tensorlogic_quantrs_hooks/

quantum_simulation.rs

//! Quantum simulation for probabilistic inference.
//!
//! This module provides integration between quantum circuit simulation
//! and probabilistic graphical models, enabling quantum-enhanced inference.
//!
//! # Overview
//!
//! Key capabilities:
//! - Execute quantum circuits and extract probability distributions
//! - Convert circuit measurement results to PGM factors
//! - Quantum-enhanced sampling for factor graphs
//!
//! # Example
//!
//! ```no_run
//! use tensorlogic_quantrs_hooks::quantum_simulation::{
//!     QuantumSimulationBackend, SimulationConfig,
//! };
//!
//! // Create a simulation backend
//! let backend = QuantumSimulationBackend::new();
//!
//! // Run simulation
//! let config = SimulationConfig::default();
//! // ... execute circuits
//! ```

use crate::error::{PgmError, Result};
use crate::factor::Factor;
use crate::graph::FactorGraph;
use crate::quantum_circuit::{QAOAConfig, QAOAResult, QUBOProblem};
use crate::sampling::Assignment;
use quantrs2_sim::Complex64;
use quantrs2_sim::StateVectorSimulator;
use scirs2_core::ndarray::ArrayD;
use scirs2_core::random::{thread_rng, Rng, SeedableRng, StdRng};
use std::collections::HashMap;

/// Configuration for quantum simulation.
#[derive(Debug, Clone)]
pub struct SimulationConfig {
    /// Number of measurement shots
    pub num_shots: usize,
    /// Whether to track intermediate states
    pub track_states: bool,
    /// Noise level (if any)
    pub noise_level: f64,
    /// Seed for reproducibility
    pub seed: Option<u64>,
}

impl Default for SimulationConfig {
    fn default() -> Self {
        Self {
            num_shots: 1024,
            track_states: false,
            noise_level: 0.0,
            seed: None,
        }
    }
}

impl SimulationConfig {
    /// Create a new configuration with specified shots.
    pub fn with_shots(num_shots: usize) -> Self {
        Self {
            num_shots,
            ..Default::default()
        }
    }

    /// Set the random seed.
    pub fn with_seed(mut self, seed: u64) -> Self {
        self.seed = Some(seed);
        self
    }

    /// Enable state tracking.
    pub fn with_state_tracking(mut self) -> Self {
        self.track_states = true;
        self
    }

    /// Set noise level.
    pub fn with_noise(mut self, noise_level: f64) -> Self {
        self.noise_level = noise_level;
        self
    }
}

/// Internal state representation for simulation results.
#[derive(Debug, Clone)]
pub struct SimulatedState {
    /// State amplitudes
    pub amplitudes: Vec<Complex64>,
    /// Number of qubits
    pub num_qubits: usize,
}

impl SimulatedState {
    /// Create a new simulated state.
    pub fn new(num_qubits: usize) -> Self {
        let dim = 1 << num_qubits;
        let mut amplitudes = vec![Complex64::new(0.0, 0.0); dim];
        if dim > 0 {
            amplitudes[0] = Complex64::new(1.0, 0.0);
        }
        Self {
            amplitudes,
            num_qubits,
        }
    }

    /// Get the amplitudes.
    pub fn amplitudes(&self) -> &[Complex64] {
        &self.amplitudes
    }

    /// Get probabilities from amplitudes.
    pub fn probabilities(&self) -> Vec<f64> {
        self.amplitudes.iter().map(|a| a.norm_sqr()).collect()
    }
}

/// Backend for quantum circuit simulation.
///
/// This backend uses quantrs2_sim to execute quantum circuits
/// and provides methods to convert results to PGM formats.
pub struct QuantumSimulationBackend {
    /// Internal simulator (kept for future integration)
    #[allow(dead_code)]
    simulator: StateVectorSimulator,
    /// Current configuration
    config: SimulationConfig,
}

impl Default for QuantumSimulationBackend {
    fn default() -> Self {
        Self::new()
    }
}

impl QuantumSimulationBackend {
    /// Create a new simulation backend.
    pub fn new() -> Self {
        Self {
            simulator: StateVectorSimulator::new(),
            config: SimulationConfig::default(),
        }
    }

    /// Create with custom configuration.
    pub fn with_config(config: SimulationConfig) -> Self {
        Self {
            simulator: StateVectorSimulator::new(),
            config,
        }
    }

    /// Execute a quantum simulation with given number of qubits.
    ///
    /// For now, this creates an initial state. Full circuit execution
    /// will be integrated with quantrs2 in future versions.
    pub fn execute_state(&self, num_qubits: usize) -> Result<SimulatedState> {
        Ok(SimulatedState::new(num_qubits))
    }

    /// Sample from a state.
    pub fn sample_state(
        &self,
        state: &SimulatedState,
        num_samples: usize,
    ) -> Result<Vec<Vec<usize>>> {
        let probabilities = state.probabilities();
        let mut samples = Vec::with_capacity(num_samples);

        // Create RNG (seeded or random)
        let mut rng = if let Some(seed) = self.config.seed {
            StdRng::seed_from_u64(seed)
        } else {
            StdRng::from_rng(&mut thread_rng())
        };

        for _ in 0..num_samples {
            let bitstring = self.sample_bitstring(&probabilities, state.num_qubits, &mut rng);
            samples.push(bitstring);
        }

        Ok(samples)
    }

    /// Sample a single bitstring from probabilities.
    fn sample_bitstring(
        &self,
        probabilities: &[f64],
        num_qubits: usize,
        rng: &mut impl Rng,
    ) -> Vec<usize> {
        let u: f64 = rng.random();
        let mut cumulative = 0.0;

        for (idx, &prob) in probabilities.iter().enumerate() {
            cumulative += prob;
            if u <= cumulative {
                // Convert index to bitstring
                return (0..num_qubits).map(|bit| (idx >> bit) & 1).collect();
            }
        }

        // Fallback: all zeros
        vec![0; num_qubits]
    }

    /// Convert simulation results to a PGM factor.
    ///
    /// Creates a factor representing the probability distribution over
    /// the measured qubits.
    pub fn state_to_factor(
        &self,
        state: &SimulatedState,
        variable_names: &[String],
    ) -> Result<Factor> {
        if variable_names.len() != state.num_qubits {
            return Err(PgmError::InvalidGraph(format!(
                "Variable count {} doesn't match qubit count {}",
                variable_names.len(),
                state.num_qubits
            )));
        }

        // Create factor with probabilities
        let probabilities = state.probabilities();
        let shape: Vec<usize> = vec![2; state.num_qubits];
        let values = ArrayD::from_shape_vec(shape, probabilities)
            .map_err(|e| PgmError::InvalidGraph(format!("Shape error: {}", e)))?;

        // Factor::new takes (name, variables, values)
        let name = format!("quantum_{}", variable_names.join("_"));
        Factor::new(name, variable_names.to_vec(), values)
    }

    /// Sample from a factor graph using quantum-enhanced methods.
    ///
    /// This is a placeholder for QAOA-based sampling.
    pub fn quantum_sample(&self, graph: &FactorGraph, num_shots: usize) -> Result<Vec<Assignment>> {
        // For now, fall back to classical sampling
        // Future: implement QAOA-based sampling
        let variables: Vec<_> = graph.variable_names().collect();

        let mut rng = if let Some(seed) = self.config.seed {
            StdRng::seed_from_u64(seed)
        } else {
            StdRng::from_rng(&mut thread_rng())
        };

        let mut samples = Vec::with_capacity(num_shots);

        for _ in 0..num_shots {
            let mut assignment: Assignment = HashMap::new();
            for var in &variables {
                // Simple uniform sampling (placeholder)
                let value = if rng.random::<f64>() < 0.5 { 0 } else { 1 };
                let var_str: String = var.to_string();
                assignment.insert(var_str, value);
            }
            samples.push(assignment);
        }

        Ok(samples)
    }
}

/// Run a QAOA optimization.
///
/// Solves the QUBO problem using the Quantum Approximate Optimization Algorithm.
pub fn run_qaoa(
    qubo: &QUBOProblem,
    config: &QAOAConfig,
    _backend: &QuantumSimulationBackend,
) -> Result<QAOAResult> {
    let num_vars = qubo.num_variables;

    // For now, return a placeholder result
    // Full QAOA implementation would involve variational optimization
    let mut solution: Vec<usize> = vec![0; num_vars];
    let mut rng = thread_rng();

    for slot in solution.iter_mut().take(num_vars) {
        *slot = if rng.random::<f64>() < 0.5 { 0 } else { 1 };
    }

    // Compute cost using QUBO: x^T Q x + c^T x + offset
    let mut cost = qubo.offset;
    for (i, &xi_val) in solution.iter().enumerate().take(num_vars) {
        let xi = xi_val as f64;
        cost += qubo.linear[i] * xi;

        for (j, &xj_val) in solution.iter().enumerate().take(num_vars).skip(i + 1) {
            let xj = xj_val as f64;
            cost += qubo.quadratic[[i, j]] * xi * xj;
        }
    }

    Ok(QAOAResult {
        gamma: vec![0.0; config.num_layers],
        beta: vec![0.0; config.num_layers],
        best_solution: solution,
        best_value: cost,
        iterations: 1,
    })
}

/// Convert a factor graph to a quantum simulation.
pub fn factor_graph_to_quantum(_graph: &FactorGraph, num_shots: usize) -> Result<Vec<Assignment>> {
    let backend = QuantumSimulationBackend::new();
    let state = backend.execute_state(2)?; // Placeholder
    let _samples = backend.sample_state(&state, num_shots)?;

    // Return empty for now - full implementation needs circuit construction
    Ok(Vec::new())
}

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

    #[test]
    fn test_simulation_config() {
        let config = SimulationConfig::with_shots(2048)
            .with_seed(42)
            .with_noise(0.01);

        assert_eq!(config.num_shots, 2048);
        assert_eq!(config.seed, Some(42));
        assert!((config.noise_level - 0.01).abs() < 1e-10);
    }

    #[test]
    fn test_backend_creation() {
        let backend = QuantumSimulationBackend::new();
        let state = backend.execute_state(3).expect("Execute failed");

        assert_eq!(state.num_qubits, 3);
        assert_eq!(state.amplitudes.len(), 8); // 2^3
    }

    #[test]
    fn test_sampling() {
        let config = SimulationConfig::with_shots(100).with_seed(42);
        let backend = QuantumSimulationBackend::with_config(config);

        let state = backend.execute_state(2).expect("Execute failed");
        let samples = backend.sample_state(&state, 100).expect("Sample failed");

        assert_eq!(samples.len(), 100);
        for sample in samples {
            assert_eq!(sample.len(), 2);
        }
    }

    #[test]
    fn test_state_probabilities() {
        let state = SimulatedState::new(2);
        let probs = state.probabilities();

        // Initial state |00⟩ should have probability 1 for |00⟩
        assert!((probs[0] - 1.0).abs() < 1e-10);
        assert!(probs[1..].iter().all(|&p| p.abs() < 1e-10));
    }

    #[test]
    fn test_state_to_factor() {
        let backend = QuantumSimulationBackend::new();
        let state = backend.execute_state(2).expect("Execute failed");

        let factor = backend
            .state_to_factor(&state, &["x".to_string(), "y".to_string()])
            .expect("Factor creation failed");

        assert_eq!(factor.variables.len(), 2);
    }
}