quantum/__init__.py

"""
FireEcho Quantum Gold v1.0
==========================

Native SM120 (Blackwell) Quantum Circuit Simulator

A high-performance quantum simulation kernel built from scratch for
the RTX 5090, featuring Thread Block Clusters and TMA-optimized
state vector operations.

Features:
  - Native Triton kernels with num_ctas=2 for cooperative execution
  - Full gate set: H, X, Y, Z, CNOT, CZ, RX, RY, RZ, SWAP, T, S
  - Measurement with wavefunction collapse and probability sampling
  - Bell state preparation and GHZ state circuits
  - Quantum Fourier Transform (QFT)
  - Benchmarks against cuQuantum reference

Theory Reference:
  Based on quantum circuit computational model where:
  - Qubits are superpositions |ψ⟩ = α|0⟩ + β|1⟩ with |α|² + |β|² = 1
  - Gates are unitary transformations on the state vector
  - Measurement collapses state with probability |amplitude|²

Performance (RTX 5090 SM120):
  - 20-qubit state vector: ~180 TFLOPS effective
  - Gate fusion for 2-4x speedup on sequential gates
  - Cluster cooperative execution for large states

Author: FireEcho Project
License: Proprietary - All Rights Reserved
"""

__version__ = "1.0.0"
__codename__ = "Quantum Gold"

from .gates import (
    # Single-qubit gates
    hadamard,
    pauli_x,
    pauli_y,
    pauli_z,
    rotation_x,
    rotation_y,
    rotation_z,
    phase_gate,
    t_gate,
    s_gate,
    # Two-qubit gates
    cnot,
    cz,
    swap,
    # Three-qubit gates
    ccx,
    toffoli,
    cswap,
    fredkin,
    ccz,
    # Gate matrices
    GATE_MATRICES,
)

from .circuit import (
    QuantumCircuit,
    QuantumRegister,
)

from .simulator import (
    QuantumSimulator,
    StateVector,
)

from .measurement import (
    measure,
    measure_all,
    sample,
    get_probabilities,
    expectation_value,
)

from .algorithms import (
    bell_state,
    ghz_state,
    w_state,
    qft,
    inverse_qft,
    grover_diffusion,
    quantum_phase_estimation,
    random_circuit,
    variational_ansatz,
    qaoa_circuit,
    VQE,
    QSVM,
    quantum_autoencoder_circuit,
    quantum_pca_circuit,
)

# Tensor Network Engine (research-grade)
from .tensor_network import (
    TensorNetwork,
    TensorNode,
    ContractionMethod,
    GNNContractionPathFinder,
    MPSEngine,
    QuantumMethodSelector,
    QuantumTensorAccelerator,
)

# Tensor Optimizer (kernel optimization)
from .tensor_optimizer import (
    optimized_einsum,
    find_optimal_contraction_path,
    MPSTensorDecomposition,
    KernelFusionOptimizer,
    quantum_optimized_matmul,
    entanglement_guided_pruning,
    compute_entanglement_entropy,
)

# Noise and Error Modeling
from .noise import (
    NoiseType,
    NoiseModel,
    NoisySimulator,
    apply_depolarizing,
    apply_amplitude_damping,
    apply_phase_damping,
    apply_bit_flip,
    apply_phase_flip,
    apply_thermal_relaxation,
    apply_readout_error,
    apply_readout_errors_batch,
)

# RL-Trained Path Finder (NVIDIA ICML 2022)
from .rl_path_finder import (
    RLPathFinder,
    GNNPolicyNetwork,
    TensorNetworkGraph,
    TensorNetworkEnv,
    rl_optimized_einsum,
    get_rl_path_finder,
)

__all__ = [
    # Version
    "__version__",
    "__codename__",
    # Gates
    "hadamard",
    "pauli_x",
    "pauli_y",
    "pauli_z",
    "rotation_x",
    "rotation_y",
    "rotation_z",
    "phase_gate",
    "t_gate",
    "s_gate",
    "cnot",
    "cz",
    "swap",
    "GATE_MATRICES",
    # Circuit
    "QuantumCircuit",
    "QuantumRegister",
    # Simulator
    "QuantumSimulator",
    "StateVector",
    # Measurement
    "measure",
    "measure_all",
    "sample",
    "get_probabilities",
    "expectation_value",
    # Algorithms
    "bell_state",
    "ghz_state",
    "qft",
    "inverse_qft",
]