quantrs2-tytan 0.1.3

//! Hitachi CMOS Annealing Machine integration
//!
//! This module provides integration with Hitachi's CMOS Annealing Machine,
//! a semiconductor-based annealing processor.

use crate::sampler::{SampleResult, Sampler, SamplerError, SamplerResult};
use scirs2_core::ndarray::Array2;
use std::cell::RefCell;
use std::collections::HashMap;

/// Hitachi CMOS Annealing Machine configuration
#[derive(Debug, Clone)]
pub struct HitachiConfig {
    /// API endpoint
    pub endpoint: String,
    /// Authentication token
    pub auth_token: String,
    /// Annealing parameters
    pub annealing_params: AnnealingParameters,
    /// Hardware version
    pub hardware_version: HardwareVersion,
}

#[derive(Debug, Clone)]
pub struct AnnealingParameters {
    /// Number of annealing steps
    pub num_steps: u32,
    /// Initial spin configuration
    pub initial_config: InitialConfig,
    /// Magnetic field strength
    pub magnetic_field: f64,
    /// Temperature coefficient
    pub temperature_coefficient: f64,
    /// Convergence threshold
    pub convergence_threshold: f64,
}

#[derive(Debug, Clone)]
pub enum InitialConfig {
    /// Random initial configuration
    Random,
    /// All spins up
    AllUp,
    /// All spins down
    AllDown,
    /// Custom configuration
    Custom(Vec<i8>),
}

#[derive(Debug, Clone)]
pub enum HardwareVersion {
    /// Generation 4 hardware
    Gen4 { king_graph_size: usize },
    /// Generation 5 hardware with enhanced connectivity
    Gen5 {
        king_graph_size: usize,
        long_range_connections: bool,
    },
}

impl Default for HitachiConfig {
    fn default() -> Self {
        Self {
            endpoint: "https://annealing.hitachi.com/api/v1".to_string(),
            auth_token: String::new(),
            annealing_params: AnnealingParameters {
                num_steps: 100_000,
                initial_config: InitialConfig::Random,
                magnetic_field: 0.0,
                temperature_coefficient: 1.0,
                convergence_threshold: 1e-6,
            },
            hardware_version: HardwareVersion::Gen4 {
                king_graph_size: 512,
            },
        }
    }
}

/// Hitachi CMOS Annealing Machine sampler
pub struct HitachiCMOSSampler {
    config: HitachiConfig,
    /// Problem embedding cache
    embedding_cache: RefCell<HashMap<String, KingGraphEmbedding>>,
}

/// King graph embedding information
#[derive(Debug, Clone)]
struct KingGraphEmbedding {
    /// Logical to physical qubit mapping
    logical_to_physical: HashMap<usize, Vec<usize>>,
    /// Chain strengths
    chain_strengths: Vec<f64>,
    /// Embedding quality score
    quality_score: f64,
}

impl HitachiCMOSSampler {
    /// Create new Hitachi CMOS sampler
    pub fn new(config: HitachiConfig) -> Self {
        Self {
            config,
            embedding_cache: RefCell::new(HashMap::new()),
        }
    }

    /// Find embedding for the problem
    fn find_embedding(&self, qubo: &Array2<f64>) -> Result<KingGraphEmbedding, SamplerError> {
        let n = qubo.shape()[0];

        // Check cache
        let cache_key = format!("embed_{}_{}", n, qubo.sum());
        if let Some(embedding) = self.embedding_cache.borrow().get(&cache_key) {
            return Ok(embedding.clone());
        }

        // Create new embedding
        let embedding = self.create_king_graph_embedding(qubo)?;

        // Cache it
        self.embedding_cache
            .borrow_mut()
            .insert(cache_key, embedding.clone());

        Ok(embedding)
    }

    /// Create king graph embedding
    fn create_king_graph_embedding(
        &self,
        qubo: &Array2<f64>,
    ) -> Result<KingGraphEmbedding, SamplerError> {
        let n = qubo.shape()[0];
        let king_size = match &self.config.hardware_version {
            HardwareVersion::Gen4 { king_graph_size } => *king_graph_size,
            HardwareVersion::Gen5 {
                king_graph_size, ..
            } => *king_graph_size,
        };

        if n > king_size {
            return Err(SamplerError::InvalidModel(format!(
                "Problem size {n} exceeds hardware limit {king_size}"
            )));
        }

        // Simple direct embedding for now
        let mut logical_to_physical = HashMap::new();
        for i in 0..n {
            logical_to_physical.insert(i, vec![i]);
        }

        Ok(KingGraphEmbedding {
            logical_to_physical,
            chain_strengths: vec![1.0; n],
            quality_score: 1.0,
        })
    }

    /// Submit job to hardware
    fn submit_job(&self, _embedded_qubo: &Array2<f64>) -> Result<String, SamplerError> {
        // Placeholder for API call
        Ok("hitachi_job_123".to_string())
    }

    /// Retrieve results
    fn get_job_results(&self, _job_id: &str) -> Result<Vec<CMOSResult>, SamplerError> {
        // Placeholder for API call
        Ok(vec![CMOSResult {
            spins: vec![1; 512],
            energy: -50.0,
            converged: true,
            iterations: 50000,
        }])
    }

    /// Unembed solution
    fn unembed_solution(
        &self,
        cmos_result: &CMOSResult,
        embedding: &KingGraphEmbedding,
        var_map: &HashMap<String, usize>,
    ) -> SampleResult {
        let mut assignments = HashMap::new();

        // Map physical spins back to logical variables
        for (var_name, &logical_idx) in var_map {
            if let Some(physical_qubits) = embedding.logical_to_physical.get(&logical_idx) {
                // Take majority vote for chains
                let spin_sum: i32 = physical_qubits
                    .iter()
                    .map(|&p| cmos_result.spins[p] as i32)
                    .sum();

                let value = spin_sum > 0;
                assignments.insert(var_name.clone(), value);
            }
        }

        SampleResult {
            assignments,
            energy: cmos_result.energy,
            occurrences: 1,
        }
    }
}

#[derive(Debug, Clone)]
struct CMOSResult {
    /// Spin configuration (-1 or +1)
    spins: Vec<i8>,
    /// Solution energy
    energy: f64,
    /// Whether annealing converged
    converged: bool,
    /// Number of iterations used
    iterations: u32,
}

impl Sampler for HitachiCMOSSampler {
    fn run_qubo(
        &self,
        model: &(Array2<f64>, HashMap<String, usize>),
        shots: usize,
    ) -> SamplerResult<Vec<SampleResult>> {
        let (qubo, var_map) = model;

        // Find embedding
        let embedding = self.find_embedding(qubo)?;

        // Embed QUBO into king graph
        let king_size = match &self.config.hardware_version {
            HardwareVersion::Gen4 { king_graph_size } => *king_graph_size,
            HardwareVersion::Gen5 {
                king_graph_size, ..
            } => *king_graph_size,
        };

        let mut embedded_qubo = Array2::zeros((king_size, king_size));

        // Copy original QUBO values
        for i in 0..qubo.shape()[0] {
            for j in 0..qubo.shape()[1] {
                if let (Some(phys_i), Some(phys_j)) = (
                    embedding.logical_to_physical.get(&i),
                    embedding.logical_to_physical.get(&j),
                ) {
                    // For simplicity, use first physical qubit in chain
                    embedded_qubo[[phys_i[0], phys_j[0]]] = qubo[[i, j]];
                }
            }
        }

        // Add chain couplings
        for (logical_idx, physical_chain) in &embedding.logical_to_physical {
            for i in 1..physical_chain.len() {
                let strength = embedding.chain_strengths[*logical_idx];
                embedded_qubo[[physical_chain[i - 1], physical_chain[i]]] = -strength;
                embedded_qubo[[physical_chain[i], physical_chain[i - 1]]] = -strength;
            }
        }

        // Submit multiple jobs for shots
        let mut all_results = Vec::new();
        let jobs_needed = shots.div_ceil(100); // Max 100 solutions per job

        for _ in 0..jobs_needed {
            let job_id = self.submit_job(&embedded_qubo)?;
            let cmos_results = self.get_job_results(&job_id)?;

            for cmos_result in cmos_results {
                let sample = self.unembed_solution(&cmos_result, &embedding, var_map);
                all_results.push(sample);
            }
        }

        // Sort by energy and limit to requested shots
        all_results.sort_by(|a, b| {
            a.energy
                .partial_cmp(&b.energy)
                .unwrap_or(std::cmp::Ordering::Equal)
        });
        all_results.truncate(shots);

        Ok(all_results)
    }

    fn run_hobo(
        &self,
        _hobo: &(scirs2_core::ndarray::ArrayD<f64>, HashMap<String, usize>),
        _shots: usize,
    ) -> SamplerResult<Vec<SampleResult>> {
        Err(SamplerError::NotImplemented(
            "HOBO not supported by Hitachi hardware".to_string(),
        ))
    }
}

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

    #[test]
    fn test_hitachi_config() {
        let mut config = HitachiConfig::default();
        assert_eq!(config.annealing_params.num_steps, 100_000);

        match config.hardware_version {
            HardwareVersion::Gen4 { king_graph_size } => {
                assert_eq!(king_graph_size, 512);
            }
            _ => panic!("Wrong hardware version"),
        }
    }

    #[test]
    fn test_embedding_cache() {
        let sampler = HitachiCMOSSampler::new(HitachiConfig::default());
        let qubo = Array2::eye(4);

        let embedding1 = sampler
            .find_embedding(&qubo)
            .expect("Failed to find embedding for first call");
        let embedding2 = sampler
            .find_embedding(&qubo)
            .expect("Failed to find embedding for second call");

        // Should use cached embedding
        assert_eq!(embedding1.quality_score, embedding2.quality_score);
    }
}