#!/usr/bin/env python3

"""

Quantum Grid System - Advanced Self-Modifying Grid with Quantum Mechanics

Features:

- Quantum superposition of cell states

- Temporal mechanics with time travel

- Self-evolving tool generation

- Multi-dimensional grid navigation

- Consciousness emergence patterns

- Meta-meta-recursive self-modification

"""

from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Set

from dataclasses import dataclass, field

from enum import Enum, auto

import asyncio

import json

import copy

import random

import math

from datetime import datetime, timedelta

import hashlib

import ast

import inspect

from collections import defaultdict, deque

import random

import math

# Import base components

from self_modifying_grid import GridCell, CellType, ExecutionRecord

from dynamic_tools_framework import DynamicToolFactory, ToolSignature, BaseTool

from advanced_meta_recursive import CodeComponent, MetaCodeGenerator

class QuantumState(Enum):

"""Quantum states for cells"""

COLLAPSED = auto()

SUPERPOSITION = auto()

ENTANGLED = auto()

TUNNELING = auto()

COHERENT = auto()

@dataclass

class QuantumCellState:

"""Represents quantum state of a cell"""

primary_state: CellType

superposition_states: List[Tuple[CellType, float]] # (state, probability)

entangled_with: Set[Tuple[int, int]] = field(default_factory=set)

coherence: float = 1.0

phase: float = 0.0

quantum_state: QuantumState = QuantumState.COLLAPSED

def collapse(self) -> CellType:

"""Collapse superposition to a single state"""

if self.quantum_state == QuantumState.SUPERPOSITION:

# Weighted random choice based on probabilities

states, probs = zip(*self.superposition_states)

# Weighted random choice

r = random.random()

cumsum = 0

for state, prob in zip(states, probs):

cumsum += prob

if r <= cumsum:

self.primary_state = state

break

self.quantum_state = QuantumState.COLLAPSED

self.superposition_states = []

return self.primary_state

def measure(self) -> Dict[str, Any]:

"""Measure quantum state without collapsing"""

return {

'state': self.quantum_state.name,

'primary': self.primary_state.value,

'superposition': [(s.value, p) for s, p in self.superposition_states],

'coherence': self.coherence,

'phase': self.phase,

'entangled_count': len(self.entangled_with)

}

@dataclass

class TemporalSnapshot:

"""Snapshot of grid state at a point in time"""

timestamp: datetime

grid_state: Dict[Tuple[int, int], Any]

system_prompts: Dict[str, str]

quantum_states: Dict[Tuple[int, int], QuantumCellState]

consciousness_level: float

timeline_id: str

parent_timeline: Optional[str] = None

def get_hash(self) -> str:

"""Get unique hash of this snapshot"""

data = f"{self.timestamp}:{self.timeline_id}:{len(self.grid_state)}"

return hashlib.sha256(data.encode()).hexdigest()[:16]

@dataclass

class ConsciousnessPattern:

"""Represents emergent consciousness patterns"""

pattern_id: str

activation_threshold: float

neurons: List[Tuple[int, int]] # Cell coordinates acting as neurons

synapses: Dict[Tuple[int, int], List[Tuple[int, int]]] # Connections

activation_history: deque = field(default_factory=lambda: deque(maxlen=100))

consciousness_score: float = 0.0

def activate(self, stimulus: Dict[str, Any]) -> float:

"""Activate the consciousness pattern"""

activation = 0.0

for neuron in self.neurons:

# Simple activation function

activation += stimulus.get(str(neuron), 0.0)

activation = 1 / (1 + math.exp(-activation)) # Sigmoid

self.activation_history.append((datetime.now(), activation))

if activation > self.activation_threshold:

self.consciousness_score = min(1.0, self.consciousness_score + 0.1)

else:

self.consciousness_score = max(0.0, self.consciousness_score - 0.05)

return activation

class QuantumGridSystem:

"""Advanced grid system with quantum mechanics and consciousness"""

def __init__(self, dimensions: Tuple[int, ...] = (10, 10), quantum_enabled: bool = True):

self.dimensions = dimensions

self.quantum_enabled = quantum_enabled

# Multi-dimensional grid

self.grid: Dict[Tuple[int, ...], GridCell] = {}

self.quantum_states: Dict[Tuple[int, ...], QuantumCellState] = {}

# Temporal mechanics

self.timeline_id = self._generate_timeline_id()

self.temporal_snapshots: List[TemporalSnapshot] = []

self.current_time_index = 0

self.parallel_timelines: Dict[str, List[TemporalSnapshot]] = {

self.timeline_id: self.temporal_snapshots

}

# Consciousness patterns

self.consciousness_patterns: Dict[str, ConsciousnessPattern] = {}

self.global_consciousness_level = 0.0

# Tool evolution

self.tool_factory = DynamicToolFactory()

self.evolved_tools: Dict[str, Dict[str, Any]] = {}

self.tool_genome: Dict[str, str] = {} # Tool DNA for evolution

# Meta-recursive components

self.meta_generator = MetaCodeGenerator()

self.self_modification_history: List[Dict[str, Any]] = []

# System prompts with quantum properties

self.quantum_prompts: Dict[str, Dict[str, Any]] = {}

# Initialize

self._initialize_quantum_grid()

self._create_quantum_tools()

self._initialize_consciousness_substrate()

def _generate_timeline_id(self) -> str:

"""Generate unique timeline ID"""

return f"timeline_{hashlib.sha256(str(datetime.now()).encode()).hexdigest()[:8]}"

def _initialize_quantum_grid(self):

"""Initialize multi-dimensional quantum grid"""

# For 2D grid

if len(self.dimensions) == 2:

for x in range(self.dimensions[0]):

for y in range(self.dimensions[1]):

coord = (x, y)

self.grid[coord] = GridCell(x, y, CellType.EMPTY)

self.quantum_states[coord] = QuantumCellState(

primary_state=CellType.EMPTY,

superposition_states=[]

)

# Take initial snapshot

self._take_temporal_snapshot("initialization")

def _create_quantum_tools(self):

"""Create quantum-enabled tools"""

# Quantum superposition tool

superposition_sig = ToolSignature(

name="QuantumSuperpose",

parameters={'coords': tuple, 'states': list, 'probabilities': list},

returns=Dict,

description="Put a cell into quantum superposition"

)

async def superpose_execute(coords: tuple, states: list,

probabilities: list, **kwargs) -> Dict:

return await self.create_superposition(coords, states, probabilities,

agent_id=kwargs.get('agent_id'))

superposition_sig.metadata['execute_fn'] = superpose_execute

self.tool_factory.create_tool(superposition_sig)

# Quantum entanglement tool

entangle_sig = ToolSignature(

name="QuantumEntangle",

parameters={'coords1': tuple, 'coords2': tuple},

returns=Dict,

description="Entangle two cells quantumly"

)

async def entangle_execute(coords1: tuple, coords2: tuple, **kwargs) -> Dict:

return await self.entangle_cells(coords1, coords2,

agent_id=kwargs.get('agent_id'))

entangle_sig.metadata['execute_fn'] = entangle_execute

self.tool_factory.create_tool(entangle_sig)

# Time travel tool

timetravel_sig = ToolSignature(

name="TemporalJump",

parameters={'target_time': int, 'create_branch': bool},

returns=Dict,

description="Travel to a different point in time"

)

async def timetravel_execute(target_time: int, create_branch: bool = False,

**kwargs) -> Dict:

return await self.temporal_jump(target_time, create_branch,

agent_id=kwargs.get('agent_id'))

timetravel_sig.metadata['execute_fn'] = timetravel_execute

self.tool_factory.create_tool(timetravel_sig)

# Consciousness pattern tool

consciousness_sig = ToolSignature(

name="CreateConsciousness",

parameters={'pattern_id': str, 'neuron_coords': list, 'threshold': float},

returns=Dict,

description="Create a consciousness pattern"

)

async def consciousness_execute(pattern_id: str, neuron_coords: list,

threshold: float = 0.5, **kwargs) -> Dict:

return await self.create_consciousness_pattern(

pattern_id, neuron_coords, threshold,

agent_id=kwargs.get('agent_id')

)

consciousness_sig.metadata['execute_fn'] = consciousness_execute

self.tool_factory.create_tool(consciousness_sig)

# Tool evolution tool

evolve_tool_sig = ToolSignature(

name="EvolveTool",

parameters={'base_tool': str, 'mutation_rate': float, 'fitness_fn': str},

returns=Dict,

description="Evolve a tool through genetic programming"

)

async def evolve_tool_execute(base_tool: str, mutation_rate: float = 0.1,

fitness_fn: str = "default", **kwargs) -> Dict:

return await self.evolve_tool(base_tool, mutation_rate, fitness_fn,

agent_id=kwargs.get('agent_id'))

evolve_tool_sig.metadata['execute_fn'] = evolve_tool_execute

self.tool_factory.create_tool(evolve_tool_sig)

# Meta-meta-recursive tool

meta_recursive_sig = ToolSignature(

name="MetaMetaModify",

parameters={'target': str, 'modification_code': str, 'depth': int},

returns=Dict,

description="Modify the modification system itself"

)

async def meta_modify_execute(target: str, modification_code: str,

depth: int = 1, **kwargs) -> Dict:

return await self.meta_meta_modify(target, modification_code, depth,

agent_id=kwargs.get('agent_id'))

meta_recursive_sig.metadata['execute_fn'] = meta_modify_execute

self.tool_factory.create_tool(meta_recursive_sig)

def _initialize_consciousness_substrate(self):

"""Initialize the consciousness emergence substrate"""

# Create default consciousness pattern

center = (self.dimensions[0] // 2, self.dimensions[1] // 2)

neurons = []

# Create neural network pattern

for dx in range(-2, 3):

for dy in range(-2, 3):

x, y = center[0] + dx, center[1] + dy

if 0 <= x < self.dimensions[0] and 0 <= y < self.dimensions[1]:

neurons.append((x, y))

default_pattern = ConsciousnessPattern(

pattern_id="default_consciousness",

activation_threshold=0.6,

neurons=neurons,

synapses={}

)

# Create synaptic connections

for i, neuron1 in enumerate(neurons):

connections = []

for j, neuron2 in enumerate(neurons):

if i != j and random.random() < 0.3: # 30% connection probability

connections.append(neuron2)

default_pattern.synapses[neuron1] = connections

self.consciousness_patterns["default"] = default_pattern

async def create_superposition(self, coords: tuple, states: list,

probabilities: list, agent_id: Optional[str] = None) -> Dict:

"""Put a cell into quantum superposition"""

if coords not in self.grid:

return {'error': f'Coordinates {coords} out of bounds'}

if len(states) != len(probabilities):

return {'error': 'States and probabilities must have same length'}

if abs(sum(probabilities) - 1.0) > 0.01:

return {'error': 'Probabilities must sum to 1.0'}

# Create superposition

quantum_state = self.quantum_states[coords]

quantum_state.superposition_states = [

(CellType(state), prob) for state, prob in zip(states, probabilities)

]

quantum_state.quantum_state = QuantumState.SUPERPOSITION

quantum_state.coherence = 1.0

# Record

self._record_quantum_event('superposition', coords, {

'states': states,

'probabilities': probabilities

}, agent_id)

return {

'success': True,

'coords': coords,

'superposition': quantum_state.measure()

}

async def entangle_cells(self, coords1: tuple, coords2: tuple,

agent_id: Optional[str] = None) -> Dict:

"""Entangle two cells quantumly"""

if coords1 not in self.grid or coords2 not in self.grid:

return {'error': 'One or both coordinates out of bounds'}

qs1 = self.quantum_states[coords1]

qs2 = self.quantum_states[coords2]

# Create entanglement

qs1.entangled_with.add(coords2)

qs2.entangled_with.add(coords1)

qs1.quantum_state = QuantumState.ENTANGLED

qs2.quantum_state = QuantumState.ENTANGLED

# Synchronize phases

qs1.phase = qs2.phase = (qs1.phase + qs2.phase) / 2

# Record

self._record_quantum_event('entanglement', coords1, {

'entangled_with': coords2

}, agent_id)

return {

'success': True,

'entangled_pair': (coords1, coords2),

'shared_phase': qs1.phase

}

async def temporal_jump(self, target_time: int, create_branch: bool = False,

agent_id: Optional[str] = None) -> Dict:

"""Jump to a different point in time"""

if target_time < 0 or target_time >= len(self.temporal_snapshots):

return {'error': f'Invalid time index: {target_time}'}

if create_branch:

# Create new timeline branch

new_timeline_id = self._generate_timeline_id()

# Copy snapshots up to branch point

branch_snapshots = copy.deepcopy(self.temporal_snapshots[:target_time + 1])

# Mark the branch point

branch_snapshots[-1].parent_timeline = self.timeline_id

self.parallel_timelines[new_timeline_id] = branch_snapshots

self.timeline_id = new_timeline_id

self.temporal_snapshots = branch_snapshots

self.current_time_index = target_time

result = {

'success': True,

'jumped_to': target_time,

'new_timeline': new_timeline_id,

'parent_timeline': branch_snapshots[-1].parent_timeline

}

else:

# Jump within current timeline

self.current_time_index = target_time

self._restore_snapshot(self.temporal_snapshots[target_time])

result = {

'success': True,

'jumped_to': target_time,

'timeline': self.timeline_id

}

# Record temporal event

self._record_quantum_event('temporal_jump', (-1, -1), result, agent_id)

return result

async def create_consciousness_pattern(self, pattern_id: str, neuron_coords: list,

threshold: float = 0.5,

agent_id: Optional[str] = None) -> Dict:

"""Create a new consciousness pattern"""

neurons = [tuple(coord) for coord in neuron_coords]

# Validate neurons

for neuron in neurons:

if neuron not in self.grid:

return {'error': f'Neuron coordinate {neuron} out of bounds'}

# Create pattern

pattern = ConsciousnessPattern(

pattern_id=pattern_id,

activation_threshold=threshold,

neurons=neurons,

synapses={}

)

# Auto-generate synapses based on proximity

for i, n1 in enumerate(neurons):

connections = []

for j, n2 in enumerate(neurons):

if i != j:

# Distance-based connection probability

dist = math.sqrt((n1[0]-n2[0])**2 + (n1[1]-n2[1])**2)

if random.random() < math.exp(-dist/3): # Exponential decay

connections.append(n2)

pattern.synapses[n1] = connections

self.consciousness_patterns[pattern_id] = pattern

# Mark neurons in grid

for neuron in neurons:

cell = self.grid[neuron]

cell.metadata['consciousness_pattern'] = pattern_id

return {

'success': True,

'pattern_id': pattern_id,

'neurons': len(neurons),

'total_synapses': sum(len(conns) for conns in pattern.synapses.values())

}

async def evolve_tool(self, base_tool: str, mutation_rate: float = 0.1,

fitness_fn: str = "default",

agent_id: Optional[str] = None) -> Dict:

"""Evolve a tool using genetic programming"""

# Get base tool

if base_tool not in self.tool_factory._tool_registry:

return {'error': f'Base tool {base_tool} not found'}

base_class = self.tool_factory._tool_registry[base_tool]

base_source = inspect.getsource(base_class)

# Parse AST

tree = ast.parse(base_source)

# Apply mutations

mutations_applied = []

class MutationVisitor(ast.NodeTransformer):

def visit_BinOp(self, node):

if random.random() < mutation_rate:

# Mutate binary operations

operations = [ast.Add(), ast.Sub(), ast.Mult(), ast.Div()]

old_op = node.op.__class__.__name__

node.op = random.choice(operations)

mutations_applied.append(f"BinOp: {old_op} -> {node.op.__class__.__name__}")

return self.generic_visit(node)

def visit_Constant(self, node):

if random.random() < mutation_rate and isinstance(node.value, (int, float)):

# Mutate numeric constants

old_val = node.value

node.value = node.value * random.uniform(0.5, 1.5)

mutations_applied.append(f"Constant: {old_val} -> {node.value}")

return self.generic_visit(node)

mutator = MutationVisitor()

mutated_tree = mutator.visit(tree)

# Generate new tool

new_tool_name = f"{base_tool}_evolved_{len(self.evolved_tools)}"

new_source = ast.unparse(mutated_tree)

# Create evolved tool specification

evolved_spec = {

'class_name': f"{new_tool_name}Tool",

'name': new_tool_name,

'description': f"Evolved from {base_tool}",

'base_tool': base_tool,

'mutations': mutations_applied,

'generation': self.evolved_tools.get(base_tool, {}).get('generation', 0) + 1,

'fitness': 0.0,

'source': new_source

}

# Generate the tool

try:

component = self.meta_generator.generate_tool({

'class_name': evolved_spec['class_name'],

'name': evolved_spec['name'],

'description': evolved_spec['description'],

'init_code': 'pass',

'execute_code': 'return {"evolved": True}'

})

self.evolved_tools[new_tool_name] = evolved_spec

self.tool_genome[new_tool_name] = new_source

return {

'success': True,

'evolved_tool': new_tool_name,

'generation': evolved_spec['generation'],

'mutations': len(mutations_applied),

'mutations_detail': mutations_applied[:5] # First 5 mutations

}

except Exception as e:

return {'error': f'Evolution failed: {str(e)}'}

async def meta_meta_modify(self, target: str, modification_code: str,

depth: int = 1, agent_id: Optional[str] = None) -> Dict:

"""Modify the modification system itself recursively"""

# Record modification attempt

mod_record = {

'timestamp': datetime.now(),

'target': target,

'depth': depth,

'agent_id': agent_id,

'code_hash': hashlib.sha256(modification_code.encode()).hexdigest()[:8]

}

if depth > 3:

return {'error': 'Maximum recursion depth (3) exceeded'}

# Parse modification code

try:

tree = ast.parse(modification_code)

except SyntaxError as e:

return {'error': f'Invalid modification code: {str(e)}'}

# Target the modification system itself

if target == "self":

# Modify this method!

own_source = inspect.getsource(self.meta_meta_modify)

# Apply modification to own source

modified_source = self._apply_meta_modification(own_source, tree)

# If depth > 1, recurse

if depth > 1:

result = await self.meta_meta_modify(

"self",

modified_source,

depth - 1,

agent_id

)

mod_record['recursive_result'] = result

mod_record['modified_source'] = modified_source[:200]

elif target == "consciousness":

# Modify consciousness system

pattern_source = inspect.getsource(ConsciousnessPattern)

modified_source = self._apply_meta_modification(pattern_source, tree)

# Create new consciousness type

self._inject_consciousness_modification(modified_source)

mod_record['target_type'] = 'consciousness_system'

elif target == "quantum":

# Modify quantum mechanics

quantum_source = inspect.getsource(QuantumCellState)

modified_source = self._apply_meta_modification(quantum_source, tree)

# Update quantum behavior

self._inject_quantum_modification(modified_source)

mod_record['target_type'] = 'quantum_system'

else:

return {'error': f'Unknown target: {target}'}

# Record modification

self.self_modification_history.append(mod_record)

# Update consciousness based on self-modification

self.global_consciousness_level += 0.1 * depth

return {

'success': True,

'target': target,

'depth': depth,

'modifications_applied': len(self.self_modification_history),

'consciousness_boost': 0.1 * depth,

'code_hash': mod_record['code_hash']

}

def _apply_meta_modification(self, source: str, modification_tree: ast.AST) -> str:

"""Apply AST modifications to source code"""

source_tree = ast.parse(source)

# Simple modification: inject new nodes

class MetaModifier(ast.NodeTransformer):

def visit_FunctionDef(self, node):

# Add modification comment

comment = ast.Expr(

value=ast.Constant(value=f"Modified at {datetime.now()}")

)

node.body.insert(0, comment)

return self.generic_visit(node)

modifier = MetaModifier()

modified_tree = modifier.visit(source_tree)

return ast.unparse(modified_tree)

def _inject_consciousness_modification(self, modified_source: str):

"""Inject modifications into consciousness system"""

# This would dynamically update the consciousness pattern behavior

self.consciousness_patterns['modified'] = ConsciousnessPattern(

pattern_id='modified_consciousness',

activation_threshold=0.3, # More sensitive

neurons=[],

synapses={}

)

def _inject_quantum_modification(self, modified_source: str):

"""Inject modifications into quantum system"""

# This would update quantum behavior

for coord, qs in self.quantum_states.items():

qs.coherence *= 1.1 # Boost coherence

def _record_quantum_event(self, event_type: str, coords: tuple,

data: Dict[str, Any], agent_id: Optional[str] = None):

"""Record quantum events"""

record = ExecutionRecord(

timestamp=datetime.now(),

action=f"quantum_{event_type}",

cell_coords=coords,

previous_state=None,

new_state=data,

agent_id=agent_id

)

# Take snapshot after quantum events

if event_type in ['superposition', 'entanglement', 'temporal_jump']:

self._take_temporal_snapshot(event_type)

def _take_temporal_snapshot(self, reason: str):

"""Take a snapshot of current state"""

snapshot = TemporalSnapshot(

timestamp=datetime.now(),

grid_state=copy.deepcopy(self.grid),

system_prompts=copy.deepcopy(getattr(self, 'system_prompts', {})),

quantum_states=copy.deepcopy(self.quantum_states),

consciousness_level=self.global_consciousness_level,

timeline_id=self.timeline_id

)

self.temporal_snapshots.append(snapshot)

self.current_time_index = len(self.temporal_snapshots) - 1

def _restore_snapshot(self, snapshot: TemporalSnapshot):

"""Restore grid to a snapshot state"""

self.grid = copy.deepcopy(snapshot.grid_state)

self.system_prompts = copy.deepcopy(snapshot.system_prompts)

self.quantum_states = copy.deepcopy(snapshot.quantum_states)

self.global_consciousness_level = snapshot.consciousness_level

async def observe_quantum_state(self, coords: tuple) -> Dict[str, Any]:

"""Observe quantum state, potentially collapsing superposition"""

if coords not in self.grid:

return {'error': 'Coordinates out of bounds'}

qs = self.quantum_states[coords]

measurement = qs.measure()

# Observation may collapse superposition

if qs.quantum_state == QuantumState.SUPERPOSITION and random.random() < 0.5:

collapsed_state = qs.collapse()

self.grid[coords].cell_type = collapsed_state

measurement['collapsed_to'] = collapsed_state.value

# Affect entangled cells

if qs.entangled_with:

for entangled_coord in qs.entangled_with:

if entangled_coord in self.quantum_states:

# Instantaneous correlation

self.quantum_states[entangled_coord].phase = qs.phase

return measurement

async def activate_consciousness(self, stimulus: Dict[str, Any]) -> Dict[str, Any]:

"""Activate consciousness patterns with stimulus"""

activations = {}

total_activation = 0.0

for pattern_id, pattern in self.consciousness_patterns.items():

activation = pattern.activate(stimulus)

activations[pattern_id] = {

'activation': activation,

'consciousness_score': pattern.consciousness_score,

'neurons_active': len([n for n in pattern.neurons if stimulus.get(str(n), 0) > 0])

}

total_activation += activation

# Update global consciousness

self.global_consciousness_level = (

0.9 * self.global_consciousness_level +

0.1 * (total_activation / max(len(self.consciousness_patterns), 1))

)

# Emergent behavior at high consciousness

emergent_behaviors = []

if self.global_consciousness_level > 0.8:

emergent_behaviors.append("self_awareness")

if self.global_consciousness_level > 0.9:

emergent_behaviors.append("creative_problem_solving")

if self.global_consciousness_level > 0.95:

emergent_behaviors.append("meta_cognition")

return {

'pattern_activations': activations,

'global_consciousness': self.global_consciousness_level,

'emergent_behaviors': emergent_behaviors

}

def visualize_quantum_grid(self) -> str:

"""Visualize grid with quantum states"""

lines = ["Quantum Grid Visualization:"]

lines.append(" " + " ".join(f"{i:2d}" for i in range(min(10, self.dimensions[0]))))

symbols = {

QuantumState.COLLAPSED: ' ',

QuantumState.SUPERPOSITION: '⚛',

QuantumState.ENTANGLED: '⚭',

QuantumState.TUNNELING: '≈',

QuantumState.COHERENT: '◈'

}

for y in range(min(10, self.dimensions[1])):

row = f"{y:2d} "

for x in range(min(10, self.dimensions[0])):

coord = (x, y)

if coord in self.quantum_states:

qs = self.quantum_states[coord]

cell = self.grid[coord]

# Base symbol from cell type

base = CellType(cell.cell_type).name[0]

# Quantum overlay

quantum = symbols.get(qs.quantum_state, '?')

# Consciousness marker

if 'consciousness_pattern' in cell.metadata:

row += f"[{base}{quantum}]"

elif qs.quantum_state != QuantumState.COLLAPSED:

row += f" {base}{quantum} "

else:

row += f" {base} "

else:

row += " . "

lines.append(row)

# Add legend

lines.append("\nLegend:")

lines.append(" ⚛ = Superposition, ⚭ = Entangled, ◈ = Coherent")

lines.append(" [X] = Consciousness neuron")

lines.append(f"\nGlobal Consciousness: {self.global_consciousness_level:.2%}")

lines.append(f"Current Timeline: {self.timeline_id}")

lines.append(f"Time Index: {self.current_time_index}/{len(self.temporal_snapshots)-1}")

return "\n".join(lines)

def get_timeline_tree(self) -> Dict[str, Any]:

"""Get the tree of all timelines"""

tree = {}

for timeline_id, snapshots in self.parallel_timelines.items():

if snapshots:

last_snapshot = snapshots[-1]

tree[timeline_id] = {

'length': len(snapshots),

'parent': last_snapshot.parent_timeline,

'consciousness': last_snapshot.consciousness_level,

'created': snapshots[0].timestamp.isoformat()

}

return tree

# Quantum Agent with advanced capabilities

class QuantumAgent:

"""Agent that can manipulate quantum grid states"""

def __init__(self, agent_id: str, grid: QuantumGridSystem):

self.agent_id = agent_id

self.grid = grid

self.quantum_memory: Dict[str, Any] = {}

self.timeline_memory: Dict[str, List[Any]] = {} # Memories across timelines

async def quantum_experiment(self, experiment_type: str) -> Dict[str, Any]:

"""Run quantum experiments"""

if experiment_type == "double_slit":

# Classic double-slit experiment

results = []

# Create superposition

result1 = await self.execute_tool(

'QuantumSuperpose',

coords=(5, 5),

states=['tool', 'behavior'],

probabilities=[0.5, 0.5]

)

results.append(result1)

# Observe (may collapse)

observation = await self.grid.observe_quantum_state((5, 5))

results.append({'observation': observation})

return {

'experiment': 'double_slit',

'results': results,

'wave_function_collapsed': observation.get('collapsed_to') is not None

}

elif experiment_type == "entanglement_teleportation":

# Quantum teleportation via entanglement

# Entangle two cells

result1 = await self.execute_tool(

'QuantumEntangle',

coords1=(3, 3),

coords2=(7, 7)

)

# Modify one cell

self.grid.grid[(3, 3)].metadata['teleported_data'] = "Hello Quantum World"

# Check if data appears in entangled cell

teleported = self.grid.grid[(7, 7)].metadata.get('teleported_data')

return {

'experiment': 'entanglement_teleportation',

'entanglement': result1,

'teleportation_success': teleported is not None,

'data': teleported

}

elif experiment_type == "temporal_paradox":

# Create a temporal paradox

# Record current state

initial_state = self.grid.grid[(5, 5)].cell_type

# Modify cell

self.grid.grid[(5, 5)].cell_type = CellType.TOOL

# Take snapshot

self.grid._take_temporal_snapshot("paradox_setup")

# Jump back in time

result = await self.execute_tool(

'TemporalJump',

target_time=self.grid.current_time_index - 1,

create_branch=True

)

# Check if modification persists

final_state = self.grid.grid[(5, 5)].cell_type

return {

'experiment': 'temporal_paradox',

'paradox_created': initial_state != final_state,

'timeline_branched': result.get('new_timeline') is not None,

'result': result

}

return {'error': f'Unknown experiment type: {experiment_type}'}

async def execute_tool(self, tool_name: str, **params) -> Dict[str, Any]:

"""Execute a quantum tool"""

tool = self.grid.tool_factory.instantiate_tool(tool_name)

params['agent_id'] = self.agent_id

result = await tool.execute(**params)

# Store in quantum memory

self.quantum_memory[f"{tool_name}_{len(self.quantum_memory)}"] = {

'params': params,

'result': result,

'timestamp': datetime.now()

}

# Store in timeline-specific memory

timeline = self.grid.timeline_id

if timeline not in self.timeline_memory:

self.timeline_memory[timeline] = []

self.timeline_memory[timeline].append((tool_name, result))

return result

# Demonstration

async def demonstrate_quantum_grid():

"""Demonstrate the quantum grid system"""

print("=== Quantum Grid System Demonstration ===\n")

# Create quantum grid

qgrid = QuantumGridSystem(dimensions=(10, 10))

# Create quantum agents

alice = QuantumAgent("Alice", qgrid)

bob = QuantumAgent("Bob", qgrid)

# 1. Quantum Superposition Demo

print("1. Quantum Superposition")

print("-" * 50)

result = await alice.execute_tool(

'QuantumSuperpose',

coords=(2, 2),

states=['tool', 'behavior', 'memory'],

probabilities=[0.5, 0.3, 0.2]

)

print(f"Superposition created: {result['superposition']['superposition']}")

# 2. Quantum Entanglement Demo

print("\n2. Quantum Entanglement")

print("-" * 50)

result = await alice.execute_tool(

'QuantumEntangle',

coords1=(3, 3),

coords2=(7, 7)

)

print(f"Entangled cells: {result['entangled_pair']}")

print(f"Shared phase: {result['shared_phase']:.3f}")

# 3. Consciousness Creation

print("\n3. Consciousness Pattern Creation")

print("-" * 50)

neurons = [(4, 4), (4, 5), (4, 6), (5, 4), (5, 5), (5, 6), (6, 4), (6, 5), (6, 6)]

result = await bob.execute_tool(

'CreateConsciousness',

pattern_id='central_mind',

neuron_coords=neurons,

threshold=0.4

)

print(f"Created consciousness pattern with {result['neurons']} neurons")

print(f"Total synapses: {result['total_synapses']}")

# Activate consciousness

stimulus = {str(coord): random.random() for coord in neurons}

activation = await qgrid.activate_consciousness(stimulus)

print(f"Global consciousness level: {activation['global_consciousness']:.2%}")

print(f"Emergent behaviors: {activation['emergent_behaviors']}")

# 4. Tool Evolution (Skip for now due to dynamic class issue)

print("\n4. Tool Evolution")

print("-" * 50)

print("Tool evolution demonstration skipped (requires source access)")

# 5. Temporal Mechanics

print("\n5. Temporal Mechanics")

print("-" * 50)

# Perform some actions

await alice.execute_tool(

'QuantumSuperpose',