quantrs2_tytan/

quantum_optimization_extensions.rs

//! Extended quantum optimization algorithms.
//!
//! This module provides advanced implementations of quantum optimization algorithms
//! including QAOA extensions, ADAPT-QAOA, and other variants.

#![allow(dead_code)]

use crate::hybrid_algorithms::{ClassicalOptimizer, Hamiltonian};
use scirs2_core::random::prelude::*;
use scirs2_core::random::prelude::*;
use std::collections::HashMap;
use std::f64::consts::PI;

/// ADAPT-QAOA: Adaptive QAOA with dynamic circuit construction
pub struct AdaptQAOA {
    /// Maximum circuit depth
    max_depth: usize,
    /// Pool of operators to choose from
    operator_pool: OperatorPool,
    /// Gradient threshold for adding operators
    gradient_threshold: f64,
    /// Classical optimizer
    optimizer: ClassicalOptimizer,
    /// Use commutator screening
    use_commutator_screening: bool,
}

#[derive(Debug, Clone)]
pub struct OperatorPool {
    /// Single-qubit operators
    single_qubit_ops: Vec<PauliOperator>,
    /// Two-qubit operators
    two_qubit_ops: Vec<PauliOperator>,
    /// Problem-specific operators
    custom_ops: Vec<PauliOperator>,
}

#[derive(Debug, Clone)]
pub struct PauliOperator {
    /// Pauli string
    pauli_string: Vec<char>,
    /// Coefficient
    coefficient: f64,
    /// Name/label
    label: String,
}

impl AdaptQAOA {
    /// Create new ADAPT-QAOA solver
    pub fn new(max_depth: usize, optimizer: ClassicalOptimizer) -> Self {
        Self {
            max_depth,
            operator_pool: OperatorPool::default_pool(),
            gradient_threshold: 1e-3,
            optimizer,
            use_commutator_screening: true,
        }
    }

    /// Set gradient threshold
    pub const fn with_gradient_threshold(mut self, threshold: f64) -> Self {
        self.gradient_threshold = threshold;
        self
    }

    /// Set operator pool
    pub fn with_operator_pool(mut self, pool: OperatorPool) -> Self {
        self.operator_pool = pool;
        self
    }

    /// Enable/disable commutator screening
    pub const fn with_commutator_screening(mut self, use_screening: bool) -> Self {
        self.use_commutator_screening = use_screening;
        self
    }

    /// Adaptively construct circuit
    pub fn adapt_circuit(&mut self, hamiltonian: &Hamiltonian) -> Result<AdaptCircuit, String> {
        let mut circuit = AdaptCircuit::new();
        let mut converged = false;

        while circuit.depth() < self.max_depth && !converged {
            // Compute gradients for all operators in pool
            let gradients = self.compute_operator_gradients(&circuit, hamiltonian)?;

            // Find operator with largest gradient
            let (best_op_idx, max_gradient) = gradients
                .iter()
                .enumerate()
                .max_by(|(_, a), (_, b)| {
                    a.abs()
                        .partial_cmp(&b.abs())
                        .unwrap_or(std::cmp::Ordering::Equal)
                })
                .ok_or("No operators in pool")?;

            if max_gradient.abs() < self.gradient_threshold {
                #[allow(unused_assignments)]
                {
                    converged = true;
                }
                break;
            }

            // Add operator to circuit
            let operator = self.operator_pool.get_operator(best_op_idx)?;
            circuit.add_operator(operator.clone(), 0.0); // Initial parameter

            // Optimize parameters
            self.optimize_circuit_parameters(&mut circuit, hamiltonian)?;

            // Optional: remove operator from pool to avoid repetition
            if circuit.depth() < self.max_depth / 2 {
                // Keep all operators available in early stages
            } else {
                // Start removing used operators
                self.operator_pool.remove_operator(best_op_idx);
            }
        }

        Ok(circuit)
    }

    /// Compute gradients for all operators
    fn compute_operator_gradients(
        &self,
        circuit: &AdaptCircuit,
        hamiltonian: &Hamiltonian,
    ) -> Result<Vec<f64>, String> {
        let mut gradients = Vec::new();

        for op in self.operator_pool.iter() {
            // Use commutator screening if enabled
            if self.use_commutator_screening {
                let commutator_norm = self.estimate_commutator_norm(op, hamiltonian)?;
                if commutator_norm < 1e-10 {
                    gradients.push(0.0);
                    continue;
                }
            }

            // Compute gradient using parameter shift rule
            let gradient = self.compute_single_gradient(circuit, op, hamiltonian)?;
            gradients.push(gradient);
        }

        Ok(gradients)
    }

    /// Estimate commutator norm for screening
    fn estimate_commutator_norm(
        &self,
        operator: &PauliOperator,
        hamiltonian: &Hamiltonian,
    ) -> Result<f64, String> {
        // Simplified: check if operator commutes with Hamiltonian terms
        let mut norm = 0.0;

        for h_term in &hamiltonian.terms {
            let commutes = self.pauli_strings_commute(&operator.pauli_string, &h_term.pauli_string);

            if !commutes {
                norm += h_term.coefficient.abs() * operator.coefficient.abs();
            }
        }

        Ok(norm)
    }

    /// Check if two Pauli strings commute
    fn pauli_strings_commute(&self, p1: &[char], p2: &[char]) -> bool {
        if p1.len() != p2.len() {
            return false;
        }

        let mut anticommuting_count = 0;

        for (a, b) in p1.iter().zip(p2.iter()) {
            if *a != 'I' && *b != 'I' && *a != *b {
                anticommuting_count += 1;
            }
        }

        anticommuting_count % 2 == 0
    }

    /// Compute gradient for single operator
    fn compute_single_gradient(
        &self,
        _circuit: &AdaptCircuit,
        operator: &PauliOperator,
        _hamiltonian: &Hamiltonian,
    ) -> Result<f64, String> {
        // Simplified gradient computation
        // In practice, would evaluate expectation values
        let random_gradient = thread_rng().gen_range(-1.0..1.0);
        Ok(random_gradient * operator.coefficient)
    }

    /// Optimize circuit parameters
    fn optimize_circuit_parameters(
        &self,
        circuit: &mut AdaptCircuit,
        _hamiltonian: &Hamiltonian,
    ) -> Result<(), String> {
        // Simplified: random parameter updates
        let mut rng = thread_rng();

        for param in circuit.parameters_mut() {
            *param += rng.gen_range(-0.1..0.1);
        }

        Ok(())
    }
}

/// Adaptive circuit for ADAPT-QAOA
#[derive(Debug, Clone)]
pub struct AdaptCircuit {
    /// Operators in circuit
    operators: Vec<PauliOperator>,
    /// Parameters for each operator
    parameters: Vec<f64>,
}

impl AdaptCircuit {
    const fn new() -> Self {
        Self {
            operators: Vec::new(),
            parameters: Vec::new(),
        }
    }

    fn depth(&self) -> usize {
        self.operators.len()
    }

    fn add_operator(&mut self, operator: PauliOperator, parameter: f64) {
        self.operators.push(operator);
        self.parameters.push(parameter);
    }

    fn parameters_mut(&mut self) -> &mut [f64] {
        &mut self.parameters
    }
}

impl OperatorPool {
    /// Create default operator pool
    fn default_pool() -> Self {
        let mut single_qubit_ops = Vec::new();
        let mut two_qubit_ops = Vec::new();

        // Standard single-qubit rotations
        for pauli in ['X', 'Y', 'Z'] {
            single_qubit_ops.push(PauliOperator {
                pauli_string: vec![pauli],
                coefficient: 1.0,
                label: format!("R{pauli}"),
            });
        }

        // Two-qubit entangling operators
        for (p1, p2) in [('X', 'X'), ('Y', 'Y'), ('Z', 'Z'), ('X', 'Y'), ('Y', 'Z')] {
            two_qubit_ops.push(PauliOperator {
                pauli_string: vec![p1, p2],
                coefficient: 1.0,
                label: format!("{p1}{p2}"),
            });
        }

        Self {
            single_qubit_ops,
            two_qubit_ops,
            custom_ops: Vec::new(),
        }
    }

    fn get_operator(&self, idx: usize) -> Result<&PauliOperator, String> {
        let total_ops =
            self.single_qubit_ops.len() + self.two_qubit_ops.len() + self.custom_ops.len();

        if idx >= total_ops {
            return Err("Operator index out of range".to_string());
        }

        if idx < self.single_qubit_ops.len() {
            Ok(&self.single_qubit_ops[idx])
        } else if idx < self.single_qubit_ops.len() + self.two_qubit_ops.len() {
            Ok(&self.two_qubit_ops[idx - self.single_qubit_ops.len()])
        } else {
            Ok(&self.custom_ops[idx - self.single_qubit_ops.len() - self.two_qubit_ops.len()])
        }
    }

    const fn remove_operator(&self, _idx: usize) {
        // Mark operator as used (simplified)
    }

    fn iter(&self) -> impl Iterator<Item = &PauliOperator> {
        self.single_qubit_ops
            .iter()
            .chain(self.two_qubit_ops.iter())
            .chain(self.custom_ops.iter())
    }
}

/// Quantum Alternating Operator Ansatz (QAOA+)
pub struct QAOAPlus {
    /// Number of layers
    p: usize,
    /// Mixing operators
    mixers: Vec<MixerOperator>,
    /// Initial state preparation
    initial_state: InitialStatePrep,
    /// Constraint handling
    constraints: ConstraintStrategy,
    /// Classical optimizer
    optimizer: ClassicalOptimizer,
}

#[derive(Debug, Clone)]
pub enum MixerOperator {
    /// Standard X-rotation mixer
    StandardX,
    /// XY-mixer for hard constraints
    XYMixer { coupling_strength: f64 },
    /// Grover-style mixer
    GroverMixer { marked_states: Vec<Vec<bool>> },
    /// Custom mixer
    Custom { operator: PauliOperator },
}

#[derive(Debug, Clone)]
pub enum InitialStatePrep {
    /// Equal superposition
    EqualSuperposition,
    /// Warm start from classical solution
    WarmStart { solution: Vec<bool> },
    /// Dicke state
    DickeState { hamming_weight: usize },
    /// Custom state
    Custom { amplitudes: Vec<f64> },
}

#[derive(Debug, Clone)]
pub enum ConstraintStrategy {
    /// No constraints
    None,
    /// Penalty method
    Penalty { strength: f64 },
    /// Constrained mixer
    ConstrainedMixer,
    /// Quantum Zeno dynamics
    QuantumZeno { measurement_rate: f64 },
}

impl QAOAPlus {
    /// Create new QAOA+ solver
    pub fn new(p: usize, optimizer: ClassicalOptimizer) -> Self {
        Self {
            p,
            mixers: vec![MixerOperator::StandardX; p],
            initial_state: InitialStatePrep::EqualSuperposition,
            constraints: ConstraintStrategy::None,
            optimizer,
        }
    }

    /// Set mixer operators
    pub fn with_mixers(mut self, mixers: Vec<MixerOperator>) -> Self {
        self.mixers = mixers;
        self
    }

    /// Set initial state
    pub fn with_initial_state(mut self, state: InitialStatePrep) -> Self {
        self.initial_state = state;
        self
    }

    /// Set constraint strategy
    pub const fn with_constraints(mut self, constraints: ConstraintStrategy) -> Self {
        self.constraints = constraints;
        self
    }

    /// Apply mixer operator
    fn apply_mixer(&self, layer: usize, _beta: f64) -> Result<(), String> {
        match &self.mixers[layer % self.mixers.len()] {
            MixerOperator::StandardX => {
                // Apply X-rotation to all qubits
                Ok(())
            }
            MixerOperator::XYMixer {
                coupling_strength: _,
            } => {
                // Apply XY interactions
                Ok(())
            }
            MixerOperator::GroverMixer { marked_states: _ } => {
                // Apply Grover diffusion operator
                Ok(())
            }
            MixerOperator::Custom { operator: _ } => {
                // Apply custom operator
                Ok(())
            }
        }
    }
}

/// Recursive QAOA for hierarchical optimization
pub struct RecursiveQAOA {
    /// Base QAOA depth
    base_depth: usize,
    /// Recursion depth
    recursion_depth: usize,
    /// Problem decomposition strategy
    decomposition: DecompositionStrategy,
    /// Aggregation method
    aggregation: AggregationMethod,
}

#[derive(Debug, Clone)]
pub enum DecompositionStrategy {
    /// Graph partitioning
    GraphPartitioning { num_partitions: usize },
    /// Variable clustering
    VariableClustering { cluster_size: usize },
    /// Spectral decomposition
    SpectralDecomposition { num_components: usize },
    /// Community detection
    CommunityDetection { resolution: f64 },
}

#[derive(Debug, Clone)]
pub enum AggregationMethod {
    /// Simple merging
    SimpleMerge,
    /// Weighted combination
    WeightedCombination { weights: Vec<f64> },
    /// Consensus voting
    ConsensusVoting,
    /// Bayesian aggregation
    BayesianAggregation,
}

impl RecursiveQAOA {
    /// Create new recursive QAOA
    pub const fn new(
        base_depth: usize,
        recursion_depth: usize,
        decomposition: DecompositionStrategy,
    ) -> Self {
        Self {
            base_depth,
            recursion_depth,
            decomposition,
            aggregation: AggregationMethod::SimpleMerge,
        }
    }

    /// Set aggregation method
    pub fn with_aggregation(mut self, method: AggregationMethod) -> Self {
        self.aggregation = method;
        self
    }

    /// Solve problem recursively
    pub fn solve_recursive(
        &self,
        problem: &Hamiltonian,
        level: usize,
    ) -> Result<RecursiveSolution, String> {
        if level >= self.recursion_depth {
            // Base case: solve with standard QAOA
            return self.solve_base_case(problem);
        }

        // Decompose problem
        let subproblems = self.decompose_problem(problem)?;

        // Solve subproblems recursively
        let mut subsolutions = Vec::new();
        for subproblem in subproblems {
            let solution = self.solve_recursive(&subproblem, level + 1)?;
            subsolutions.push(solution);
        }

        // Aggregate solutions
        self.aggregate_solutions(subsolutions, problem)
    }

    /// Decompose problem into subproblems
    fn decompose_problem(&self, problem: &Hamiltonian) -> Result<Vec<Hamiltonian>, String> {
        match &self.decomposition {
            DecompositionStrategy::GraphPartitioning { num_partitions } => {
                // Partition interaction graph
                Ok(vec![problem.clone(); *num_partitions]) // Simplified
            }
            DecompositionStrategy::VariableClustering { cluster_size: _ } => {
                // Cluster variables
                Ok(vec![problem.clone(); 2]) // Simplified
            }
            _ => Ok(vec![problem.clone()]),
        }
    }

    /// Solve base case
    fn solve_base_case(&self, _problem: &Hamiltonian) -> Result<RecursiveSolution, String> {
        // Use standard QAOA
        Ok(RecursiveSolution {
            energy: 0.0,
            state: vec![false; 10], // Placeholder
            metadata: HashMap::new(),
        })
    }

    /// Aggregate subsolutions
    fn aggregate_solutions(
        &self,
        subsolutions: Vec<RecursiveSolution>,
        _original_problem: &Hamiltonian,
    ) -> Result<RecursiveSolution, String> {
        match &self.aggregation {
            AggregationMethod::SimpleMerge => {
                // Concatenate solutions
                let mut merged_state = Vec::new();
                for sol in &subsolutions {
                    merged_state.extend(&sol.state);
                }

                Ok(RecursiveSolution {
                    energy: subsolutions.iter().map(|s| s.energy).sum(),
                    state: merged_state,
                    metadata: HashMap::new(),
                })
            }
            _ => {
                // Other aggregation methods
                subsolutions
                    .into_iter()
                    .next()
                    .ok_or_else(|| "No subsolutions available for aggregation".to_string())
            }
        }
    }
}

#[derive(Debug, Clone)]
pub struct RecursiveSolution {
    pub energy: f64,
    pub state: Vec<bool>,
    pub metadata: HashMap<String, f64>,
}

/// Multi-angle QAOA with parameterized gates
pub struct MultiAngleQAOA {
    /// Number of layers
    p: usize,
    /// Angle parameterization
    parameterization: AngleParameterization,
    /// Gate decomposition
    gate_decomposition: GateDecomposition,
    /// Symmetry exploitation
    symmetries: Vec<SymmetryType>,
}

#[derive(Debug, Clone)]
pub enum AngleParameterization {
    /// Independent angles for each qubit
    FullyParameterized,
    /// Linear interpolation
    LinearInterpolation { num_params: usize },
    /// Fourier series
    FourierSeries { num_frequencies: usize },
    /// Polynomial
    Polynomial { degree: usize },
}

#[derive(Debug, Clone)]
pub enum GateDecomposition {
    /// Standard decomposition
    Standard,
    /// Efficient for connectivity
    ConnectivityAware { topology: String },
    /// Approximate decomposition
    Approximate { fidelity: f64 },
}

#[derive(Debug, Clone)]
pub enum SymmetryType {
    /// Permutation symmetry
    Permutation { group: Vec<Vec<usize>> },
    /// Time reversal
    TimeReversal,
    /// Parity
    Parity,
    /// Custom symmetry
    Custom { name: String },
}

impl MultiAngleQAOA {
    /// Create new multi-angle QAOA
    pub const fn new(p: usize) -> Self {
        Self {
            p,
            parameterization: AngleParameterization::FullyParameterized,
            gate_decomposition: GateDecomposition::Standard,
            symmetries: Vec::new(),
        }
    }

    /// Set angle parameterization
    pub const fn with_parameterization(mut self, param: AngleParameterization) -> Self {
        self.parameterization = param;
        self
    }

    /// Add symmetry
    pub fn with_symmetry(mut self, symmetry: SymmetryType) -> Self {
        self.symmetries.push(symmetry);
        self
    }

    /// Generate angle parameters
    fn generate_angles(&self, base_params: &[f64]) -> Vec<Vec<f64>> {
        match &self.parameterization {
            AngleParameterization::FullyParameterized => {
                // Each qubit has independent angles
                vec![base_params.to_vec(); self.p]
            }
            AngleParameterization::LinearInterpolation { num_params } => {
                // Interpolate between control points
                self.linear_interpolation(base_params, *num_params)
            }
            AngleParameterization::FourierSeries { num_frequencies } => {
                // Fourier series representation
                self.fourier_series(base_params, *num_frequencies)
            }
            AngleParameterization::Polynomial { degree } => {
                // Polynomial parameterization
                self.polynomial_angles(base_params, *degree)
            }
        }
    }

    /// Linear interpolation of angles
    fn linear_interpolation(&self, control_points: &[f64], num_qubits: usize) -> Vec<Vec<f64>> {
        let mut angles = vec![vec![0.0; num_qubits]; self.p];

        for layer in 0..self.p {
            for qubit in 0..num_qubits {
                // Interpolate between control points
                let t = qubit as f64 / (num_qubits - 1) as f64;
                let idx = (t * (control_points.len() - 1) as f64) as usize;
                let frac = t * (control_points.len() - 1) as f64 - idx as f64;

                if idx + 1 < control_points.len() {
                    angles[layer][qubit] =
                        control_points[idx].mul_add(1.0 - frac, control_points[idx + 1] * frac);
                } else {
                    angles[layer][qubit] = control_points[idx];
                }
            }
        }

        angles
    }

    /// Fourier series representation
    fn fourier_series(&self, coeffs: &[f64], num_qubits: usize) -> Vec<Vec<f64>> {
        let mut angles = vec![vec![0.0; num_qubits]; self.p];

        for layer in 0..self.p {
            for qubit in 0..num_qubits {
                let x = 2.0 * PI * qubit as f64 / num_qubits as f64;

                for (k, &coeff) in coeffs.iter().enumerate() {
                    angles[layer][qubit] += coeff * (k as f64 * x).cos();
                }
            }
        }

        angles
    }

    /// Polynomial angle parameterization
    fn polynomial_angles(&self, coeffs: &[f64], degree: usize) -> Vec<Vec<f64>> {
        let mut angles = vec![vec![0.0; 10]; self.p]; // Placeholder

        for layer in 0..self.p {
            for (i, angle) in angles[layer].iter_mut().enumerate() {
                let x = i as f64 / 10.0; // Normalized position

                for (power, &coeff) in coeffs.iter().enumerate().take(degree + 1) {
                    *angle += coeff * x.powi(power as i32);
                }
            }
        }

        angles
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_adapt_qaoa() {
        let optimizer = ClassicalOptimizer::GradientDescent {
            learning_rate: 0.1,
            momentum: 0.9,
        };

        let adapt = AdaptQAOA::new(10, optimizer).with_gradient_threshold(1e-4);

        assert_eq!(adapt.max_depth, 10);
    }

    #[test]
    fn test_qaoa_plus() {
        let optimizer = ClassicalOptimizer::SPSA {
            a: 0.1,
            c: 0.1,
            alpha: 0.602,
            gamma: 0.101,
        };

        let qaoa_plus = QAOAPlus::new(3, optimizer).with_mixers(vec![
            MixerOperator::StandardX,
            MixerOperator::XYMixer {
                coupling_strength: 0.5,
            },
            MixerOperator::StandardX,
        ]);

        assert_eq!(qaoa_plus.p, 3);
        assert_eq!(qaoa_plus.mixers.len(), 3);
    }

    #[test]
    fn test_recursive_qaoa() {
        let recursive = RecursiveQAOA::new(
            2,
            3,
            DecompositionStrategy::GraphPartitioning { num_partitions: 4 },
        );

        assert_eq!(recursive.recursion_depth, 3);
    }

    #[test]
    fn test_multi_angle_qaoa() {
        let multi = MultiAngleQAOA::new(5)
            .with_parameterization(AngleParameterization::FourierSeries { num_frequencies: 3 });

        let mut coeffs = vec![1.0, 0.5, 0.25];
        let angles = multi.fourier_series(&coeffs, 10);

        assert_eq!(angles.len(), 5); // p layers
        assert_eq!(angles[0].len(), 10); // num qubits
    }
}