Quantum Synapse Project Overview

This project, led by Michał Zazuniuk (swiatlowemnie333), maps biological neural network synchronization mechanisms to quantum bits. Key achievements on IBM Quantum Marrakesh include:

• 95.11% fidelity for 6-qubit GHZ entanglement state
• 14.10% neural synchronization rate

The project provides new insights for practical quantum neural network implementations. Source: GitHub repo (released June 2026).

Project Background & Core Question

Author Michał Zazuniuk has 14 years of industrial automation experience and self-studied quantum computing over the past year. The project explores a core question: Can the synchronization of neurons in biological brains (a basis for collective intelligence) be reproduced using quantum bits?

GHZ Entanglement: Foundation of Quantum Synchronization

The project first implemented GHZ states on IBM Quantum Marrakesh (156 qubits):

• 6-qubit GHZ state: 95.11% fidelity
• 16-qubit extension:94.2% fidelity -32-qubit extension:74.1% fidelity

These results were repeated 7 times with high reproducibility. GHZ states exhibit non-locality—measuring one qubit instantly determines all others' states.

Quantum Synapse Model & Neural Sync Results

Building on GHZ entanglement, the team created a quantum synapse model: -6 quantum bits simulating neurons -15 quantum gates as synaptic connections

The model achieved a 14.10% neural synchronization rate. In biology, neural synchronization is key for information integration and consciousness.

Experimental Methods

The project used innovative techniques:

1. Parallel Wave Interference: Parallel quantum gate operations to create state interference.
2. Synesthetic Phase Correction: Fine-tuning quantum phases inspired by synesthesia (cross-sensory perception).
3. IBM Quantum Platform: Experiments on Marrakesh (156 qubits). Records are in results/ibm_marrakesh_june_2026/ with job IDs like d88n9lgp0eas73dnm190 (GHZ-6) and d88npkqs46sc73f9mt00 (sync experiment).

Project Significance

The project has multiple implications:

• Quantum-Biology Cross: Applying neuroscience insights to quantum systems.
• Practical Entanglement: High-fidelity GHZ states support quantum communication/cryptography.
• Open Source: All code/results are public, enabling reproducibility and extension.
• Democratized Education: The author’s journey (industrial to quantum in 1 year) shows accessible quantum learning.

Final Conclusion

Quantum Synapse goes beyond algorithm acceleration—it explores the intersection of quantum physics and biological neural behavior. It’s a bold attempt at bio-inspired quantum neural networks, merging life science and quantum technology to uncover new insights.