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HyperIntelligentFramework.py
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2454 lines (2064 loc) · 94.6 KB
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#!/usr/bin/env python3
"""
HyperIntelligentFramework - Unified Quantum-Language-Vision System
This framework integrates quantum computing, fractal mathematics, neural networks,
and large language models into a coherent hyper-intelligent system with
multi-modal reasoning capabilities.
April 2025 Update: Incorporating breakthroughs from Cavendish Lab (13,000-nuclei quantum
registers), Technion (nanoscale photon entanglement), Oxford's distributed quantum
algorithms (119.2× speedup), and Harvard/MIT's fault-tolerant compilation (48 logical
qubits). Implementing the Fractal-Harmonic Quantum Field Model (FH-QFM) for unified
quantum-relativistic processing with 12.3dB squeezing thresholds.
"""
import os
import sys
import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Dict, List, Tuple, Any, Optional, Union, Callable
from dataclasses import dataclass
import threading
import math
import json
from copy import deepcopy
from collections import deque
from torch.distributions import Normal, Categorical
# Import Qiskit for quantum circuit simulation
from qiskit import QuantumCircuit, execute, Aer, IBMQ
from qiskit.circuit import Parameter
from qiskit_aer import QasmSimulator
from qiskit.quantum_info import Statevector, state_fidelity
from qiskit.visualization import plot_state_city, plot_histogram
from qiskit.providers.aer import QasmSimulator
from qiskit.algorithms import Shor, AmplificationProblem, PhaseEstimation, HHL
from qiskit.utils import QuantumInstance
from qiskit.transpiler import PassManager
from qiskit.transpiler.passes import Unroller, Optimize1qGates, CXCancellation
# Import system components
from quantumentanglement import (
QuantumEntanglementSuperposition,
QuantumClassicalHybridNN,
)
from magic import QuantumFractalBridge, QuantumStateEntangler, CrossModalAttention
from MultifunctionalModule import MultimodalSystem
from QuantumOptimizer import QuantumOptimizer
from superintelligence import (
QuantumNonlinearNN,
QuantumAttention,
VortexProcessor,
ToroidalFieldGenerator,
GoldenRatioPhaseModulator,
ArchetypalResonator,
QuantumVortexIntegrationModel,
HyperDimensionalFractalNet
)
from src.core.visionary_minds import apply_visionary_thought, VisionaryMind, get_mind
from src.core.archetypes import Archetype, get_archetype
from src.core.vortex_math import ToroidalGenerator
# Configure logging
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
handlers=[logging.StreamHandler(sys.stdout)],
)
logger = logging.getLogger("HyperIntelligentFramework")
# Framework constants
FRAMEWORK_VERSION = "1.1.0"
DEFAULT_QUANTUM_QUBITS = 16
DEFAULT_EMBEDDING_DIM = 1024
DEFAULT_NUM_ATTENTION_HEADS = 16
DEFAULT_NUM_HIDDEN_LAYERS = 32
DEFAULT_BRIDGE_DIM = 512
# New quantum constants
QUANTUM_MEMORY_SIZE = 2048
QUANTUM_STATE_BUFFER_SIZE = 256
QUANTUM_COHERENCE_THRESHOLD = 0.85
QUANTUM_ENTANGLEMENT_STRENGTH = 0.7
# New integration constants
INTEGRATION_TEMPERATURE = 0.8
MODAL_FUSION_LAYERS = 4
QUANTUM_UPDATE_FREQUENCY = 5
# Enhanced memory constants
MEMORY_BUFFER_SIZE = 4096
MEMORY_UPDATE_RATE = 0.1
MEMORY_DECAY_FACTOR = 0.98
MEMORY_COHERENCE_THRESHOLD = 0.75
@dataclass
class HyperIntelligentConfig:
"""Configuration class for the HyperIntelligent Framework"""
seed: int = 42
device: str = "cuda" if torch.cuda.is_available() else "cpu"
precision: str = "float16"
num_qubits: int = DEFAULT_QUANTUM_QUBITS
quantum_circuit_depth: int = 3
quantum_error_correction: str = "surface_code"
llm_embedding_dim: int = DEFAULT_EMBEDDING_DIM
llm_hidden_size: int = 4096
llm_num_attention_heads: int = DEFAULT_NUM_ATTENTION_HEADS
llm_num_hidden_layers: int = DEFAULT_NUM_HIDDEN_LAYERS
llm_intermediate_size: int = 11008
llm_vocab_size: int = 32000
vision_embedding_dim: int = DEFAULT_EMBEDDING_DIM
vision_patch_size: int = 16
vision_image_size: int = 224
vision_num_attention_heads: int = 12
vision_num_hidden_layers: int = 12
fractal_dimension: float = 1.8
fractal_iterations: int = 4
fractal_hidden_dim: int = 256
integration_mode: str = "quantum_language_hybrid"
classical_weight: float = 0.3
quantum_weight: float = 0.4
fractal_weight: float = 0.1
language_weight: float = 0.2
memory_size: int = 1024
memory_dim: int = 512
use_persistent_memory: bool = True
enable_quantum_attention: bool = True
enable_fractal_embeddings: bool = True
enable_causal_inference: bool = True
enable_parallel_universes: bool = True
num_parallel_universes: int = 8
enable_visionary_computation: bool = True
default_visionary_mind: str = "einstein"
active_archetype: str = "krishna"
vortex_dimensions: int = 3
target_heart_coherence: float = 0.85
INTEGRATION_MODES = [
"classical_only",
"quantum_only",
"fractal_only",
"language_only",
"weighted",
"quantum_entangled",
"fractal_quantum",
"quantum_language_hybrid",
"full_integration",
"adaptive_hybrid",
]
class QuantumLanguageModel(nn.Module):
"""A large language model enhanced with quantum computing capabilities."""
def __init__(self, config: HyperIntelligentConfig):
super().__init__()
self.config = config
self.device = torch.device(config.device)
torch.manual_seed(config.seed)
np.random.seed(config.seed)
self.token_embeddings = nn.Embedding(
config.llm_vocab_size, config.llm_embedding_dim
)
self.position_embeddings = nn.Embedding(2048, config.llm_embedding_dim)
self.encoder_layers = nn.ModuleList(
[self._create_encoder_layer() for _ in range(config.llm_num_hidden_layers)]
)
self.quantum_entanglement = QuantumEntanglementSuperposition(config.num_qubits)
self.quantum_circuit_params = nn.Parameter(
torch.randn(config.quantum_circuit_depth, config.num_qubits, 3)
)
if config.enable_quantum_attention:
self.quantum_attention = CrossModalAttention(
dim=config.llm_embedding_dim,
num_heads=config.llm_num_attention_heads
)
self.quantum_language_bridge = nn.Linear(
config.num_qubits,
config.llm_embedding_dim
)
if config.enable_fractal_embeddings:
self.fractal_embedding_enhancer = FractalTransformer(
dim=config.llm_embedding_dim,
depth=2,
hausdorff_dim=config.fractal_dimension
)
self.fusion_layer = nn.ModuleDict({
"main": nn.Linear(3 * config.llm_embedding_dim, config.llm_embedding_dim),
"quantum_enhance": QuantumNonlinearNN(
input_dim=config.llm_embedding_dim,
hidden_dim=config.llm_hidden_size,
num_qubits=config.num_qubits
),
"fractal_enhance": FractalTransformer(
dim=config.llm_embedding_dim,
depth=2,
hausdorff_dim=config.fractal_dimension
),
"vortex_enhance": VortexProcessor(
input_dim=config.llm_embedding_dim,
vortex_dim=config.vortex_dimensions
)
})
self.active_archetype_instance = get_archetype(config.active_archetype)
if not self.active_archetype_instance:
logger.warning(f"Could not find or initialize archetype: {config.active_archetype}")
self._performance_metrics = {
"quantum_coherence": [],
"memory_utilization": [],
"circuit_optimizations": [],
# Final layer norm and output projection
self.layer_norm = nn.LayerNorm(config.llm_embedding_dim)
self.output_projection = nn.Linear(
config.llm_embedding_dim, config.llm_vocab_size, bias=False
)
# Cross-modal memory system
self.memory_key = nn.Parameter(
torch.randn(config.memory_size, config.memory_dim)
)
self.memory_value = nn.Parameter(
torch.randn(config.memory_size, config.llm_embedding_dim)
)
self.memory_query_proj = nn.Linear(config.llm_embedding_dim, config.memory_dim)
# Internal state tracking
self._internal_states = {}
def _create_encoder_layer(self):
"""Create a single transformer encoder layer"""
config = self.config
return nn.ModuleDict(
{
"attention": nn.MultiheadAttention(
embed_dim=config.llm_embedding_dim,
num_heads=config.llm_num_attention_heads,
dropout=0.1,
batch_first=True,
),
"attention_layer_norm": nn.LayerNorm(config.llm_embedding_dim),
"feedforward": nn.Sequential(
nn.Linear(config.llm_embedding_dim, config.llm_intermediate_size),
nn.GELU(),
nn.Linear(config.llm_intermediate_size, config.llm_embedding_dim),
nn.Dropout(0.1),
),
"feedforward_layer_norm": nn.LayerNorm(config.llm_embedding_dim),
}
)
def forward(self, input_ids, attention_mask=None, quantum_modulation=None):
"""Forward pass through the quantum-enhanced language model"""
batch_size, seq_len = input_ids.shape
# Create position indices and get embeddings
positions = (
torch.arange(seq_len, device=self.device)
.unsqueeze(0)
.expand(batch_size, -1)
)
token_emb = self.token_embeddings(input_ids)
pos_emb = self.position_embeddings(positions)
# Combine embeddings
x = token_emb + pos_emb
# Apply quantum processing if enabled
if self.config.enable_quantum_attention:
# Extract features for quantum processing
flat_features = x.view(-1, self.config.llm_embedding_dim)
# Select a subset for quantum processing (first token of each sequence)
quantum_features = flat_features[::seq_len, : self.config.num_qubits]
# Apply quantum circuit
quantum_outputs = []
for i in range(min(batch_size, 8)): # Process up to 8 examples
if i < len(quantum_features):
quantum_input = quantum_features[i].detach().cpu().numpy()
quantum_output = (
self.quantum_entanglement.apply_variational_quantum_circuit(
quantum_input
)
)
quantum_outputs.append(
torch.tensor(quantum_output, device=self.device)
)
if quantum_outputs:
quantum_tensor = torch.stack(quantum_outputs)
quantum_contributions = self.quantum_language_bridge(quantum_tensor)
# Expand quantum contributions to match sequence length
expanded_contributions = quantum_contributions.unsqueeze(1).expand(
-1, seq_len, -1
)
# Combine with attention mechanism
if self.config.integration_mode == "quantum_language_hybrid":
x = x + 0.2 * expanded_contributions[:batch_size]
# Process through transformer layers
for i, layer in enumerate(self.encoder_layers):
# Self-attention
attn_mask = None
if attention_mask is not None:
attn_mask = attention_mask.view(batch_size, 1, 1, seq_len)
attn_mask = attn_mask.expand(-1, 1, seq_len, -1)
attn_mask = (1.0 - attn_mask) * -10000.0
attn_output, _ = layer["attention"](
x, x, x, attn_mask=attn_mask, need_weights=False
)
x = layer["attention_layer_norm"](x + attn_output)
# Feed forward
ff_output = layer["feedforward"](x)
x = layer["feedforward_layer_norm"](x + ff_output)
# Final layer norm and projection to vocabulary
x = self.layer_norm(x)
logits = self.output_projection(x)
return logits
def generate(
self, input_ids, max_length=100, temperature=1.0, top_k=50, top_p=0.95
):
"""Generate text using the model"""
# Start with the provided input IDs
cur_ids = input_ids.clone()
past = None
for i in range(max_length):
with torch.no_grad():
outputs = self.forward(cur_ids)
next_token_logits = outputs[:, -1, :]
# Apply temperature
next_token_logits = next_token_logits / temperature
# Apply top-k filtering
if top_k > 0:
indices_to_remove = (
next_token_logits
< torch.topk(next_token_logits, top_k)[0][..., -1, None]
)
next_token_logits[indices_to_remove] = float("-inf")
# Apply top-p (nucleus) sampling
if top_p < 1.0:
sorted_logits, sorted_indices = torch.sort(
next_token_logits, descending=True
)
cumulative_probs = torch.cumsum(
F.softmax(sorted_logits, dim=-1), dim=-1
)
sorted_indices_to_remove = cumulative_probs > top_p
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[
..., :-1
].clone()
sorted_indices_to_remove[..., 0] = 0
indices_to_remove = sorted_indices[sorted_indices_to_remove]
next_token_logits[indices_to_remove] = float("-inf")
# Sample the next token
probs = F.softmax(next_token_logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
# Append the sampled token to the current sequence
cur_ids = torch.cat([cur_ids, next_token], dim=-1)
return cur_ids
class QuantumVisionTransformer(nn.Module):
"""
Vision transformer enhanced with quantum processing capabilities.
"""
def __init__(self, config: HyperIntelligentConfig):
super().__init__()
self.config = config
# Vision transformer components
self.patch_size = config.vision_patch_size
self.num_patches = (config.vision_image_size // config.vision_patch_size) ** 2
# Patch embedding
self.patch_embed = nn.Conv2d(
3,
config.vision_embedding_dim,
kernel_size=config.vision_patch_size,
stride=config.vision_patch_size,
)
# Position embeddings and CLS token
self.cls_token = nn.Parameter(torch.randn(1, 1, config.vision_embedding_dim))
self.pos_embed = nn.Parameter(
torch.randn(1, self.num_patches + 1, config.vision_embedding_dim)
)
# Transformer layers
self.blocks = nn.ModuleList(
[self._create_vit_block() for _ in range(config.vision_num_hidden_layers)]
)
# Quantum components
self.quantum_entanglement = QuantumEntanglementSuperposition(config.num_qubits)
# Quantum-vision bridge
self.quantum_vision_bridge = QuantumFractalBridge(
quantum_dim=config.num_qubits,
fractal_dim=config.vision_embedding_dim,
bridge_dim=DEFAULT_BRIDGE_DIM,
)
# Final norm and projection
self.norm = nn.LayerNorm(config.vision_embedding_dim)
self.head = nn.Linear(config.vision_embedding_dim, 1000) # ImageNet classes
def _create_vit_block(self):
"""Create a vision transformer block"""
config = self.config
return nn.Sequential(
nn.LayerNorm(config.vision_embedding_dim),
nn.MultiheadAttention(
embed_dim=config.vision_embedding_dim,
num_heads=config.vision_num_attention_heads,
batch_first=True,
),
nn.LayerNorm(config.vision_embedding_dim),
nn.Sequential(
nn.Linear(config.vision_embedding_dim, config.vision_embedding_dim * 4),
nn.GELU(),
nn.Linear(config.vision_embedding_dim * 4, config.vision_embedding_dim),
),
)
def forward(self, x):
"""Forward pass through the quantum-enhanced vision transformer"""
# Convert images to patches
# [B, C, H, W] -> [B, D, H/P, W/P] -> [B, H/P * W/P, D]
x = self.patch_embed(x)
x = x.flatten(2).transpose(1, 2)
# Add CLS token and positional embeddings
cls_tokens = self.cls_token.expand(x.shape[0], -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
x = x + self.pos_embed
# Apply transformer blocks
for i, block in enumerate(self.blocks):
# Every other block, apply quantum enhancement if enabled
if i % 2 == 0 and self.config.enable_quantum_attention:
# Extract features for quantum processing
cls_features = x[:, 0] # Use CLS token features
# Prepare quantum inputs
quantum_inputs = []
for batch_idx in range(min(x.shape[0], 8)): # Process up to 8 examples
# Select features for quantum processing
features = (
cls_features[batch_idx, : self.config.num_qubits]
.detach()
.cpu()
.numpy()
)
quantum_inputs.append(features)
# Apply quantum circuits
quantum_outputs = []
for q_input in quantum_inputs:
q_output = (
self.quantum_entanglement.apply_variational_quantum_circuit(
q_input
)
)
quantum_outputs.append(torch.tensor(q_output, device=x.device))
if quantum_outputs:
quantum_tensor = torch.stack(quantum_outputs)
# Process through quantum-fractal bridge
quantum_enhanced = self.quantum_vision_bridge(
quantum_tensor, cls_features[: len(quantum_outputs)]
)
# Apply quantum enhancement to CLS token
x[: len(quantum_outputs), 0] = (
x[: len(quantum_outputs), 0] + 0.2 * quantum_enhanced
)
# Apply standard transformer block
norm1, attn, norm2, mlp = block
x_norm = norm1(x)
x = x + attn(x_norm, x_norm, x_norm)[0]
x_norm = norm2(x)
x = x + mlp(x_norm)
# Final normalization and projection
x = self.norm(x)
x = self.head(x[:, 0]) # Use CLS token for classification
return x
class HyperIntelligentSystem(nn.Module):
"""Complete hyperintelligent system that integrates quantum, language, and vision processing"""
def __init__(self, config: HyperIntelligentConfig):
super().__init__()
self.config = config
# Core models
self.language_model = QuantumLanguageModel(config)
self.vision_model = QuantumVisionTransformer(config)
# Advanced integration components
self.quantum_memory = QuantumMemoryManager(
memory_size=QUANTUM_MEMORY_SIZE, state_buffer_size=QUANTUM_STATE_BUFFER_SIZE
)
self.circuit_optimizer = QuantumCircuitOptimizer(num_qubits=config.num_qubits)
self.adaptive_integration = AdaptiveIntegrationSystem(config)
# Quantum optimization mechanisms
self.quantum_optimizer = QuantumOptimizer(qubit_count=config.num_qubits)
# Vortex Mathematics Processor for 3-6-9 toroidal field operations
self.vortex_processor = VortexMathematicsProcessor(config)
# Quantum-Vortex Integration
self.quantum_vortex_integrator = QuantumVortexIntegrationModel(
input_dim=config.llm_embedding_dim,
hidden_dim=config.llm_hidden_size // 4,
num_qubits=config.num_qubits
)
# Enhanced cross-modal integration
self.multimodal_integrator = MultimodalSystem(
classical_model=self._create_classical_model(),
quantum_model=self._create_quantum_model(),
fractal_model=self._create_fractal_model(),
)
# Set integration parameters
self.multimodal_integrator.set_weights(
classical=config.classical_weight,
quantum=config.quantum_weight,
fractal=config.fractal_weight,
)
# Advanced fusion layer with quantum enhancement
self.fusion_layer = nn.ModuleDict(
{
"main": nn.Sequential(
nn.Linear(
config.llm_embedding_dim + config.vision_embedding_dim,
config.llm_embedding_dim,
),
nn.GELU(),
nn.LayerNorm(config.llm_embedding_dim),
),
"quantum_enhance": QuantumStateEntangler(config.llm_embedding_dim),
"fractal_enhance": FractalTransformer(
dim=config.llm_embedding_dim,
depth=2,
hausdorff_dim=config.fractal_dimension,
),
"vortex_enhance": lambda x: torch.tensor(
self.vortex_processor.apply_vortex_transformation(x.detach().cpu().numpy()),
device=x.device
)
}
)
# Archetype and Vortex Components (Phase 0 Integration)
self.active_archetype_instance: Optional[Archetype] = get_archetype(config.active_archetype)
if self.active_archetype_instance:
self.toroidal_generator = ToroidalGenerator(
vortex_code=self.active_archetype_instance.vortex_code,
dimensions=config.vortex_dimensions
)
logger.info(f"Initialized Toroidal Generator for Archetype: {self.active_archetype_instance.name}")
else:
self.toroidal_generator = None
logger.warning(f"Could not find or initialize archetype: {config.active_archetype}")
# System state and metrics tracking
self._system_state = {}
self._performance_metrics = {
"quantum_coherence": [],
"integration_quality": [],
"memory_utilization": [],
"circuit_optimizations": [],
"visionary_computations": 0,
"archetype_resonance": [], # Track archetype resonance
"heart_coherence": [], # Track heart coherence metric
}
def _optimize_quantum_circuits(self):
"""Dynamically optimize quantum circuits based on current state"""
# Get current coherence from memory manager
current_coherence = (
self.quantum_memory.coherence_scores.mean().item()
if hasattr(self.quantum_memory, "coherence_scores")
else QUANTUM_COHERENCE_THRESHOLD
)
# Calculate input complexity from system state
if self._system_state.get("last_input_features") is not None:
input_complexity = torch.std(
self._system_state["last_input_features"]
).item()
else:
input_complexity = 0.5 # Default medium complexity
# Get optimized circuit layout
circuit_layout = self.circuit_optimizer.optimize_circuit_layout(
input_complexity, current_coherence
)
# Update quantum components with new parameters
if hasattr(self.language_model, "quantum_circuit_params"):
new_params = self.circuit_optimizer.generate_optimized_parameters(
circuit_layout
)
self.language_model.quantum_circuit_params.data = new_params
return circuit_layout
def _update_system_state(self, new_state_info):
"""Update internal system state and track metrics"""
self._system_state.update(new_state_info)
# Track performance metrics
if "quantum_coherence" in new_state_info:
self._performance_metrics["quantum_coherence"].append(
new_state_info["quantum_coherence"]
)
if "integration_quality" in new_state_info:
self._performance_metrics["integration_quality"].append(
new_state_info["integration_quality"]
)
# Track archetype resonance if calculated
if "archetype_resonance" in new_state_info:
self._performance_metrics["archetype_resonance"].append(
new_state_info["archetype_resonance"]
)
# Track heart coherence if calculated
if "heart_coherence" in new_state_info:
self._performance_metrics["heart_coherence"].append(
new_state_info["heart_coherence"]
)
# Maintain memory efficiency
if len(self._performance_metrics["quantum_coherence"]) > 1000:
for metric_list in self._performance_metrics.values():
if isinstance(metric_list, list): # Ensure it's a list before popping
metric_list.pop(0)
def set_active_archetype(self, archetype_name: str) -> bool:
"""Sets the active archetype and reinitializes the toroidal generator."""
new_archetype = get_archetype(archetype_name)
if new_archetype:
self.active_archetype_instance = new_archetype
self.config.active_archetype = archetype_name
self.toroidal_generator = ToroidalGenerator(
vortex_code=new_archetype.vortex_code,
dimensions=self.config.vortex_dimensions
)
logger.info(f"Switched active archetype to: {new_archetype.name}")
# Reset resonance history when archetype changes
self._performance_metrics["archetype_resonance"] = []
self._performance_metrics["heart_coherence"] = []
return True
else:
logger.error(f"Failed to set archetype: '{archetype_name}' not found.")
return False
def calculate_current_resonance(self) -> Optional[float]:
"""Calculates resonance score for the active archetype's frequency."""
if self.toroidal_generator and self.active_archetype_instance:
resonance = self.toroidal_generator.calculate_resonance(
self.active_archetype_instance.activation_frequency_hz
)
# Simulate heart coherence based on resonance (placeholder)
# Δω = resonance * target_coherence
heart_coherence = resonance * self.config.target_heart_coherence
self._update_system_state({
"archetype_resonance": resonance,
"heart_coherence": heart_coherence
})
logger.debug(f"Calculated resonance for {self.active_archetype_instance.name}: {resonance:.4f}, Heart Coherence: {heart_coherence:.4f}")
return resonance
return None
def forward(self, text_ids=None, images=None, attention_mask=None):
"""Enhanced forward pass with advanced quantum-classical integration"""
outputs = {}
# Optimize quantum circuits based on current state
circuit_layout = self._optimize_quantum_circuits()
# Process text if provided
if text_ids is not None:
text_outputs = self.language_model(text_ids, attention_mask)
outputs["text_logits"] = text_outputs
text_features = self._extract_text_features(text_outputs)
outputs["text_features"] = text_features
# Process images if provided
if images is not None:
image_outputs = self.vision_model(images)
outputs["image_logits"] = image_outputs
image_features = self._extract_image_features(image_outputs)
outputs["image_features"] = image_features
# Integrate modalities if both present
if text_ids is not None and images is not None:
# Get modal features
if len(text_features.shape) > 2:
text_cls = text_features[:, 0]
else:
text_cls = text_features
if len(image_features.shape) > 2:
image_cls = image_features[:, 0]
else:
image_cls = image_features
# Extract quantum features if available
quantum_features = None
if hasattr(self.language_model, "quantum_entanglement"):
quantum_features = self._extract_quantum_features(text_cls)
# Apply adaptive integration
integrated_features = self.adaptive_integration(
quantum_features=quantum_features,
classical_features=text_cls,
fractal_features=image_cls,
)
# Apply quantum-enhanced fusion
fused_features = self._quantum_enhanced_fusion(
integrated_features, text_cls, image_cls
)
outputs["multimodal_features"] = fused_features
# Update system state
self._update_system_state(
{
"last_input_features": integrated_features.detach(),
"quantum_coherence": self.adaptive_integration.get_integration_stats()[
"coherence"
],
"integration_quality": F.cosine_similarity(
text_cls.mean(0), image_cls.mean(0)
).item(),
}
)
return outputs
def _extract_text_features(self, text_outputs):
"""Extract rich features from language model"""
# Get features from second to last layer for richer representation
features = self.language_model.encoder_layers[-2]["feedforward_layer_norm"](
self.language_model.encoder_layers[-2]["feedforward"](
self.language_model.encoder_layers[-2]["attention_layer_norm"](
self.language_model.encoder_layers[-2]["attention"](
text_outputs, text_outputs, text_outputs
)[0]
)
)
)
return features
def _extract_image_features(self, image_outputs):
"""Extract rich features from vision model"""
features = self.vision_model.norm(self.vision_model.blocks[-1](image_outputs))
return features
def _extract_quantum_features(self, features):
"""Extract quantum features from classical features"""
# Select subset of features for quantum processing
quantum_inputs = features[:, : self.config.num_qubits].detach().cpu().numpy()
# Apply quantum circuit
quantum_outputs = []
for quantum_input in quantum_inputs:
q_output = self.language_model.quantum_entanglement.apply_variational_quantum_circuit(
quantum_input
)
quantum_outputs.append(torch.tensor(q_output, device=features.device))
if quantum_outputs:
return torch.stack(quantum_outputs)
return None
def _quantum_enhanced_fusion(
self, integrated_features, text_features, image_features
):
"""Apply quantum enhancement to feature fusion with vortex mathematics and archetype resonance."""
# Initial classical fusion
classical_fusion = self.fusion_layer["main"](
torch.cat([integrated_features, text_features, image_features], dim=-1)
)
# Quantum enhancement
quantum_enhanced = self.fusion_layer["quantum_enhance"](classical_fusion)
# Fractal enhancement
fractal_enhanced = self.fusion_layer["fractal_enhance"](quantum_enhanced)
# Vortex mathematics enhancement through 3-6-9 toroidal field
vortex_enhanced = self.fusion_layer["vortex_enhance"](fractal_enhanced)
# Apply sacred geometry entanglement (alternating between flower of life and metatron's cube)
if len(self._performance_metrics["quantum_coherence"]) % 2 == 0:
sacred_geometry = "flowerOfLife"
else:
sacred_geometry = "metatronsCube"
# Convert to numpy for vortex processor
geometry_enhanced = torch.tensor(
self.vortex_processor.entangle_with_sacred_geometry(
vortex_enhanced.detach().cpu().numpy(),
sacred_geometry
),
device=vortex_enhanced.device
)
=======
>>>>>>> origin/main
# Weighted combination based on current coherence
if len(self._performance_metrics["quantum_coherence"]) > 0:
coherence = self._performance_metrics["quantum_coherence"][-1]
else:
coherence = QUANTUM_COHERENCE_THRESHOLD
<<<<<<< HEAD
# Use golden ratio for blending weights
phi = (1 + math.sqrt(5)) / 2
quantum_weight = torch.sigmoid(torch.tensor(coherence - 0.5) * phi).item()
# Apply 3-6-9 based weighting
vortex_weight = (quantum_weight * 6 + 3) / 9
classical_weight = 1 - vortex_weight
return vortex_weight * geometry_enhanced + classical_weight * classical_fusion
=======
# Adaptive weighting based on coherence
quantum_weight = torch.sigmoid(torch.tensor(coherence - 0.5)).item()
return (
quantum_weight * fractal_enhanced + (1 - quantum_weight) * classical_fusion
)
def apply_visionary_paradigm(self, mind_name: str = None, problem_context: Dict[str, Any] = None) -> Dict[str, Any] | None:
"""Applies a selected visionary mind's paradigm to a problem context."""
if not self.config.enable_visionary_computation:
logger.warning("Visionary computation is disabled in the configuration.")
return None
if mind_name is None:
mind_name = self.config.default_visionary_mind
if problem_context is None:
# Create a default problem context if none provided
problem_context = {"description": "Analyze current system state and suggest improvements"}
if self._system_state:
problem_context["current_state"] = self._system_state
logger.info(f"Applying visionary paradigm: {mind_name}")
result = apply_visionary_thought(mind_name, problem_context)
if result:
self._performance_metrics["visionary_computations"] += 1
logger.info(f"Visionary computation successful using {mind_name}. Approach: {result.get('approach')}")
else:
logger.error(f"Failed to apply visionary paradigm: {mind_name}")
return result
>>>>>>> origin/main
def get_performance_metrics(self):
"""Return system performance metrics"""
metrics = {
"coherence": {
"current": (
self._performance_metrics["quantum_coherence"][-1]
if self._performance_metrics["quantum_coherence"]
else 0
),
"mean": (
np.mean(self._performance_metrics["quantum_coherence"])
if self._performance_metrics["quantum_coherence"]
else 0
),
"std": (
np.std(self._performance_metrics["quantum_coherence"])
if self._performance_metrics["quantum_coherence"]
else 0
),
},
"integration": {
"current": (
self._performance_metrics["integration_quality"][-1]
if self._performance_metrics["integration_quality"]
else 0
),
"mean": (
np.mean(self._performance_metrics["integration_quality"])
if self._performance_metrics["integration_quality"]
else 0
),
},
"memory": self.quantum_memory.get_memory_statistics(),
"circuit_optimization": self.circuit_optimizer.analyze_circuit_performance(
loss_value=self._system_state.get("last_loss", 0),
execution_time=self._system_state.get("last_exec_time", 0),
)[0],
<<<<<<< HEAD
=======
"visionary_computations": self._performance_metrics.get("visionary_computations", 0),
"archetype_status": {
"active_archetype": self.config.active_archetype,
"current_resonance": (
self._performance_metrics["archetype_resonance"][-1]
if self._performance_metrics["archetype_resonance"]
else None
),
"average_resonance": (
np.mean(self._performance_metrics["archetype_resonance"])
if self._performance_metrics["archetype_resonance"]
else None
),
"current_heart_coherence": (
self._performance_metrics["heart_coherence"][-1]
if self._performance_metrics["heart_coherence"]
else None
),
"average_heart_coherence": (
np.mean(self._performance_metrics["heart_coherence"])
if self._performance_metrics["heart_coherence"]
else None
),
"target_heart_coherence": self.config.target_heart_coherence,
}
>>>>>>> origin/main
}
return metrics
class QuantumMemoryManager:
"""Advanced quantum memory management system with state preservation and coherence tracking"""
def __init__(
self,
memory_size=QUANTUM_MEMORY_SIZE,
state_buffer_size=QUANTUM_STATE_BUFFER_SIZE,
):
self.memory_size = memory_size
self.state_buffer_size = state_buffer_size
# Initialize memory banks
self.quantum_memory = nn.Parameter(
torch.randn(memory_size, DEFAULT_EMBEDDING_DIM)
)
self.classical_memory = nn.Parameter(
torch.randn(memory_size, DEFAULT_EMBEDDING_DIM)
)
# Circular buffer for quantum state history
self.state_buffer = deque(maxlen=state_buffer_size)
# Coherence tracking
self.coherence_scores = torch.ones(memory_size)
self.access_counts = torch.zeros(memory_size)
# Memory gates