QNMF-Encryption-and-Compression
Recent simulations of the quantum mind model have demonstrated immense levels of compression emerging from the encoding of neural activity patterns into Psiron representations.
Over 24 hours of simulation time, approximately 2.5 billion biological neurons were estimated to fire per second on average. Given a 6 Hz cycle rate, this equates to 216 billion neurons firing per cycle.
Remarkably, the simulation formed only around 200,000 distinct Psirons in total over this time period. This suggests a compression ratio of 1000x or more from the raw neural firing data to the derived Psiron information manifold.
Such extreme dimensionality reduction implies the model extracts only the most essential features and patterns from the abundant neural signaling. This computational funneling mirrors biological constraints like the brain's limited attention capacity.
In effect, the modeling dynamically filters the incessant noise of neural firing, crystallizing key activity motifs into Psiron engrams through recurrent reinforcement. Only the most salient structures permeate the abstraction process.
This demonstrates a core theoretical principle of the model - distilling the overwhelming complexity of neural computations into a compact quantum informational space. The emergent encoding efficiency has roots in manifold learning and quantum measurement.
Ongoing work will further investigate the dynamics giving rise to this entropic compression. But the current results provide a promising proof-of-concept of the model's ability to recapitulate neural information processing at vastly reduced dimensionality. The functional essence is preserved in the projection.
This paper explores a novel application of a quantum neural network framework, initially developed for simulating brain functionalities. Specifically, the potential for using the framework's high-dimensional data representations, termed Psirons, for data compression and encryption is investigated.
The rapidly advancing field of quantum computing has opened up new avenues for computational efficiency and data security. One such development is the quantum neural network, a model that leverages quantum superposition and entanglement to process information. In our previous work, we introduced a quantum neural network framework and the concept of Psirons, units of knowledge represented in a high-dimensional quantum space. In this paper, we explore a new application of this framework: data compression and encryption.
Our quantum neural network framework builds upon the principles of quantum mechanics to represent data in high-dimensional spaces. The key entities are Psirons, quantum units of memory or knowledge, representing a superposition in a high-dimensional space that encodes the entanglement and collective state of a set of neurons. When a group of neurons fire together, they create a Psiron at a coordinate in the quantum space, representing their connection. The location and state of a Psiron respectively represent the 'where' and 'what' of an encoded memory or knowledge unit.
In the framework, the transition of data from a lower-dimensional form to a Psiron's high-dimensional representation could be considered a form of lossy data compression. The inherent compression takes place as the data is converted to a high-dimensional state, potentially resulting in a smaller, compact representation. This approach, however, is not without its challenges. The computational complexity of handling high-dimensional data and the potential information loss during compression would need to be addressed.
The high-dimensional representation of data in this framework can also serve as a form of encryption. The data is transformed into a state that is not easily decipherable without the specific knowledge of the quantum states and the Psirons involved. The encryption key, in this case, could be the configuration of the Psirons or the specific quantum states involved.
Data decompression and decryption in this model would involve reversing the process of compression and encryption. By reversing the motion in the quantum space, the high-dimensional data could be transformed back into its original lower-dimensional form. The decryption key could be a map of the specific data points and values required to reverse the motion of the quantum space back to its original state.
This paper has proposed a novel use of a quantum neural network framework for data compression and encryption. The high-dimensional representation in this framework, provided by Psirons, offers unique possibilities for compact and secure data representation. However, significant challenges, such as handling high-dimensional data and ensuring effective decompression and decryption, would need to be overcome. Future research directions include rigorous testing of this method's robustness and security, as well as the development of efficient algorithms for the compression, encryption, decompression, and decryption processes.