scirs2-series 0.1.5

//! Quantum-Inspired Time Series Forecasting
//!
//! This module implements cutting-edge quantum-inspired algorithms for time series analysis,
//! including quantum attention mechanisms, variational quantum circuits, and quantum kernel methods.
//! These implementations leverage quantum computing principles for enhanced pattern recognition
//! and forecasting capabilities.
//!
//! ## Quantum-Inspired Architectures
//! - **Quantum Attention**: Superposition-based attention mechanisms
//! - **Variational Quantum Circuits**: Quantum neural networks for time series
//! - **Quantum Kernel Methods**: Distance metrics using quantum similarity measures
//! - **Quantum-Inspired Optimization**: Quantum annealing for hyperparameter tuning

use scirs2_core::ndarray::{Array1, Array2, Array3};
use scirs2_core::numeric::Complex;
use scirs2_core::numeric::{Float, FromPrimitive};
use std::collections::HashMap;
use std::fmt::Debug;

use crate::error::{Result, TimeSeriesError};

/// Quantum state representation using complex amplitudes
#[derive(Debug, Clone)]
pub struct QuantumState<F: Float + Debug> {
    /// Complex amplitudes for quantum state
    #[allow(dead_code)]
    amplitudes: Array1<Complex<F>>,
    /// Number of qubits
    #[allow(dead_code)]
    num_qubits: usize,
}

impl<F: Float + Debug + Clone + FromPrimitive> QuantumState<F> {
    /// Create new quantum state
    pub fn new(_numqubits: usize) -> Self {
        let num_states = 1 << _numqubits; // 2^_num_qubits
        let mut amplitudes = Array1::zeros(num_states);

        // Initialize in |0...0⟩ state
        amplitudes[0] = Complex::new(F::one(), F::zero());

        Self {
            amplitudes,
            num_qubits: _numqubits,
        }
    }

    /// Create superposition state
    pub fn create_superposition(&mut self) {
        let num_states = self.amplitudes.len();
        let amplitude = F::one()
            / F::from(num_states as f64)
                .expect("Failed to convert to float")
                .sqrt();

        for i in 0..num_states {
            self.amplitudes[i] = Complex::new(amplitude, F::zero());
        }
    }

    /// Apply quantum gate (simplified)
    pub fn apply_rotation(&mut self, qubit: usize, theta: F, phi: F) -> Result<()> {
        if qubit >= self.num_qubits {
            return Err(TimeSeriesError::InvalidInput(
                "Qubit index out of bounds".to_string(),
            ));
        }

        let cos_half = (theta / F::from(2.0).expect("Failed to convert constant to float")).cos();
        let sin_half = (theta / F::from(2.0).expect("Failed to convert constant to float")).sin();
        let exp_phi = Complex::new(phi.cos(), phi.sin());

        let num_states = self.amplitudes.len();
        let qubit_mask = 1 << qubit;

        for i in 0..num_states {
            if i & qubit_mask == 0 {
                let j = i | qubit_mask;
                let old_i = self.amplitudes[i];
                let old_j = self.amplitudes[j];

                self.amplitudes[i] = old_i * Complex::new(cos_half, F::zero())
                    - old_j * Complex::new(sin_half, F::zero()) * exp_phi;
                self.amplitudes[j] = old_i * Complex::new(sin_half, F::zero()) * exp_phi.conj()
                    + old_j * Complex::new(cos_half, F::zero());
            }
        }

        Ok(())
    }

    /// Measure quantum state (probabilistic collapse)
    pub fn measure(&self) -> (usize, F) {
        let mut probabilities = Array1::zeros(self.amplitudes.len());

        for (i, &amplitude) in self.amplitudes.iter().enumerate() {
            probabilities[i] = amplitude.norm_sqr();
        }

        // Find maximum probability (simplified measurement)
        let mut max_prob = F::zero();
        let mut max_idx = 0;

        for (i, &prob) in probabilities.iter().enumerate() {
            if prob > max_prob {
                max_prob = prob;
                max_idx = i;
            }
        }

        (max_idx, max_prob)
    }

    /// Get probability distribution
    pub fn get_probabilities(&self) -> Array1<F> {
        let mut probabilities = Array1::zeros(self.amplitudes.len());

        for (i, &amplitude) in self.amplitudes.iter().enumerate() {
            probabilities[i] = amplitude.norm_sqr();
        }

        // Normalize probabilities to sum to 1.0
        let total: F = probabilities.sum();
        if total > F::zero() {
            probabilities.mapv_inplace(|p| p / total);
        }

        probabilities
    }
}

/// Quantum Attention Mechanism using superposition principles
#[derive(Debug)]
pub struct QuantumAttention<F: Float + Debug> {
    /// Model dimension
    #[allow(dead_code)]
    model_dim: usize,
    /// Number of attention heads
    num_heads: usize,
    /// Number of qubits per head
    #[allow(dead_code)]
    qubits_per_head: usize,
    /// Quantum parameters
    #[allow(dead_code)]
    theta_params: Array2<F>,
    #[allow(dead_code)]
    phi_params: Array2<F>,
    /// Classical projection layers
    #[allow(dead_code)]
    w_query: Array2<F>,
    #[allow(dead_code)]
    w_key: Array2<F>,
    #[allow(dead_code)]
    w_value: Array2<F>,
    #[allow(dead_code)]
    w_output: Array2<F>,
}

impl<F: Float + Debug + Clone + FromPrimitive> QuantumAttention<F> {
    /// Create new quantum attention layer
    pub fn new(_model_dim: usize, num_heads: usize, qubits_perhead: usize) -> Result<Self> {
        if !_model_dim.is_multiple_of(num_heads) {
            return Err(TimeSeriesError::InvalidInput(
                "Model dimension must be divisible by number of _heads".to_string(),
            ));
        }

        let scale = F::from(2.0).expect("Failed to convert constant to float")
            / F::from(_model_dim).expect("Failed to convert to float");
        let std_dev = scale.sqrt();

        // Initialize quantum parameters
        let theta_params = Self::init_params(num_heads, qubits_perhead);
        let phi_params = Self::init_params(num_heads, qubits_perhead);

        Ok(Self {
            model_dim: _model_dim,
            num_heads,
            qubits_per_head: qubits_perhead,
            theta_params,
            phi_params,
            w_query: Self::random_matrix(_model_dim, _model_dim, std_dev),
            w_key: Self::random_matrix(_model_dim, _model_dim, std_dev),
            w_value: Self::random_matrix(_model_dim, _model_dim, std_dev),
            w_output: Self::random_matrix(_model_dim, _model_dim, std_dev),
        })
    }

    /// Initialize quantum parameters
    fn init_params(_num_heads: usize, qubits_perhead: usize) -> Array2<F> {
        let mut params = Array2::zeros((_num_heads, qubits_perhead));

        for i in 0.._num_heads {
            for j in 0..qubits_perhead {
                // Initialize with random angles
                let angle = F::from(((i + j * 7) % 100) as f64 / 100.0 * std::f64::consts::PI)
                    .expect("Operation failed");
                params[[i, j]] = angle;
            }
        }

        params
    }

    /// Random matrix initialization
    fn random_matrix(_rows: usize, cols: usize, stddev: F) -> Array2<F> {
        let mut matrix = Array2::zeros((_rows, cols));

        for i in 0.._rows {
            for j in 0..cols {
                let rand_val = ((i + j * 17) % 1000) as f64 / 1000.0 - 0.5;
                matrix[[i, j]] = F::from(rand_val).expect("Failed to convert to float") * stddev;
            }
        }

        matrix
    }

    /// Quantum attention forward pass
    pub fn forward(&self, input: &Array2<F>) -> Result<Array2<F>> {
        let (seq_len, _) = input.dim();

        // Classical projections
        let queries = self.linear_transform(input, &self.w_query);
        let keys = self.linear_transform(input, &self.w_key);
        let values = self.linear_transform(input, &self.w_value);

        // Quantum attention computation
        let mut attention_outputs = Vec::new();

        for head in 0..self.num_heads {
            let quantum_attention =
                self.quantum_attention_head(&queries, &keys, &values, head, seq_len)?;
            attention_outputs.push(quantum_attention);
        }

        // Concatenate heads
        let concatenated = self.concatenate_heads(&attention_outputs, seq_len);

        // Output projection
        let output = self.linear_transform(&concatenated, &self.w_output);

        Ok(output)
    }

    /// Quantum attention computation for single head
    fn quantum_attention_head(
        &self,
        queries: &Array2<F>,
        keys: &Array2<F>,
        values: &Array2<F>,
        head: usize,
        seq_len: usize,
    ) -> Result<Array2<F>> {
        let head_dim = self.model_dim / self.num_heads;
        let mut output = Array2::zeros((seq_len, head_dim));

        for i in 0..seq_len {
            // Create quantum state for this position
            let mut quantum_state = QuantumState::new(self.qubits_per_head);
            quantum_state.create_superposition();

            // Apply quantum rotations based on query-key interactions
            for j in 0..seq_len {
                // Compute query-key similarity
                let mut similarity = F::zero();
                for d in 0..head_dim.min(queries.ncols()).min(keys.ncols()) {
                    let q_idx = head * head_dim + d;
                    let k_idx = head * head_dim + d;
                    if q_idx < queries.ncols() && k_idx < keys.ncols() {
                        similarity = similarity + queries[[i, q_idx]] * keys[[j, k_idx]];
                    }
                }

                // Apply quantum gates based on similarity
                let theta = self.theta_params[[head, j % self.qubits_per_head]] * similarity;
                let phi = self.phi_params[[head, j % self.qubits_per_head]] * similarity;

                if j % self.qubits_per_head < self.qubits_per_head {
                    quantum_state.apply_rotation(j % self.qubits_per_head, theta, phi)?;
                }
            }

            // Measure quantum state to get attention weights
            let probabilities = quantum_state.get_probabilities();

            // Apply quantum attention to values
            for d in 0..head_dim {
                let mut weighted_value = F::zero();

                for j in 0..seq_len.min(probabilities.len()) {
                    let v_idx = head * head_dim + d;
                    if v_idx < values.ncols() && j < values.nrows() {
                        weighted_value = weighted_value + probabilities[j] * values[[j, v_idx]];
                    }
                }

                output[[i, d]] = weighted_value;
            }
        }

        Ok(output)
    }

    /// Helper methods
    fn linear_transform(&self, input: &Array2<F>, weights: &Array2<F>) -> Array2<F> {
        let (seq_len, input_dim) = input.dim();
        let output_dim = weights.nrows();
        let mut output = Array2::zeros((seq_len, output_dim));

        for i in 0..seq_len {
            for j in 0..output_dim {
                let mut sum = F::zero();
                for k in 0..input_dim.min(weights.ncols()) {
                    sum = sum + input[[i, k]] * weights[[j, k]];
                }
                output[[i, j]] = sum;
            }
        }

        output
    }

    fn concatenate_heads(&self, heads: &[Array2<F>], seqlen: usize) -> Array2<F> {
        let head_dim = self.model_dim / self.num_heads;
        let mut concatenated = Array2::zeros((seqlen, self.model_dim));

        for (h, head_output) in heads.iter().enumerate() {
            for i in 0..seqlen.min(head_output.nrows()) {
                for j in 0..head_dim.min(head_output.ncols()) {
                    concatenated[[i, h * head_dim + j]] = head_output[[i, j]];
                }
            }
        }

        concatenated
    }
}

/// Variational Quantum Circuit for time series pattern recognition
#[derive(Debug)]
pub struct VariationalQuantumCircuit<F: Float + Debug> {
    /// Number of qubits
    num_qubits: usize,
    /// Circuit depth (number of layers)
    #[allow(dead_code)]
    depth: usize,
    /// Variational parameters
    #[allow(dead_code)]
    parameters: Array3<F>, // [layer, qubit, parameter_type]
    /// Input encoding dimension
    input_dim: usize,
}

impl<F: Float + Debug + Clone + FromPrimitive> VariationalQuantumCircuit<F> {
    /// Create new variational quantum circuit
    pub fn new(_num_qubits: usize, depth: usize, inputdim: usize) -> Self {
        // Initialize parameters randomly
        let mut parameters = Array3::zeros((depth, _num_qubits, 3)); // 3 parameters per qubit per layer

        for layer in 0..depth {
            for qubit in 0.._num_qubits {
                for param in 0..3 {
                    let val = ((layer + qubit * 7 + param * 13) % 1000) as f64 / 1000.0
                        * std::f64::consts::PI
                        * 2.0;
                    parameters[[layer, qubit, param]] =
                        F::from(val).expect("Failed to convert to float");
                }
            }
        }

        Self {
            num_qubits: _num_qubits,
            depth,
            parameters,
            input_dim: inputdim,
        }
    }

    /// Encode classical data into quantum state
    pub fn encode_data(&self, data: &Array1<F>) -> Result<QuantumState<F>> {
        let mut state = QuantumState::new(self.num_qubits);

        // Amplitude encoding (simplified)
        for (i, &value) in data.iter().enumerate().take(self.num_qubits) {
            let angle = value * F::from(std::f64::consts::PI).expect("Failed to convert to float");
            state.apply_rotation(i, angle, F::zero())?;
        }

        Ok(state)
    }

    /// Forward pass through variational circuit
    pub fn forward(&self, input: &Array1<F>) -> Result<Array1<F>> {
        if input.len() < self.input_dim {
            return Err(TimeSeriesError::DimensionMismatch {
                expected: self.input_dim,
                actual: input.len(),
            });
        }

        // Encode input data
        let mut state = self.encode_data(input)?;

        // Apply variational layers
        for layer in 0..self.depth {
            self.apply_variational_layer(&mut state, layer)?;
        }

        // Extract expectation values for each qubit
        let probabilities = state.get_probabilities();
        let output_dim = self.num_qubits; // Output dimension equals number of qubits
        let mut output = Array1::zeros(output_dim);

        // Compute expectation value for each qubit (probability of being in |1⟩ state)
        for qubit in 0..output_dim {
            let mut prob_one = F::zero();
            let qubit_mask = 1 << qubit;

            for (state_idx, &prob) in probabilities.iter().enumerate() {
                if state_idx & qubit_mask != 0 {
                    prob_one = prob_one + prob;
                }
            }

            output[qubit] = prob_one;
        }

        Ok(output)
    }

    /// Apply single variational layer
    fn apply_variational_layer(&self, state: &mut QuantumState<F>, layer: usize) -> Result<()> {
        // Single-qubit rotations
        for qubit in 0..self.num_qubits {
            let theta = self.parameters[[layer, qubit, 0]];
            let phi = self.parameters[[layer, qubit, 1]];
            state.apply_rotation(qubit, theta, phi)?;
        }

        // Entangling gates (simplified - just additional rotations)
        for qubit in 0..self.num_qubits - 1 {
            let entangle_angle = self.parameters[[layer, qubit, 2]];
            state.apply_rotation(qubit, entangle_angle, F::zero())?;
            state.apply_rotation(qubit + 1, entangle_angle, F::zero())?;
        }

        Ok(())
    }

    /// Update variational parameters (for training)
    pub fn update_parameters(&mut self, gradients: &Array3<F>, learningrate: F) {
        for layer in 0..self.depth {
            for qubit in 0..self.num_qubits {
                for param in 0..3 {
                    if layer < gradients.shape()[0]
                        && qubit < gradients.shape()[1]
                        && param < gradients.shape()[2]
                    {
                        self.parameters[[layer, qubit, param]] = self.parameters
                            [[layer, qubit, param]]
                            - learningrate * gradients[[layer, qubit, param]];
                    }
                }
            }
        }
    }
}

/// Quantum Kernel Methods for time series similarity
#[derive(Debug)]
pub struct QuantumKernel<F: Float + Debug> {
    /// Number of qubits for encoding
    num_qubits: usize,
    /// Feature map parameters
    feature_map_params: Array2<F>,
    /// Kernel type
    kernel_type: QuantumKernelType,
}

#[derive(Debug, Clone)]
/// Quantum kernel types for quantum machine learning
pub enum QuantumKernelType {
    /// Quantum feature map kernel
    FeatureMap,
    /// Quantum fidelity kernel
    Fidelity,
    /// Quantum distance kernel
    Distance,
}

impl<F: Float + Debug + Clone + FromPrimitive> QuantumKernel<F> {
    /// Create new quantum kernel
    pub fn new(_num_qubits: usize, kerneltype: QuantumKernelType) -> Self {
        let mut feature_map_params = Array2::zeros((_num_qubits, 3));

        // Initialize feature map parameters
        for i in 0.._num_qubits {
            for j in 0..3 {
                let val = ((i + j * 11) % 100) as f64 / 100.0 * std::f64::consts::PI;
                feature_map_params[[i, j]] = F::from(val).expect("Failed to convert to float");
            }
        }

        Self {
            num_qubits: _num_qubits,
            feature_map_params,
            kernel_type: kerneltype,
        }
    }

    /// Compute quantum kernel between two time series
    pub fn compute_kernel(&self, x1: &Array1<F>, x2: &Array1<F>) -> Result<F> {
        match self.kernel_type {
            QuantumKernelType::FeatureMap => self.feature_map_kernel(x1, x2),
            QuantumKernelType::Fidelity => self.fidelity_kernel(x1, x2),
            QuantumKernelType::Distance => self.distance_kernel(x1, x2),
        }
    }

    /// Feature map quantum kernel
    fn feature_map_kernel(&self, x1: &Array1<F>, x2: &Array1<F>) -> Result<F> {
        let state1 = self.create_feature_map(x1)?;
        let state2 = self.create_feature_map(x2)?;

        // Compute overlap between quantum states
        let mut overlap = Complex::new(F::zero(), F::zero());

        for i in 0..state1.amplitudes.len().min(state2.amplitudes.len()) {
            overlap = overlap + state1.amplitudes[i].conj() * state2.amplitudes[i];
        }

        Ok(overlap.norm_sqr())
    }

    /// Quantum fidelity kernel
    fn fidelity_kernel(&self, x1: &Array1<F>, x2: &Array1<F>) -> Result<F> {
        // Simplified fidelity computation
        let mut fidelity = F::zero();
        let min_len = x1.len().min(x2.len());

        for i in 0..min_len {
            let diff = x1[i] - x2[i];
            fidelity = fidelity + (-diff * diff).exp();
        }

        Ok(fidelity / F::from(min_len).expect("Failed to convert to float"))
    }

    /// Quantum distance kernel
    fn distance_kernel(&self, x1: &Array1<F>, x2: &Array1<F>) -> Result<F> {
        let state1 = self.create_feature_map(x1)?;
        let state2 = self.create_feature_map(x2)?;

        // Compute quantum distance
        let mut distance = F::zero();

        for i in 0..state1.amplitudes.len().min(state2.amplitudes.len()) {
            let diff = state1.amplitudes[i] - state2.amplitudes[i];
            distance = distance + diff.norm_sqr();
        }

        // Convert distance to similarity
        let gamma = F::from(0.1).expect("Failed to convert constant to float");
        Ok((-gamma * distance).exp())
    }

    /// Create quantum feature map
    fn create_feature_map(&self, data: &Array1<F>) -> Result<QuantumState<F>> {
        let mut state = QuantumState::new(self.num_qubits);

        // Apply feature map encoding
        for (i, &value) in data.iter().enumerate().take(self.num_qubits) {
            let theta = self.feature_map_params[[i, 0]] * value;
            let phi = self.feature_map_params[[i, 1]] * value;
            state.apply_rotation(i, theta, phi)?;
        }

        // Apply entangling operations
        for i in 0..self.num_qubits - 1 {
            let entangle_param = self.feature_map_params[[i, 2]];
            state.apply_rotation(i, entangle_param, F::zero())?;
            state.apply_rotation(i + 1, entangle_param, F::zero())?;
        }

        Ok(state)
    }

    /// Compute kernel matrix for a set of time series
    pub fn compute_kernel_matrix(&self, data: &Array2<F>) -> Result<Array2<F>> {
        let num_samples = data.nrows();
        let mut kernel_matrix = Array2::zeros((num_samples, num_samples));

        for i in 0..num_samples {
            for j in i..num_samples {
                let row_i = data.row(i).to_owned();
                let row_j = data.row(j).to_owned();
                let kernel_value = self.compute_kernel(&row_i, &row_j)?;

                kernel_matrix[[i, j]] = kernel_value;
                kernel_matrix[[j, i]] = kernel_value; // Symmetric
            }
        }

        Ok(kernel_matrix)
    }
}

/// Quantum-Inspired Optimization for hyperparameter tuning
#[derive(Debug)]
pub struct QuantumAnnealingOptimizer<F: Float + Debug> {
    /// Number of variables to optimize
    num_vars: usize,
    /// Temperature schedule
    temperature_schedule: Array1<F>,
    /// Current solution
    current_solution: Array1<F>,
    /// Best solution found
    best_solution: Array1<F>,
    /// Best energy (objective value)
    best_energy: F,
}

impl<F: Float + Debug + Clone + FromPrimitive> QuantumAnnealingOptimizer<F> {
    /// Create new quantum annealing optimizer
    pub fn new(_num_vars: usize, maxiterations: usize) -> Self {
        // Create temperature schedule (exponential cooling)
        let mut temperature_schedule = Array1::zeros(maxiterations);
        let initial_temp = F::from(10.0).expect("Failed to convert constant to float");
        let final_temp = F::from(0.01).expect("Failed to convert constant to float");
        let cooling_rate = (final_temp / initial_temp).ln()
            / F::from(maxiterations as f64).expect("Failed to convert to float");

        for i in 0..maxiterations {
            let temp = initial_temp
                * (cooling_rate * F::from(i as f64).expect("Failed to convert to float")).exp();
            temperature_schedule[i] = temp;
        }

        // Initialize random solution closer to center
        let mut current_solution = Array1::zeros(_num_vars);
        for i in 0.._num_vars {
            current_solution[i] = F::from(0.5 + (i as f64 * 0.1 - 0.05)).expect("Operation failed");
        }

        Self {
            num_vars: _num_vars,
            temperature_schedule,
            current_solution: current_solution.clone(),
            best_solution: current_solution,
            best_energy: F::from(f64::INFINITY).expect("Failed to convert to float"),
        }
    }

    /// Optimize objective function using quantum annealing
    pub fn optimize<Func>(&mut self, objectivefunction: Func) -> Result<Array1<F>>
    where
        Func: Fn(&Array1<F>) -> F,
    {
        let max_iterations = self.temperature_schedule.len();

        for iteration in 0..max_iterations {
            let temperature = self.temperature_schedule[iteration];

            // Generate neighbor solution (quantum tunneling effect)
            let neighbor = self.generate_neighbor_solution(temperature);

            // Evaluate objective _function
            let current_energy = objectivefunction(&self.current_solution);
            let neighbor_energy = objectivefunction(&neighbor);

            // Accept or reject neighbor (Metropolis criterion)
            let energy_diff = neighbor_energy - current_energy;
            let acceptance_prob = if energy_diff < F::zero() {
                F::one() // Always accept better solutions
            } else {
                (-energy_diff / temperature).exp()
            };

            // Simplified random decision (deterministic for reproducibility)
            let random_val =
                F::from(((iteration * 17) % 1000) as f64 / 1000.0).expect("Operation failed");

            if random_val < acceptance_prob {
                self.current_solution = neighbor;

                // Update best solution
                if neighbor_energy < self.best_energy {
                    self.best_energy = neighbor_energy;
                    self.best_solution = self.current_solution.clone();
                }
            }
        }

        Ok(self.best_solution.clone())
    }

    /// Generate neighbor solution with quantum tunneling
    fn generate_neighbor_solution(&self, temperature: F) -> Array1<F> {
        let mut neighbor = self.current_solution.clone();

        // Apply quantum tunneling effect (larger jumps at higher temperature)
        for i in 0..self.num_vars {
            let perturbation_scale =
                temperature / F::from(5.0).expect("Failed to convert constant to float"); // Increased from 10.0 to 5.0
            let perturbation = F::from(((i * 23) % 1000) as f64 / 1000.0 - 0.5)
                .expect("Operation failed")
                * perturbation_scale;

            neighbor[i] = neighbor[i] + perturbation;

            // Clip to valid range [0, 1]
            if neighbor[i] < F::zero() {
                neighbor[i] = F::zero();
            } else if neighbor[i] > F::one() {
                neighbor[i] = F::one();
            }
        }

        neighbor
    }
}

// Additional quantum forecasting types and implementations
#[path = "quantum_forecasting_tests.rs"]
mod quantum_extended;

// Re-export public types from extended module
pub use quantum_extended::{
    QuantumActivation, QuantumEnsemble, QuantumEnsembleMethod, QuantumNeuralNetwork,
};