# antinature/qiskit_integration/ansatze.py

import math
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np

# Import Qiskit with error handling
try:
    from qiskit import QuantumCircuit
    from qiskit.circuit import Parameter
    from qiskit.circuit.library import EfficientSU2
    from qiskit.quantum_info import Statevector

    HAS_QISKIT = True
except ImportError:
    # Create dummy classes for type hints
    class QuantumCircuit:
        def __init__(self, *args, **kwargs):
            raise ImportError("QuantumCircuit not available. Install qiskit.")

    class Parameter:
        def __init__(self, *args, **kwargs):
            raise ImportError("Parameter not available. Install qiskit.")

    class EfficientSU2:
        def __init__(self, *args, **kwargs):
            raise ImportError("EfficientSU2 not available. Install qiskit.")

    class Statevector:
        def __init__(self, *args, **kwargs):
            raise ImportError("Statevector not available. Install qiskit.")

    HAS_QISKIT = False

# Indicate class availability
HAS_ANSATZ = HAS_QISKIT


class AntinatureAnsatz:
    """
    Collection of specialized ansatz circuits for antimatter systems.

    This class provides a library of quantum circuit ansätze specifically designed
    for simulating various antinature systems including positronium, anti-hydrogen,
    and more complex systems like positronium molecules.
    """
    
    def __init__(self, num_qubits=None, reps=2, entanglement='full', system_type=None):
        """
        Initialize AntinatureAnsatz with parameters.
        
        Parameters:
        -----------
        num_qubits : int, optional
            Number of qubits for the ansatz
        reps : int
            Number of repetition layers
        entanglement : str
            Entanglement pattern
        system_type : str, optional
            Type of antinature system ('positronium', 'anti_hydrogen', etc.)
        """
        self.num_qubits = num_qubits
        self.reps = reps
        self.entanglement = entanglement
        self.system_type = system_type
    
    def create_positronium_ansatz(self, include_entanglement=True):
        """
        Create positronium ansatz for backward compatibility.
        
        Parameters:
        -----------
        include_entanglement : bool
            Whether to include entanglement gates
            
        Returns:
        --------
        QuantumCircuit
            Positronium ansatz circuit
        """
        entanglement_type = 'full' if include_entanglement else 'linear'
        return self.positronium_ansatz(reps=self.reps, entanglement=entanglement_type)

    @staticmethod
    def positronium_ansatz(reps: int = 3, entanglement: str = 'full') -> QuantumCircuit:
        """
        Creates a specialized ansatz for positronium with 2 qubits.

        Positronium is an electron-positron bound system that can be
        represented with 2 qubits (one for each particle). The ansatz
        uses parameterized rotations and entanglement to represent the
        electron-positron correlation.

        Parameters:
        -----------
        reps : int
            Number of repetition layers
        entanglement : str
            Entanglement strategy ('full', 'linear', or 'circular')

        Returns:
        --------
        QuantumCircuit
            Specialized quantum circuit for positronium

        Notes:
        ------
        This ansatz is specifically optimized to represent the electron-positron
        correlation in positronium ground and excited states.
        """
        if not HAS_QISKIT:
            raise ImportError("Qiskit is required for this functionality")

        # Create a 2-qubit circuit (one for electron, one for positron)
        circuit = QuantumCircuit(2)

        # Initialize in superposition state
        circuit.h(0)  # Electron in superposition
        circuit.h(1)  # Positron in superposition

        # For tracking parameters
        params = []

        for r in range(reps):
            # Parameterized rotations for electron
            param_rx_e = Parameter(f'rx_e_{r}')
            param_ry_e = Parameter(f'ry_e_{r}')
            param_rz_e = Parameter(f'rz_e_{r}')
            params.extend([param_rx_e, param_ry_e, param_rz_e])

            circuit.rx(param_rx_e, 0)
            circuit.ry(param_ry_e, 0)
            circuit.rz(param_rz_e, 0)

            # Parameterized rotations for positron
            param_rx_p = Parameter(f'rx_p_{r}')
            param_ry_p = Parameter(f'ry_p_{r}')
            param_rz_p = Parameter(f'rz_p_{r}')
            params.extend([param_rx_p, param_ry_p, param_rz_p])

            circuit.rx(param_rx_p, 1)
            circuit.ry(param_ry_p, 1)
            circuit.rz(param_rz_p, 1)

            # Entanglement layer - crucial for electron-positron correlation
            if entanglement == 'full':
                # Full entanglement (more expressive)
                circuit.cx(0, 1)

                # Parameterized ZZ interaction
                param_zz = Parameter(f'zz_{r}')
                params.append(param_zz)

                # Implement parameterized ZZ interaction
                circuit.rz(param_zz, 1)
                circuit.cx(0, 1)

                # Add reverse entanglement for symmetry
                circuit.cx(1, 0)

                # Parameterized ZZ interaction in other direction
                param_zz_rev = Parameter(f'zz_rev_{r}')
                params.append(param_zz_rev)

                circuit.rz(param_zz_rev, 0)
                circuit.cx(1, 0)

            elif entanglement == 'circular':
                # Circular entanglement (good for periodic boundary conditions)
                circuit.cx(0, 1)
                circuit.cx(1, 0)

                # Parameterized phase for binding
                param_phase = Parameter(f'phase_{r}')
                params.append(param_phase)

                circuit.rz(param_phase, 0)

            else:  # 'linear' is the default
                # Simple linear entanglement
                circuit.cx(0, 1)

                # Parameterized ZZ interaction
                param_zz = Parameter(f'zz_{r}')
                params.append(param_zz)

                circuit.rz(param_zz, 1)
                circuit.cx(0, 1)

            # Add additional phase for positronium binding
            if r < reps - 1:
                param_phase_e = Parameter(f'phase_e_{r}')
                param_phase_p = Parameter(f'phase_p_{r}')
                params.extend([param_phase_e, param_phase_p])

                circuit.rz(param_phase_e, 0)
                circuit.rz(param_phase_p, 1)

        # Final rotations for measurement flexibility
        param_final_rx_e = Parameter(f'final_rx_e')
        param_final_rx_p = Parameter(f'final_rx_p')
        params.extend([param_final_rx_e, param_final_rx_p])

        circuit.rx(param_final_rx_e, 0)
        circuit.rx(param_final_rx_p, 1)

        return circuit

    @staticmethod
    def anti_hydrogen_ansatz(
        n_orbitals: int = 1, reps: int = 3, electronic_reps: int = 2
    ) -> QuantumCircuit:
        """
        Creates a specialized ansatz for anti-hydrogen with 3 qubits.

        Anti-hydrogen consists of an antiproton and a positron.
        The ansatz uses 3 qubits: 1 for the positron's position
        and 2 for spatial orbitals. The circuit implements the necessary
        correlations for binding.

        Parameters:
        -----------
        n_orbitals : int
            Number of orbitals (typically 1-3 for various states)
        reps : int
            Number of repetition layers
        electronic_reps : int
            Number of electronic structure repetitions

        Returns:
        --------
        QuantumCircuit
            Specialized quantum circuit for anti-hydrogen

        Notes:
        ------
        This ansatz is designed to capture the positron-antiproton interaction in
        anti-hydrogen atoms, with emphasis on the bound state properties.
        """
        if not HAS_QISKIT:
            raise ImportError("Qiskit is required for this functionality")

        # For anti-hydrogen, we use 3 qubits:
        # qubit 0: represents the positron's position
        # qubit 1: represents the positron's spin
        # qubit 2: represents the antiproton's relative position
        circuit = QuantumCircuit(3)

        # Initialize with the positron in a superposition state
        circuit.h(0)

        # For a more realistic state, prepare specific initial state
        circuit.h(1)
        circuit.h(2)

        # Additional phase adjustment for initial state
        circuit.s(0)  # Phase gate (S = sqrt(Z))

        params = []
        for r in range(reps):
            # Parameterized rotations for each qubit
            for i in range(3):
                param_rx = Parameter(f'rx_{i}_{r}')
                param_ry = Parameter(f'ry_{i}_{r}')
                param_rz = Parameter(f'rz_{i}_{r}')
                params.extend([param_rx, param_ry, param_rz])

                circuit.rx(param_rx, i)
                circuit.ry(param_ry, i)
                circuit.rz(param_rz, i)

            # Entanglement layer - positron-nucleus correlation
            # First: position-based entanglement
            circuit.cx(0, 2)  # Connect positron position to antiproton

            # Parameterized ZZ interaction for binding
            param_zz1 = Parameter(f'zz1_{r}')
            params.append(param_zz1)

            circuit.rz(param_zz1, 2)
            circuit.cx(0, 2)

            # Second: spin-based entanglement
            circuit.cx(1, 2)  # Connect positron spin to antiproton

            param_zz2 = Parameter(f'zz2_{r}')
            params.append(param_zz2)

            circuit.rz(param_zz2, 2)
            circuit.cx(1, 2)

            # Add electronic structure repetitions for more accurate representation
            for e in range(electronic_reps):
                # Connect all qubits in circular pattern
                circuit.cx(0, 1)
                circuit.cx(1, 2)
                circuit.cx(2, 0)

                # Parameterized interaction
                param_el = Parameter(f'el_{r}_{e}')
                params.append(param_el)

                circuit.rz(param_el, 0)

                # Reverse the connections to ensure unitary evolution
                circuit.cx(2, 0)
                circuit.cx(1, 2)
                circuit.cx(0, 1)

            # Add barrier for clarity in circuit diagram
            circuit.barrier()

        return circuit

    @staticmethod
    def positronium_molecule_ansatz(
        reps: int = 3, use_advanced: bool = True
    ) -> QuantumCircuit:
        """
        Creates a specialized ansatz for positronium molecule with 4 qubits.

        Positronium molecule (Ps2) consists of two positronium atoms bound together.
        The ansatz uses 4 qubits: 2 for electrons and 2 for positrons.
        The circuit implements both intra-atom and inter-atom correlations.

        Parameters:
        -----------
        reps : int
            Number of repetition layers
        use_advanced : bool
            Whether to use advanced entanglement patterns (more gates but higher expressivity)

        Returns:
        --------
        QuantumCircuit
            Specialized quantum circuit for positronium molecule

        Notes:
        ------
        This ansatz is designed to represent the complex interactions between
        electrons and positrons in a positronium molecule, including both the
        intra-positronium correlations and the binding between positronium atoms.
        """
        if not HAS_QISKIT:
            raise ImportError("Qiskit is required for this functionality")

        # For positronium molecule, we use 4 qubits:
        # qubits 0,1: represent the electrons
        # qubits 2,3: represent the positrons
        circuit = QuantumCircuit(4)

        # Initialize in superposition for all particles
        for i in range(4):
            circuit.h(i)

        params = []
        for r in range(reps):
            # Parameterized rotations for each particle
            for i in range(4):
                param_rx = Parameter(f'rx_{i}_{r}')
                param_ry = Parameter(f'ry_{i}_{r}')
                param_rz = Parameter(f'rz_{i}_{r}')
                params.extend([param_rx, param_ry, param_rz])

                circuit.rx(param_rx, i)
                circuit.ry(param_ry, i)
                circuit.rz(param_rz, i)

            # Intra-atom correlations (electron-positron binding)
            # First positronium atom (electron 0, positron 2)
            circuit.cx(0, 2)
            param_zz1 = Parameter(f'zz1_{r}')
            params.append(param_zz1)
            circuit.rz(param_zz1, 2)
            circuit.cx(0, 2)

            # Second positronium atom (electron 1, positron 3)
            circuit.cx(1, 3)
            param_zz2 = Parameter(f'zz2_{r}')
            params.append(param_zz2)
            circuit.rz(param_zz2, 3)
            circuit.cx(1, 3)

            # Inter-atom correlations (binding between atoms)
            # Electron-electron correlation
            circuit.cx(0, 1)
            param_ee = Parameter(f'ee_{r}')
            params.append(param_ee)
            circuit.rz(param_ee, 1)
            circuit.cx(0, 1)

            # Positron-positron correlation
            circuit.cx(2, 3)
            param_pp = Parameter(f'pp_{r}')
            params.append(param_pp)
            circuit.rz(param_pp, 3)
            circuit.cx(2, 3)

            # Cross-correlations (electron from one atom with positron from other)
            circuit.cx(0, 3)
            param_cross1 = Parameter(f'cross1_{r}')
            params.append(param_cross1)
            circuit.rz(param_cross1, 3)
            circuit.cx(0, 3)

            circuit.cx(1, 2)
            param_cross2 = Parameter(f'cross2_{r}')
            params.append(param_cross2)
            circuit.rz(param_cross2, 2)
            circuit.cx(1, 2)

            # Advanced entanglement pattern for better accuracy
            if use_advanced and r < reps - 1:
                # Create a fully entangled state (all-to-all connections)
                # This improves the ansatz's expressivity but increases circuit depth
                for i in range(4):
                    for j in range(i + 1, 4):
                        if (i, j) not in [
                            (0, 2),
                            (1, 3),
                            (0, 1),
                            (2, 3),
                            (0, 3),
                            (1, 2),
                        ]:
                            # Skip pairs already connected above
                            circuit.cx(i, j)
                            param_adv = Parameter(f'adv_{i}_{j}_{r}')
                            params.append(param_adv)
                            circuit.rz(param_adv, j)
                            circuit.cx(i, j)

                # Add barrier for clarity
                circuit.barrier()

            # Add mixer for better exploration if not the final repetition
            if r < reps - 1:
                for i in range(4):
                    circuit.h(i)

        return circuit

    @staticmethod
    def anti_helium_ansatz(reps: int = 2, advanced: bool = False) -> QuantumCircuit:
        """
        Creates a specialized ansatz for anti-helium with 6 qubits.

        Anti-helium consists of an anti-nucleus (with 2 antiprotons) and 2 positrons.
        The ansatz uses 6 qubits to represent the various degrees of freedom.

        Parameters:
        -----------
        reps : int
            Number of repetition layers
        advanced : bool
            Whether to use advanced entanglement patterns for higher expressivity

        Returns:
        --------
        QuantumCircuit
            Specialized quantum circuit for anti-helium

        Notes:
        ------
        This ansatz is designed to capture the interactions between positrons and
        the anti-nucleus in anti-helium, representing both orbital and spin degrees
        of freedom.
        """
        if not HAS_QISKIT:
            raise ImportError("Qiskit is required for this functionality")

        # For anti-helium, we use 6 qubits:
        # qubits 0,1: represent the first positron (position and spin)
        # qubits 2,3: represent the second positron (position and spin)
        # qubits 4,5: represent the anti-nucleus orbital structure
        circuit = QuantumCircuit(6)

        # Initialize in superposition
        for i in range(6):
            circuit.h(i)

        params = []
        for r in range(reps):
            # Parameterized rotations for each qubit
            for i in range(6):
                param_rx = Parameter(f'rx_{i}_{r}')
                param_ry = Parameter(f'ry_{i}_{r}')
                param_rz = Parameter(f'rz_{i}_{r}')
                params.extend([param_rx, param_ry, param_rz])

                circuit.rx(param_rx, i)
                circuit.ry(param_ry, i)
                circuit.rz(param_rz, i)

            # Positron 1 - Anti-nucleus correlation
            for i in range(2):
                for j in range(4, 6):
                    circuit.cx(i, j)
                    param = Parameter(f'p1n_{i}_{j}_{r}')
                    params.append(param)
                    circuit.rz(param, j)
                    circuit.cx(i, j)

            # Positron 2 - Anti-nucleus correlation
            for i in range(2, 4):
                for j in range(4, 6):
                    circuit.cx(i, j)
                    param = Parameter(f'p2n_{i}_{j}_{r}')
                    params.append(param)
                    circuit.rz(param, j)
                    circuit.cx(i, j)

            # Positron-positron correlation
            for i in range(2):
                for j in range(2, 4):
                    circuit.cx(i, j)
                    param = Parameter(f'pp_{i}_{j}_{r}')
                    params.append(param)
                    circuit.rz(param, j)
                    circuit.cx(i, j)

            # Advanced entanglement pattern
            if advanced:
                # Add extra entanglement within each positron's qubits
                circuit.cx(0, 1)
                circuit.cx(2, 3)

                # Add entanglement between anti-nucleus qubits
                circuit.cx(4, 5)

                # Add cyclic connections to capture higher-order correlations
                circuit.cx(1, 2)
                circuit.cx(3, 4)
                circuit.cx(5, 0)

                # Add parameterized rotations after this complex entanglement
                for i in range(6):
                    param_adv = Parameter(f'adv_{i}_{r}')
                    params.append(param_adv)
                    circuit.rz(param_adv, i)

            # Add barrier for clarity
            circuit.barrier()

        return circuit

    @staticmethod
    def create_hardware_efficient_ansatz(
        n_qubits: int, reps: int = 2, entanglement: str = 'linear'
    ) -> QuantumCircuit:
        """
        Creates a hardware-efficient ansatz for antinature systems.

        Parameters:
        -----------
        n_qubits : int
            Number of qubits
        reps : int
            Number of repetition layers
        entanglement : str
            Entanglement pattern ('linear', 'full', 'circular')

        Returns:
        --------
        QuantumCircuit
            Hardware-efficient quantum circuit

        Notes:
        ------
        Uses the EfficientSU2 circuit from Qiskit's circuit library, which is
        designed to be efficient on real quantum hardware while maintaining
        good expressivity.
        """
        if not HAS_QISKIT:
            raise ImportError("Qiskit is required for this functionality")

        try:
            # Try to use the circuit library for efficiency
            circuit = EfficientSU2(
                num_qubits=n_qubits,
                entanglement=entanglement,
                reps=reps,
                skip_final_rotation_layer=False,
            )
            return circuit
        except Exception:
            # Fallback implementation if circuit library fails
            # Create a custom implementation
            circuit = QuantumCircuit(n_qubits)

            # Initialize with Hadamard on all qubits
            for i in range(n_qubits):
                circuit.h(i)

            # Create parameterized layers
            for r in range(reps):
                # Rotation layer - RX, RY, RZ on all qubits
                for i in range(n_qubits):
                    circuit.rx(Parameter(f'rx_{i}_{r}'), i)
                    circuit.ry(Parameter(f'ry_{i}_{r}'), i)
                    circuit.rz(Parameter(f'rz_{i}_{r}'), i)

                # Entanglement layer
                if entanglement == 'linear':
                    for i in range(n_qubits - 1):
                        circuit.cx(i, i + 1)

                elif entanglement == 'circular':
                    for i in range(n_qubits):
                        circuit.cx(i, (i + 1) % n_qubits)

                elif entanglement == 'full':
                    for i in range(n_qubits):
                        for j in range(i + 1, n_qubits):
                            circuit.cx(i, j)

                else:
                    raise ValueError(f"Unknown entanglement pattern: {entanglement}")

            # Final rotation layer
            for i in range(n_qubits):
                circuit.rx(Parameter(f'rx_final_{i}'), i)
                circuit.ry(Parameter(f'ry_final_{i}'), i)
                circuit.rz(Parameter(f'rz_final_{i}'), i)

            return circuit

    @staticmethod
    def two_particle_ansatz(
        particle_type: str = 'both', reps: int = 2
    ) -> QuantumCircuit:
        """
        Creates an ansatz specifically for two-particle systems.

        Parameters:
        -----------
        particle_type : str
            Type of particles ('electron', 'positron', or 'both')
        reps : int
            Number of repetition layers

        Returns:
        --------
        QuantumCircuit
            Quantum circuit for two-particle system

        Notes:
        ------
        This ansatz is designed for simulating systems with two identical particles
        (two electrons or two positrons) or electron-positron pairs, accounting for
        symmetry requirements.
        """
        if not HAS_QISKIT:
            raise ImportError("Qiskit is required for this functionality")

        # Two-qubit circuit
        circuit = QuantumCircuit(2)

        # Initial state preparation
        if particle_type == 'electron':
            # Antisymmetric state for electrons (fermions)
            # Bell state |01⟩ - |10⟩ is antisymmetric
            circuit.h(0)
            circuit.x(1)
            circuit.cx(0, 1)
            circuit.z(1)  # Phase flip to get - sign

        elif particle_type == 'positron':
            # Antisymmetric state for positrons (fermions)
            circuit.h(0)
            circuit.x(1)
            circuit.cx(0, 1)
            circuit.z(1)

        elif particle_type == 'both':
            # For electron-positron pair, no symmetry requirement
            circuit.h(0)
            circuit.h(1)
            circuit.cx(0, 1)

        else:
            raise ValueError(f"Unknown particle type: {particle_type}")

        # Add variational layers
        params = []
        for r in range(reps):
            for i in range(2):
                param_rx = Parameter(f'rx_{i}_{r}')
                param_ry = Parameter(f'ry_{i}_{r}')
                param_rz = Parameter(f'rz_{i}_{r}')
                params.extend([param_rx, param_ry, param_rz])

                circuit.rx(param_rx, i)
                circuit.ry(param_ry, i)
                circuit.rz(param_rz, i)

            # Add entanglement
            circuit.cx(0, 1)
            param_zz = Parameter(f'zz_{r}')
            params.append(param_zz)
            circuit.rz(param_zz, 1)
            circuit.cx(0, 1)

        return circuit

    @staticmethod
    def get_specialized_ansatz(system_name: str, **kwargs) -> QuantumCircuit:
        """
        Returns the appropriate specialized ansatz for a given system.

        Parameters:
        -----------
        system_name : str
            Name of the antinature system
        **kwargs : dict
            Additional parameters for the specific ansatz

        Returns:
        --------
        QuantumCircuit
            Specialized quantum circuit for the system

        Notes:
        ------
        This factory method provides a convenient way to get the right
        ansatz for different antinature systems without having to remember
        the specific method names.
        """
        if not HAS_QISKIT:
            raise ImportError("Qiskit is required for this functionality")

        # Extract common parameters
        reps = kwargs.get('reps', 2)

        if system_name.lower() == 'positronium':
            entanglement = kwargs.get('entanglement', 'full')
            return AntinatureAnsatz.positronium_ansatz(
                reps=reps, entanglement=entanglement
            )

        elif (
            system_name.lower() == 'anti_hydrogen'
            or system_name.lower() == 'antihydrogen'
        ):
            n_orbitals = kwargs.get('n_orbitals', 1)
            electronic_reps = kwargs.get('electronic_reps', 2)
            return AntinatureAnsatz.anti_hydrogen_ansatz(
                n_orbitals=n_orbitals, reps=reps, electronic_reps=electronic_reps
            )

        elif (
            system_name.lower() == 'positronium_molecule'
            or system_name.lower() == 'ps2'
        ):
            use_advanced = kwargs.get('use_advanced', True)
            return AntinatureAnsatz.positronium_molecule_ansatz(
                reps=reps, use_advanced=use_advanced
            )

        elif (
            system_name.lower() == 'anti_helium' or system_name.lower() == 'antihelium'
        ):
            advanced = kwargs.get('advanced', False)
            return AntinatureAnsatz.anti_helium_ansatz(reps=reps, advanced=advanced)

        elif system_name.lower() == 'two_particle':
            particle_type = kwargs.get('particle_type', 'both')
            return AntinatureAnsatz.two_particle_ansatz(
                particle_type=particle_type, reps=reps
            )

        else:
            # Default to hardware efficient ansatz
            n_qubits = kwargs.get('n_qubits', 4)
            entanglement = kwargs.get('entanglement', 'linear')
            return AntinatureAnsatz.create_hardware_efficient_ansatz(
                n_qubits=n_qubits, reps=reps, entanglement=entanglement
            )


# Standalone function for backward compatibility
def create_positronium_circuit(reps: int = 2, include_entanglement: bool = True):
    """
    Create a parameterized quantum circuit for positronium VQE.
    
    This is a standalone function for backward compatibility with older code.
    
    Parameters:
    -----------
    reps : int
        Number of repetition layers in the ansatz
    include_entanglement : bool
        Whether to include entanglement gates between electron and positron qubits
        
    Returns:
    --------
    QuantumCircuit
        Parameterized quantum circuit for VQE
    """
    if not HAS_QISKIT:
        raise ImportError("Qiskit is required for this functionality")
    
    # Use the AntinatureAnsatz class
    ansatz = AntinatureAnsatz(num_qubits=2, reps=reps)
    return ansatz.create_positronium_ansatz(include_entanglement=include_entanglement)
