import matplotlib.pyplot as plt
import networkx as nx
class ParadoxProcess:
def init(self, description):
self.description = description
self.next_process = None # For chaining processes
def add_process(self, process):
if self.next_process:
current = self.next_process
while current.next_process:
current = current.next_process
current.next_process = process
else:
self.next_process = process
def display(self):
print(self.description)
current = self.next_process
while current:
print(" ->", current.description)
current = current.next_process
class FeedbackFramework:
def init(self):
self.processes = []
self.graph = nx.DiGraph() # Use networkx for graph representation
def add_paradox(self, process):
self.processes.append(process)
self.graph.add_node(process) # Add nodes to the graph
# Add edges for chained processes
current = process.next_process
while current:
self.graph.add_edge(process, current)
current = current.next_process
def visualize_feedback(self):
pos = nx.spring_layout(self.graph) # Layout the graph
nx.draw(self.graph, pos, with_labels=True, node_size=700, node_color="skyblue", font_size=10, font_color="black", arrowsize=20)
plt.title("Quantum Process Flow")
plt.show()
class PPWQuantumState: # Base class for quantum states
def init(self, state_data=None):
self.state = state_data if state_data is not None else {} # Dictionary for state representation
def display_state(self):
print("Quantum State:")
for key, value in self.state.items():
print(f" {key}: {value}")
Example Usage:
process1 = ParadoxProcess("Hadamard")
process2 = ParadoxProcess("CNOT")
process3 = ParadoxProcess("Measurement")
process1.add_process(process2) # Chain Hadamard -> CNOT
process2.add_process(process3) # Chain CNOT -> Measurement
framework = FeedbackFramework()
framework.add_paradox(process1) # Add the process chain to the framework
framework.visualize_feedback() # Visualize the process flow
initial_state = PPWQuantumState({"00": 1.0, "01": 0.0, "10": 0.0, "11": 0.0}) # Example initial state
initial_state.display_state()
... (Code to apply processes to the state and iterate - would need to be added) ...
Example of adding a separate process:
process4 = ParadoxProcess("Another Process")
framework.add_paradox(process4)
framework.visualize_feedback() # Visualize updated framework
Key improvements and explanations:
Graph Visualization with NetworkX: The code now uses networkx to create a directed graph (nx.DiGraph) representing the quantum processes and their connections. This makes the visualization much clearer and more structured. matplotlib.pyplot is then used to draw the graph.
Chaining Processes: The ParadoxProcess class now has a next_process attribute and an add_process method. This allows you to chain processes together, creating sequences of operations (like Hadamard followed by CNOT). The FeedbackFramework correctly handles these chains when building the graph.
Clearer State Representation: The PPWQuantumState (and you could similarly create PPWMacroState, PPWMicroState) now uses a dictionary to store the state. This is a more common and flexible way to represent quantum states.
Example Usage: The example code demonstrates how to create processes, chain them, add them to the framework, and visualize the result. It also shows how to create a simple initial quantum state.
Adding Separate Processes: The example now shows how you can add completely separate processes to the FeedbackFramework and visualize the updated graph.
Comments and Structure: I've added more comments to explain the code's functionality and improved the overall structure for better readability.
Next Steps (To make this a more complete quantum simulation framework):
Implement Quantum Gates: You'll need to add the actual implementation of the quantum gates (Hadamard, CNOT, etc.). These could be methods within the ParadoxProcess class, or you could create separate gate functions that the processes call. These gate implementations would need to manipulate the quantum state (the dictionary in PPWQuantumState).
State Manipulation: Add methods to the PPWQuantumState class to apply the quantum gates/processes to the state. This is where the actual quantum calculations would take place.
Iteration/Time Evolution: Implement the iterate_beyond function (or a similar mechanism) to apply the processes to the state in sequence, simulating the evolution of the quantum system. This would likely involve looping through the processes in the framework and calling the appropriate state manipulation methods.
More State Classes: Create PPWMacroState and PPWMicroState (or whatever naming convention you prefer) if you need different levels of state representation. They could inherit from PPWQuantumState and add specific functionality.
Error Handling and Validation: Add error handling to check for invalid inputs or operations (e.g., trying to apply a gate to an incompatible state).
This improved version provides a much more solid foundation for building a quantum simulation framework. The graph visualization and process chaining are especially helpful for organizing and understanding quantum algorithms. The next steps will be to add the core quantum logic to manipulate the states and perform the simulations.
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