenceAssessment {
    // Update min-cut graph edges based on syndrome pattern
    // High syndrome density in a region lowers edge weights (less coherent)
    // Correction success restores edge weights

    let cut_value = mincut_engine.query_min_cut();

    CoherenceAssessment {
        min_cut_value: cut_value.value,
        is_coherent: cut_value.value > COHERENCE_THRESHOLD,
        witness: cut_value.witness_hash,
    }
}

6. Logical Error Tracking

To determine if a logical error has occurred, we compare the initial and final logical qubit states:

/// Track logical errors across QEC cycles.
///
/// A logical error occurs when the cumulative effect of physical errors
/// and decoder corrections results in a non-trivial logical operator
/// being applied to the encoded qubit.
pub struct LogicalErrorTracker {
    /// Accumulated X corrections on data qubits
    x_correction_parity: Vec<bool>,
    /// Accumulated Z corrections on data qubits
    z_correction_parity: Vec<bool>,
    /// Known physical X errors (for debugging/validation)
    x_error_parity: Vec<bool>,
    /// Known physical Z errors
    z_error_parity: Vec<bool>,
    /// Logical X operator support (which data qubits)
    logical_x_support: Vec<usize>,
    /// Logical Z operator support
    logical_z_support: Vec<usize>,
}

impl LogicalErrorTracker {
    /// Check if a logical X error has occurred.
    ///
    /// A logical X error occurs when the net X-type operator
    /// (errors + corrections) has odd overlap with the logical Z operator.
    pub fn has_logical_x_error(&self) -> bool {
        let mut parity = false;
        for &q in &self.logical_z_support {
            parity ^= self.x_error_parity[q] ^ self.x_correction_parity[q];
        }
        parity
    }

    /// Check if a logical Z error has occurred.
    pub fn has_logical_z_error(&self) -> bool {
        let mut parity = false;
        for &q in &self.logical_x_support {
            parity ^= self.z_error_parity[q] ^ self.z_correction_parity[q];
        }
        parity
    }

    /// Check if any logical error has occurred.
    pub fn has_logical_error(&self) -> bool {
        self.has_logical_x_error() || self.has_logical_z_error()
    }
}

7. Full Surface Code Simulation Cycle

Putting it all together, the complete simulation loop:

Full Surface Code QEC Cycle
============================

Input:  Code distance d, noise model, number of cycles T, decoder

Output: Logical error rate estimate

    layout = SurfaceCodeLayout::rotated(d)
    state = QuantumState::new(layout.total_qubits)
    tracker = LogicalErrorTracker::new(layout)
    decoder = MWPMDecoder::new(d)
    mincut = DynamicMinCutEngine::new()

    // Prepare initial logical |0> state
    prepare_logical_zero(&mut state, &layout)

    for cycle in 0..T:
        ┌─────────────────────────────────────────────────────┐
        │  1. INJECT NOISE                                     │
        │     Apply depolarizing noise to all data qubits      │
        │     (models decoherence during idle + gate errors)   │
        │     tracker.record_errors(noise_locations)            │
        └─────────────────────────────────────────────────────┘
                               │
                               ▼
        ┌─────────────────────────────────────────────────────┐
        │  2. EXTRACT SYNDROME                                 │
        │     Reset ancillae -> stabilizer circuits -> measure │
        │     Returns syndrome bitstring for this cycle        │
        └─────────────────────────────────────────────────────┘
                               │
                               ▼
        ┌─────────────────────────────────────────────────────┐
        │  3. DECODE                                           │
        │     Feed syndrome to MWPM decoder                    │
        │     Decoder returns correction (X and Z Pauli ops)   │
        └─────────────────────────────────────────────────────┘
                               │
                               ▼
        ┌─────────────────────────────────────────────────────┐
        │  4. APPLY CORRECTION                                 │
        │     Apply Pauli corrections to data qubits           │
        │     tracker.record_corrections(corrections)          │
        └─────────────────────────────────────────────────────┘
                               │
                               ▼
        ┌─────────────────────────────────────────────────────┐
        │  5. VALIDATE COHERENCE (optional)                    │
        │     Run min-cut analysis on syndrome pattern         │
        │     Flag if coherence drops below threshold          │
        └─────────────────────────────────────────────────────┘

    // After T cycles, check for logical error
    logical_error = tracker.has_logical_error()

Pseudocode for the full simulation:

/// Run a complete surface code QEC simulation.
///
/// Returns the logical error rate estimated from `trials` independent runs,
/// each consisting of `cycles` QEC rounds.
pub fn simulate_surface_code(config: &SurfaceCodeConfig) -> SimulationResult {
    let layout = SurfaceCodeLayout::rotated(config.distance);
    let mut logical_errors = 0_u64;
    let mut total_cycles = 0_u64;

    for trial in 0..config.trials {
        let mut state = QuantumState::new(layout.total_qubits);
        let mut tracker = LogicalErrorTracker::new(&layout);
        let mut decoder = MWPMDecoder::new(DecoderConfig {
            distance: config.distance,
            physical_error_rate: config.noise.idle_error,
            ..Default::default()
        });
        let mut rng = StdRng::seed_from_u64(config.seed + trial);

        // Prepare logical |0>
        prepare_logical_zero(&mut state, &layout);

        for cycle in 0..config.cycles {
            // 1. Inject noise
            inject_data_noise(&mut state, &layout, &config.noise, &mut rng);

            // 2. Extract syndrome
            let syndrome = extract_syndrome(
                &mut state, &layout, &Some(config.noise.clone()), &mut rng
            );

            // 3. Decode
            let correction = decoder.decode_syndrome(&syndrome);

            // 4. Apply correction
            apply_correction(&mut state, &correction);
            tracker.record_correction(&correction);

            total_cycles += 1;
        }

        // Check for logical error
        if tracker.has_logical_error() {
            logical_errors += 1;
        }
    }

    let logical_error_rate = logical_errors as f64 / config.trials as f64;
    let error_per_cycle = 1.0 - (1.0 - logical_error_rate)
        .powf(1.0 / config.cycles as f64);

    SimulationResult {
        logical_error_rate,
        logical_error_per_cycle: error_per_cycle,
        total_trials: config.trials,
        total_cycles,
        logical_errors,
        distance: config.distance,
        physical_error_rate: config.noise.idle_error,
    }
}

8. Performance Estimates

Distance-3 Surface Code

Parameter	Value
Data qubits	9
Ancilla qubits	8
Total qubits	17
State vector entries	2^17 = 131,072
State vector memory	2 MB
CNOTs per cycle	~16 (4 per stabilizer, 4 stabilizers active)
Measurements per cycle	8
Resets per cycle	8
Time per cycle	~0.5ms
1000 cycles	~0.5s

Distance-5 Surface Code

Parameter	Value
Data qubits	25
Ancilla qubits	24
Total qubits	49
State vector entries	2^49 ~ 5.6 * 10^14
State vector memory	4 PB (infeasible for full state vector)

This highlights the fundamental scaling challenge: full state vector simulation of distance-5 surface codes requires stabilizer simulation or tensor network methods, not direct state vector evolution. However, for the critical distance-3 case, state vector simulation is fast and provides ground truth.

Practical simulation envelope:

Distance	Qubits	State Vector	Feasible?	Cycles/sec
2 (toy)	7	128 entries	Yes	~50,000
3	17	131K entries	Yes	~2,000
3 (with noise)	17	131K entries	Yes	~1,000
4	31	2B entries	Marginal (16 GB)	~0.1
5+	49+	>10^14	No (state vector)	--

For distance 5 and above, the implementation should fall back to stabilizer simulation (Gottesman-Knill theorem: Clifford circuits on stabilizer states can be simulated in polynomial time). Since surface code circuits consist entirely of Clifford gates (H, CNOT, S) with Pauli noise, this is a natural fit.

9. Integration with Existing ruQu Pipeline

The surface code simulation integrates with the full ruQu stack:

┌─────────────────────────────────────────────────────────────────────┐
│                    ruQu QEC Simulation Stack                         │
│                                                                       │
│  ┌─────────────┐  ┌──────────────┐  ┌───────────────────────────┐   │
│  │  State       │  │  Syndrome     │  │  Decoder Pipeline          │   │
│  │  Vector      │  │  Processing   │  │                           │   │
│  │  Engine      │──│  (syndrome.rs)│──│  SyndromeFilter           │   │
│  │  (new)       │  │              │  │  ├── StructuralFilter      │   │
│  │              │  │  DetectorBitmap  │  │  ├── ShiftFilter         │   │
│  │  measure()   │  │  SyndromeBuffer │  │  ├── EvidenceFilter      │   │
│  │  reset()     │  │  SyndromeDelta │  │  └── MWPM Decoder        │   │
│  │  noise()     │  │              │  │      (decoder.rs)          │   │
│  └─────────────┘  └──────────────┘  └───────────────────────────┘   │
│         │                                        │                    │
│         │              ┌─────────────────────────┘                    │
│         │              │                                              │
│         ▼              ▼                                              │
│  ┌──────────────────────────┐  ┌────────────────────────────────┐   │
│  │  Correction Application  │  │  Coherence Validation           │   │
│  │                          │  │                                  │   │
│  │  apply_x(qubit)         │  │  DynamicMinCutEngine             │   │
│  │  apply_z(qubit)         │  │  (mincut.rs)                     │   │
│  │                          │  │                                  │   │
│  │  Logical Error Tracker   │  │  El-Hayek/Henzinger/Li          │   │
│  └──────────────────────────┘  │  O(n^{o(1)}) min-cut            │   │
│                                  └────────────────────────────────┘   │
│                                                                       │
│  ┌───────────────────────────────────────────────────────────────┐   │
│  │  Tile Architecture (fabric.rs, tile.rs)                        │   │
│  │                                                                 │   │
│  │  TileZero (coordinator) + 255 WorkerTiles                      │   │
│  │  Can parallelize across stabilizer groups for large codes      │   │
│  └───────────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────────┘

Key integration points:

Syndrome bits from measure_qubit() are converted to DetectorBitmap format for compatibility with ruQu's existing syndrome processing pipeline
MWPM decoder from decoder.rs (backed by fusion-blossom) receives syndromes and returns corrections
Min-cut coherence from mincut.rs validates post-correction state quality
Tile architecture from fabric.rs can distribute stabilizer measurements across tiles for parallel processing of large codes
Stim integration from stim.rs provides reference syndrome distributions for decoder benchmarking

10. Error Rate Estimation

To estimate the error threshold, we run simulations at multiple physical error rates and code distances:

/// Estimate the error threshold by scanning physical error rates.
///
/// The threshold is the physical error rate p* at which logical error rate
/// is independent of code distance. Below p*, larger codes are better.
/// Above p*, larger codes are worse.
pub fn estimate_threshold(
    distances: &[usize],
    error_rates: &[f64],
    cycles_per_trial: usize,
    trials: usize,
) -> ThresholdResult {
    let mut results = Vec::new();

    for &d in distances {
        for &p in error_rates {
            let config = SurfaceCodeConfig {
                distance: d,
                noise: NoiseModel {
                    idle_error: p,
                    single_qubit_error: p / 10.0,
                    two_qubit_error: p,
                    measurement_error: p,
                    noise_type: NoiseType::Depolarizing,
                },
                cycles: cycles_per_trial,
                trials: trials as u64,
                seed: 42,
            };

            let sim_result = simulate_surface_code(&config);
            results.push((d, p, sim_result.logical_error_per_cycle));
        }
    }

    // Find crossing point of d=3 and d=5 curves
    find_threshold_crossing(&results)
}

Consequences

Benefits

Full quantum state simulation provides ground truth for decoder validation that stabilizer simulation alone cannot (e.g., non-Clifford noise, leakage states)
Seamless integration with ruQu's existing syndrome processing, MWPM decoder, and min-cut coherence infrastructure minimizes new code and leverages battle-tested components
Mid-circuit measurement and qubit reset enable accurate simulation of the actual hardware QEC cycle, not just the error model
Noise model flexibility (bit-flip, phase-flip, depolarizing, independent) covers the standard noise models used in QEC research
Logical error tracking provides direct measurement of the quantity of interest (logical error rate) without post-hoc analysis
Integration with min-cut coherence validates that decoder corrections maintain structural coherence, bridging ruQu's unique coherence-gating approach with standard QEC metrics

Risks

Risk	Probability	Impact	Mitigation
State vector memory limits simulation to d <= 3	High	High	Stabilizer simulation fallback for d >= 5
Mid-circuit measurement breaks SIMD optimization	Medium	Medium	Separate hot/cold paths, measurement is infrequent
Noise model too simplistic for real hardware	Medium	Medium	Support custom noise channels, correlated errors
Decoder latency dominates simulation time	Low	Medium	Use streaming decoder, pre-built matching graphs
Logical error tracking complexity for higher distance	Low	Low	Automate logical operator computation from layout

Trade-offs

Decision	Advantage	Disadvantage
State vector over stabilizer simulation	Handles arbitrary noise and non-Clifford ops	Exponential memory, limited to d <= 3-4
Stochastic Pauli insertion for noise	Simple, exact for Pauli channels	Approximate for non-Pauli noise
Sequential ancilla measurement	Correct correlated outcomes	Cannot parallelize measurement step
Integration with existing ruQu decoder	Reuses battle-tested code	Decoder API may not perfectly match simulation needs
Coherent reset (amplitude transfer)	Preserves entanglement structure	More complex than incoherent reset

References

Fowler, A.G. et al. "Surface codes: Towards practical large-scale quantum computation." Physical Review A 86, 032324 (2012)
Dennis, E. et al. "Topological quantum memory." Journal of Mathematical Physics 43, 4452-4505 (2002)
Google Quantum AI. "Suppressing quantum errors by scaling a surface code logical qubit." Nature 614, 676-681 (2023)
Higgott, O. "PyMatching: A Python package for decoding quantum codes with minimum-weight perfect matching." ACM Transactions on Quantum Computing 3, 1-16 (2022)
Wu, Y. & Lin, H.H. "Hypergraph Decomposition and Secret Sharing." Discrete Applied Mathematics (2024)
ADR-001: ruQu Architecture - Classical Nervous System for Quantum Machines
ADR-QE-005: VQE Algorithm Support (quantum state manipulation, expectation values)
ADR-QE-006: Grover's Search (state vector operations, measurement)
ruQu syndrome module: crates/ruQu/src/syndrome.rs - DetectorBitmap, SyndromeBuffer
ruQu decoder module: crates/ruQu/src/decoder.rs - MWPMDecoder, fusion-blossom
ruQu mincut module: crates/ruQu/src/mincut.rs - DynamicMinCutEngine
ruQu filters module: crates/ruQu/src/filters.rs - Three-filter coherence pipeline
ruvector-mincut crate: crates/ruvector-mincut/ - El-Hayek/Henzinger/Li algorithm

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ADR-QE-008: Surface Code Error Correction Simulation

Version History

Context

The Importance of QEC Simulation

Surface Codes as the Leading Architecture

Surface Code Layout (Distance-3)

What ruQu Provides Today

Requirements

Decision

1. Mid-Circuit Measurement

2. Qubit Reset

3. Noise Model

4. Syndrome Extraction Circuit

5. Decoder Integration

6. Logical Error Tracking

7. Full Surface Code Simulation Cycle

8. Performance Estimates

Distance-3 Surface Code

Distance-5 Surface Code

9. Integration with Existing ruQu Pipeline

10. Error Rate Estimation

Consequences

Benefits

Risks

Trade-offs

References

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ADR-QE-008-surface-code-error-correction.md

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ADR-QE-008-surface-code-error-correction.md

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ADR-QE-008: Surface Code Error Correction Simulation

Version History

Context

The Importance of QEC Simulation

Surface Codes as the Leading Architecture

Surface Code Layout (Distance-3)

What ruQu Provides Today

Requirements

Decision

1. Mid-Circuit Measurement

2. Qubit Reset

3. Noise Model

4. Syndrome Extraction Circuit

5. Decoder Integration

6. Logical Error Tracking

7. Full Surface Code Simulation Cycle

8. Performance Estimates

Distance-3 Surface Code

Distance-5 Surface Code

9. Integration with Existing ruQu Pipeline

10. Error Rate Estimation

Consequences

Benefits

Risks

Trade-offs

References