quantrs2_circuit/

tensor_network.rs

//! Tensor network compression for quantum circuits
//!
//! This module provides tensor network representations of quantum circuits
//! for efficient simulation and optimization.

use crate::builder::Circuit;
use crate::dag::{circuit_to_dag, CircuitDag, DagNode};
use nalgebra::{Complex, DMatrix};
use quantrs2_core::{
    error::{QuantRS2Error, QuantRS2Result},
    gate::GateOp,
    qubit::QubitId,
};
use std::collections::{HashMap, HashSet};
use std::f64::consts::PI;

/// Complex number type
type C64 = Complex<f64>;

/// Tensor representing a quantum gate or state
#[derive(Debug, Clone)]
pub struct Tensor {
    /// Tensor data in row-major order
    pub data: Vec<C64>,
    /// Shape of the tensor (dimensions)
    pub shape: Vec<usize>,
    /// Labels for each index
    pub indices: Vec<String>,
}

impl Tensor {
    /// Create a new tensor
    pub fn new(data: Vec<C64>, shape: Vec<usize>, indices: Vec<String>) -> Self {
        assert_eq!(shape.len(), indices.len());
        let total_size: usize = shape.iter().product();
        assert_eq!(data.len(), total_size);

        Self {
            data,
            shape,
            indices,
        }
    }

    /// Create an identity tensor
    pub fn identity(dim: usize, in_label: String, out_label: String) -> Self {
        let mut data = vec![C64::new(0.0, 0.0); dim * dim];
        for i in 0..dim {
            data[i * dim + i] = C64::new(1.0, 0.0);
        }

        Self::new(data, vec![dim, dim], vec![in_label, out_label])
    }

    /// Get the rank (number of indices)
    pub fn rank(&self) -> usize {
        self.shape.len()
    }

    /// Get the total number of elements
    pub fn size(&self) -> usize {
        self.data.len()
    }

    /// Contract two tensors along specified indices
    pub fn contract(
        &self,
        other: &Tensor,
        self_idx: &str,
        other_idx: &str,
    ) -> QuantRS2Result<Tensor> {
        // Find index positions
        let self_pos = self
            .indices
            .iter()
            .position(|s| s == self_idx)
            .ok_or_else(|| QuantRS2Error::InvalidInput(format!("Index {} not found", self_idx)))?;
        let other_pos = other
            .indices
            .iter()
            .position(|s| s == other_idx)
            .ok_or_else(|| QuantRS2Error::InvalidInput(format!("Index {} not found", other_idx)))?;

        // Check dimensions match
        if self.shape[self_pos] != other.shape[other_pos] {
            return Err(QuantRS2Error::InvalidInput(format!(
                "Dimension mismatch: {} vs {}",
                self.shape[self_pos], other.shape[other_pos]
            )));
        }

        // Compute new shape and indices
        let mut new_shape = Vec::new();
        let mut new_indices = Vec::new();

        for (i, (dim, idx)) in self.shape.iter().zip(&self.indices).enumerate() {
            if i != self_pos {
                new_shape.push(*dim);
                new_indices.push(idx.clone());
            }
        }

        for (i, (dim, idx)) in other.shape.iter().zip(&other.indices).enumerate() {
            if i != other_pos {
                new_shape.push(*dim);
                new_indices.push(idx.clone());
            }
        }

        // Perform contraction (simplified implementation)
        let new_size: usize = new_shape.iter().product();
        let mut new_data = vec![C64::new(0.0, 0.0); new_size];

        // This is a simplified contraction - in practice, would use optimized tensor libraries
        let contract_dim = self.shape[self_pos];

        // For now, return a placeholder
        Ok(Tensor::new(new_data, new_shape, new_indices))
    }

    /// Reshape the tensor
    pub fn reshape(&mut self, new_shape: Vec<usize>) -> QuantRS2Result<()> {
        let new_size: usize = new_shape.iter().product();
        if new_size != self.size() {
            return Err(QuantRS2Error::InvalidInput(format!(
                "Cannot reshape {} elements to shape {:?}",
                self.size(),
                new_shape
            )));
        }

        self.shape = new_shape;
        Ok(())
    }
}

/// Tensor network representation of a quantum circuit
#[derive(Debug)]
pub struct TensorNetwork {
    /// Tensors in the network
    tensors: Vec<Tensor>,
    /// Connections between tensors (tensor_idx1, idx1, tensor_idx2, idx2)
    bonds: Vec<(usize, String, usize, String)>,
    /// Open indices (external legs)
    open_indices: HashMap<String, (usize, usize)>, // index -> (tensor_idx, position)
}

impl TensorNetwork {
    /// Create a new empty tensor network
    pub fn new() -> Self {
        Self {
            tensors: Vec::new(),
            bonds: Vec::new(),
            open_indices: HashMap::new(),
        }
    }

    /// Add a tensor to the network
    pub fn add_tensor(&mut self, tensor: Tensor) -> usize {
        let idx = self.tensors.len();

        // Track open indices
        for (pos, index) in tensor.indices.iter().enumerate() {
            self.open_indices.insert(index.clone(), (idx, pos));
        }

        self.tensors.push(tensor);
        idx
    }

    /// Connect two tensor indices
    pub fn add_bond(
        &mut self,
        t1: usize,
        idx1: String,
        t2: usize,
        idx2: String,
    ) -> QuantRS2Result<()> {
        if t1 >= self.tensors.len() || t2 >= self.tensors.len() {
            return Err(QuantRS2Error::InvalidInput(
                "Tensor index out of range".to_string(),
            ));
        }

        // Remove from open indices
        self.open_indices.remove(&idx1);
        self.open_indices.remove(&idx2);

        self.bonds.push((t1, idx1, t2, idx2));
        Ok(())
    }

    /// Contract the entire network to a single tensor
    pub fn contract_all(&self) -> QuantRS2Result<Tensor> {
        if self.tensors.is_empty() {
            return Err(QuantRS2Error::InvalidInput(
                "Empty tensor network".to_string(),
            ));
        }

        // Simple contraction order: left to right
        // In practice, would use optimal contraction ordering
        let mut result = self.tensors[0].clone();

        for bond in &self.bonds {
            let (t1, idx1, t2, idx2) = bond;
            if *t1 == 0 {
                result = result.contract(&self.tensors[*t2], idx1, idx2)?;
            }
        }

        Ok(result)
    }

    /// Apply SVD-based compression
    pub fn compress(&mut self, max_bond_dim: usize, tolerance: f64) -> QuantRS2Result<()> {
        // Placeholder for SVD-based compression
        // Would implement MPS/MPO compression techniques
        Ok(())
    }
}

/// Convert a quantum circuit to tensor network representation
pub struct CircuitToTensorNetwork<const N: usize> {
    /// Maximum bond dimension for compression
    max_bond_dim: Option<usize>,
    /// Truncation tolerance
    tolerance: f64,
}

impl<const N: usize> CircuitToTensorNetwork<N> {
    /// Create a new converter
    pub fn new() -> Self {
        Self {
            max_bond_dim: None,
            tolerance: 1e-10,
        }
    }

    /// Set maximum bond dimension
    pub fn with_max_bond_dim(mut self, dim: usize) -> Self {
        self.max_bond_dim = Some(dim);
        self
    }

    /// Set truncation tolerance
    pub fn with_tolerance(mut self, tol: f64) -> Self {
        self.tolerance = tol;
        self
    }

    /// Convert circuit to tensor network
    pub fn convert(&self, circuit: &Circuit<N>) -> QuantRS2Result<TensorNetwork> {
        let mut tn = TensorNetwork::new();
        let mut qubit_wires: HashMap<usize, String> = HashMap::new();

        // Initialize qubit wires
        for i in 0..N {
            qubit_wires.insert(i, format!("q{}_in", i));
        }

        // Convert each gate to a tensor
        for (gate_idx, gate) in circuit.gates().iter().enumerate() {
            let tensor = self.gate_to_tensor(gate.as_ref(), gate_idx)?;
            let tensor_idx = tn.add_tensor(tensor);

            // Connect to previous wires
            for qubit in gate.qubits() {
                let q = qubit.id() as usize;
                let prev_wire = qubit_wires.get(&q).unwrap().clone();
                let new_wire = format!("q{}_g{}", q, gate_idx);

                // Add bond from previous wire to this gate
                if gate_idx > 0 || prev_wire.contains("_g") {
                    tn.add_bond(
                        tensor_idx - 1,
                        prev_wire.clone(),
                        tensor_idx,
                        format!("in_{}", q),
                    )?;
                }

                // Update wire for next connection
                qubit_wires.insert(q, new_wire);
            }
        }

        Ok(tn)
    }

    /// Convert a gate to tensor representation
    fn gate_to_tensor(&self, gate: &dyn GateOp, gate_idx: usize) -> QuantRS2Result<Tensor> {
        let qubits = gate.qubits();
        let n_qubits = qubits.len();

        match n_qubits {
            1 => {
                // Single-qubit gate
                let matrix = self.get_single_qubit_matrix(gate)?;
                let q = qubits[0].id() as usize;

                Ok(Tensor::new(
                    matrix.iter().cloned().collect(),
                    vec![2, 2],
                    vec![format!("in_{}", q), format!("out_{}", q)],
                ))
            }
            2 => {
                // Two-qubit gate
                let matrix = self.get_two_qubit_matrix(gate)?;
                let q0 = qubits[0].id() as usize;
                let q1 = qubits[1].id() as usize;

                Ok(Tensor::new(
                    matrix,
                    vec![2, 2, 2, 2],
                    vec![
                        format!("in_{}", q0),
                        format!("in_{}", q1),
                        format!("out_{}", q0),
                        format!("out_{}", q1),
                    ],
                ))
            }
            _ => Err(QuantRS2Error::UnsupportedOperation(format!(
                "{}-qubit gates not yet supported for tensor networks",
                n_qubits
            ))),
        }
    }

    /// Get matrix representation of single-qubit gate
    fn get_single_qubit_matrix(&self, gate: &dyn GateOp) -> QuantRS2Result<Vec<C64>> {
        // Simplified - would use actual gate matrices
        match gate.name() {
            "H" => Ok(vec![
                C64::new(1.0 / 2.0_f64.sqrt(), 0.0),
                C64::new(1.0 / 2.0_f64.sqrt(), 0.0),
                C64::new(1.0 / 2.0_f64.sqrt(), 0.0),
                C64::new(-1.0 / 2.0_f64.sqrt(), 0.0),
            ]),
            "X" => Ok(vec![
                C64::new(0.0, 0.0),
                C64::new(1.0, 0.0),
                C64::new(1.0, 0.0),
                C64::new(0.0, 0.0),
            ]),
            "Y" => Ok(vec![
                C64::new(0.0, 0.0),
                C64::new(0.0, -1.0),
                C64::new(0.0, 1.0),
                C64::new(0.0, 0.0),
            ]),
            "Z" => Ok(vec![
                C64::new(1.0, 0.0),
                C64::new(0.0, 0.0),
                C64::new(0.0, 0.0),
                C64::new(-1.0, 0.0),
            ]),
            _ => Ok(vec![
                C64::new(1.0, 0.0),
                C64::new(0.0, 0.0),
                C64::new(0.0, 0.0),
                C64::new(1.0, 0.0),
            ]),
        }
    }

    /// Get matrix representation of two-qubit gate
    fn get_two_qubit_matrix(&self, gate: &dyn GateOp) -> QuantRS2Result<Vec<C64>> {
        // Simplified - would use actual gate matrices
        match gate.name() {
            "CNOT" => {
                let mut matrix = vec![C64::new(0.0, 0.0); 16];
                matrix[0] = C64::new(1.0, 0.0); // |00⟩ -> |00⟩
                matrix[5] = C64::new(1.0, 0.0); // |01⟩ -> |01⟩
                matrix[15] = C64::new(1.0, 0.0); // |10⟩ -> |11⟩
                matrix[10] = C64::new(1.0, 0.0); // |11⟩ -> |10⟩
                Ok(matrix)
            }
            _ => {
                // Identity for unsupported gates
                let mut matrix = vec![C64::new(0.0, 0.0); 16];
                for i in 0..16 {
                    matrix[i * 16 + i] = C64::new(1.0, 0.0);
                }
                Ok(matrix)
            }
        }
    }
}

/// Matrix Product State representation of a circuit
#[derive(Debug)]
pub struct MatrixProductState {
    /// Site tensors
    tensors: Vec<Tensor>,
    /// Bond dimensions
    bond_dims: Vec<usize>,
    /// Number of qubits
    n_qubits: usize,
}

impl MatrixProductState {
    /// Create MPS from circuit
    pub fn from_circuit<const N: usize>(circuit: &Circuit<N>) -> QuantRS2Result<Self> {
        let converter = CircuitToTensorNetwork::<N>::new();
        let tn = converter.convert(circuit)?;

        // Convert tensor network to MPS form
        // This is a placeholder - would implement actual MPS conversion
        Ok(Self {
            tensors: Vec::new(),
            bond_dims: Vec::new(),
            n_qubits: N,
        })
    }

    /// Compress the MPS
    pub fn compress(&mut self, max_bond_dim: usize, tolerance: f64) -> QuantRS2Result<()> {
        // Implement SVD-based compression
        // Sweep through the MPS and truncate bonds
        Ok(())
    }

    /// Calculate overlap with another MPS
    pub fn overlap(&self, other: &MatrixProductState) -> QuantRS2Result<C64> {
        if self.n_qubits != other.n_qubits {
            return Err(QuantRS2Error::InvalidInput(
                "MPS have different number of qubits".to_string(),
            ));
        }

        // Calculate ⟨ψ|φ⟩
        Ok(C64::new(1.0, 0.0)) // Placeholder
    }

    /// Calculate expectation value of observable
    pub fn expectation_value(&self, observable: &TensorNetwork) -> QuantRS2Result<f64> {
        // Calculate ⟨ψ|O|ψ⟩
        Ok(0.0) // Placeholder
    }
}

/// Circuit compression using tensor networks
pub struct TensorNetworkCompressor {
    /// Maximum bond dimension
    max_bond_dim: usize,
    /// Truncation tolerance
    tolerance: f64,
    /// Compression method
    method: CompressionMethod,
}

#[derive(Debug, Clone)]
pub enum CompressionMethod {
    /// Singular Value Decomposition
    SVD,
    /// Density Matrix Renormalization Group
    DMRG,
    /// Time-Evolving Block Decimation
    TEBD,
}

impl TensorNetworkCompressor {
    /// Create a new compressor
    pub fn new(max_bond_dim: usize) -> Self {
        Self {
            max_bond_dim,
            tolerance: 1e-10,
            method: CompressionMethod::SVD,
        }
    }

    /// Set compression method
    pub fn with_method(mut self, method: CompressionMethod) -> Self {
        self.method = method;
        self
    }

    /// Compress a circuit
    pub fn compress<const N: usize>(
        &self,
        circuit: &Circuit<N>,
    ) -> QuantRS2Result<CompressedCircuit<N>> {
        let mps = MatrixProductState::from_circuit(circuit)?;

        Ok(CompressedCircuit {
            mps,
            original_gates: circuit.num_gates(),
            compression_ratio: 1.0, // Placeholder
        })
    }
}

/// Compressed circuit representation
#[derive(Debug)]
pub struct CompressedCircuit<const N: usize> {
    /// MPS representation
    mps: MatrixProductState,
    /// Original number of gates
    original_gates: usize,
    /// Compression ratio
    compression_ratio: f64,
}

impl<const N: usize> CompressedCircuit<N> {
    /// Get compression ratio
    pub fn compression_ratio(&self) -> f64 {
        self.compression_ratio
    }

    /// Decompress back to circuit
    pub fn decompress(&self) -> QuantRS2Result<Circuit<N>> {
        // Convert MPS back to circuit representation
        // This is non-trivial and would require gate synthesis
        Ok(Circuit::<N>::new())
    }

    /// Get fidelity with original circuit
    pub fn fidelity(&self, original: &Circuit<N>) -> QuantRS2Result<f64> {
        // Calculate |⟨ψ_compressed|ψ_original⟩|²
        Ok(0.99) // Placeholder
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use quantrs2_core::gate::single::Hadamard;

    #[test]
    fn test_tensor_creation() {
        let data = vec![
            C64::new(1.0, 0.0),
            C64::new(0.0, 0.0),
            C64::new(0.0, 0.0),
            C64::new(1.0, 0.0),
        ];
        let tensor = Tensor::new(data, vec![2, 2], vec!["in".to_string(), "out".to_string()]);

        assert_eq!(tensor.rank(), 2);
        assert_eq!(tensor.size(), 4);
    }

    #[test]
    fn test_tensor_network() {
        let mut tn = TensorNetwork::new();

        let t1 = Tensor::identity(2, "a".to_string(), "b".to_string());
        let t2 = Tensor::identity(2, "c".to_string(), "d".to_string());

        let idx1 = tn.add_tensor(t1);
        let idx2 = tn.add_tensor(t2);

        tn.add_bond(idx1, "b".to_string(), idx2, "c".to_string())
            .unwrap();

        assert_eq!(tn.tensors.len(), 2);
        assert_eq!(tn.bonds.len(), 1);
    }

    #[test]
    fn test_circuit_to_tensor_network() {
        let mut circuit = Circuit::<2>::new();
        circuit.add_gate(Hadamard { target: QubitId(0) }).unwrap();

        let converter = CircuitToTensorNetwork::<2>::new();
        let tn = converter.convert(&circuit).unwrap();

        assert!(tn.tensors.len() > 0);
    }

    #[test]
    fn test_compression() {
        let circuit = Circuit::<2>::new();
        let compressor = TensorNetworkCompressor::new(32);

        let compressed = compressor.compress(&circuit).unwrap();
        assert!(compressed.compression_ratio() <= 1.0);
    }
}