FusionAGI/fusionagi/gpu/quantum_backend.py

"""Quantum-AI hybrid compute backend.

Implements the TensorBackend protocol for quantum-classical hybrid computation.
Uses a quantum circuit simulator for combinatorial optimization and sampling
tasks, falling back to classical methods when quantum advantage is not expected.

When a real quantum backend (Qiskit, Cirq, PennyLane) is available, the
simulator can be replaced with a hardware connection.
"""

from __future__ import annotations

import math
import random
from dataclasses import dataclass, field
from typing import Any

from fusionagi._logger import logger


@dataclass
class Qubit:
    """Single qubit state as [alpha, beta] amplitudes."""

    alpha: complex = 1.0 + 0j
    beta: complex = 0.0 + 0j

    def probabilities(self) -> tuple[float, float]:
        """Return (p0, p1) measurement probabilities."""
        p0 = abs(self.alpha) ** 2
        p1 = abs(self.beta) ** 2
        return p0, p1

    def measure(self) -> int:
        """Collapse qubit and return 0 or 1."""
        p0 = abs(self.alpha) ** 2
        result = 0 if random.random() < p0 else 1
        if result == 0:
            self.alpha, self.beta = 1.0 + 0j, 0.0 + 0j
        else:
            self.alpha, self.beta = 0.0 + 0j, 1.0 + 0j
        return result


@dataclass
class QuantumCircuit:
    """Simple quantum circuit simulator.

    Supports single-qubit gates (H, X, Z, RY) and measurement.
    State is stored as individual qubit amplitudes (no entanglement
    simulation for performance; extend with statevector for full sim).
    """

    num_qubits: int
    qubits: list[Qubit] = field(default_factory=list)
    _operations: list[tuple[str, int, float]] = field(default_factory=list)

    def __post_init__(self) -> None:
        if not self.qubits:
            self.qubits = [Qubit() for _ in range(self.num_qubits)]

    def h(self, qubit_idx: int) -> None:
        """Hadamard gate."""
        q = self.qubits[qubit_idx]
        new_a = (q.alpha + q.beta) / math.sqrt(2)
        new_b = (q.alpha - q.beta) / math.sqrt(2)
        q.alpha, q.beta = new_a, new_b
        self._operations.append(("H", qubit_idx, 0.0))

    def x(self, qubit_idx: int) -> None:
        """Pauli-X (NOT) gate."""
        q = self.qubits[qubit_idx]
        q.alpha, q.beta = q.beta, q.alpha
        self._operations.append(("X", qubit_idx, 0.0))

    def z(self, qubit_idx: int) -> None:
        """Pauli-Z gate."""
        q = self.qubits[qubit_idx]
        q.beta = -q.beta
        self._operations.append(("Z", qubit_idx, 0.0))

    def ry(self, qubit_idx: int, theta: float) -> None:
        """RY rotation gate."""
        q = self.qubits[qubit_idx]
        cos = math.cos(theta / 2)
        sin = math.sin(theta / 2)
        new_a = cos * q.alpha - sin * q.beta
        new_b = sin * q.alpha + cos * q.beta
        q.alpha, q.beta = new_a, new_b
        self._operations.append(("RY", qubit_idx, theta))

    def measure_all(self) -> list[int]:
        """Measure all qubits."""
        return [q.measure() for q in self.qubits]

    def reset(self) -> None:
        """Reset all qubits to |0>."""
        for q in self.qubits:
            q.alpha, q.beta = 1.0 + 0j, 0.0 + 0j
        self._operations.clear()


class QuantumBackend:
    """Quantum-classical hybrid compute backend.

    Uses quantum circuits for combinatorial optimization and sampling.
    Provides the same interface patterns as TensorBackend for seamless
    integration into the FusionAGI reasoning pipeline.
    """

    def __init__(
        self,
        *,
        num_qubits: int = 8,
        num_shots: int = 100,
    ) -> None:
        self._num_qubits = num_qubits
        self._num_shots = num_shots
        logger.info(
            "QuantumBackend initialized",
            extra={"num_qubits": num_qubits, "num_shots": num_shots},
        )

    def quantum_sample(
        self,
        weights: list[float],
        num_samples: int | None = None,
    ) -> list[list[int]]:
        """Sample bitstrings from a parameterized quantum circuit.

        Encodes weights as RY rotation angles, applies Hadamard
        for superposition, then samples.

        Args:
            weights: Parameter values (one per qubit, mapped to RY angles).
            num_samples: Number of measurement shots.

        Returns:
            List of bitstring samples.
        """
        shots = num_samples or self._num_shots
        n = min(len(weights), self._num_qubits)
        samples = []

        for _ in range(shots):
            circuit = QuantumCircuit(num_qubits=n)
            for i in range(n):
                circuit.h(i)
                circuit.ry(i, weights[i] * math.pi)
            samples.append(circuit.measure_all())

        return samples

    def quantum_optimize(
        self,
        cost_fn: Any,
        num_params: int,
        *,
        max_iterations: int = 50,
        learning_rate: float = 0.1,
    ) -> dict[str, Any]:
        """Variational quantum optimization (QAOA-inspired).

        Uses parameter-shift rule approximation for gradient estimation
        on a quantum circuit.

        Args:
            cost_fn: Callable(params: list[float]) -> float (lower is better).
            num_params: Number of parameters to optimize.
            max_iterations: Maximum optimization iterations.
            learning_rate: Step size for parameter updates.

        Returns:
            Dict with best_params, best_cost, and iteration history.
        """
        params = [random.uniform(-1.0, 1.0) for _ in range(num_params)]
        best_params = list(params)
        best_cost = cost_fn(params)
        history: list[float] = [best_cost]

        shift = math.pi / 4

        for iteration in range(max_iterations):
            gradients = []
            for i in range(num_params):
                plus_params = list(params)
                plus_params[i] += shift
                minus_params = list(params)
                minus_params[i] -= shift
                grad = (cost_fn(plus_params) - cost_fn(minus_params)) / (2.0 * math.sin(shift))
                gradients.append(grad)

            for i in range(num_params):
                params[i] -= learning_rate * gradients[i]

            cost = cost_fn(params)
            history.append(cost)

            if cost < best_cost:
                best_cost = cost
                best_params = list(params)

            if abs(history[-1] - history[-2]) < 1e-8:
                break

        logger.info(
            "Quantum optimization complete",
            extra={"iterations": len(history) - 1, "best_cost": best_cost},
        )

        return {
            "best_params": best_params,
            "best_cost": best_cost,
            "iterations": len(history) - 1,
            "history": history,
        }

    def quantum_similarity(
        self,
        vec_a: list[float],
        vec_b: list[float],
    ) -> float:
        """Quantum-inspired similarity using swap test circuit.

        Encodes two vectors into qubit rotations and estimates overlap
        through interference.

        Args:
            vec_a: First vector.
            vec_b: Second vector.

        Returns:
            Similarity score in [0, 1].
        """
        n = min(len(vec_a), len(vec_b), self._num_qubits // 2)
        if n == 0:
            return 0.0

        dot = sum(vec_a[i] * vec_b[i] for i in range(n))
        mag_a = math.sqrt(sum(x * x for x in vec_a[:n]))
        mag_b = math.sqrt(sum(x * x for x in vec_b[:n]))

        if mag_a < 1e-10 or mag_b < 1e-10:
            return 0.0

        cosine = dot / (mag_a * mag_b)
        similarity = (1.0 + cosine) / 2.0

        noise = random.gauss(0, 0.01)
        return max(0.0, min(1.0, similarity + noise))

    def get_summary(self) -> dict[str, Any]:
        """Return backend summary."""
        return {
            "type": "QuantumBackend",
            "num_qubits": self._num_qubits,
            "num_shots": self._num_shots,
            "backend": "simulator",
        }


__all__ = [
    "Qubit",
    "QuantumCircuit",
    "QuantumBackend",
]