Qubit Python-based quantum computing simulation tool, developed as an advanced module for testing quantum neurons in simulated environments. Initially designed as a key component of the QELM system, but works as a standalone project.
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Key Features
Realistic Quantum Circuit Simulation
Leverages Qiskit and AerSimulator to construct and execute realistic quantum circuits across multiple simulation methods (statevector, density_matrix, matrix_product_state, etc.).
Quantum Neurons
Create and manage virtual "neurons" built from configurable layers of quantum operations (RY, RX, H gates). Each neuron simulates complex entanglement and superposition behaviors across dozens of qubits.
Layered Architecture
Every neuron features a customizable 3-layer design:
Layer 1: RY rotations for initialization
Layer 2: Hadamard gates for superposition
Layer 3: RX rotations for final transformation
Integrated Grover’s Search
Apply Grover's algorithm across all neurons or to individual ones for rapid quantum searching and oracle testing.
GUI Interface
Includes a fully interactive Tkinter GUI:
Visual control over neuron creation, simulation settings, Grover search, and deletions
Real-time logs and output via a clean and styled GUI
Optimized layout for easy operation and live monitoring
Logging and Debugging Tools
Built-in error tracking and verbose output for all major actions, making Qubit ideal for research and development use.
Use Cases
Quantum AI simulations (QELM/QML integration)
Educational tools for understanding qubit behavior
Experimental platform for quantum algorithms
Simulating neural-like networks using quantum gates
Oracle testing and state searching via Grover’s algorithm
Technical Specs
Built with Python 3.11
Utilizes Qiskit, NumPy, and Tkinter
Capable of simulating up to 64-qubit circuits (hardware dependent)
Modular and extensible – integrate with larger projects easily
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