Database of quantum circuits encoding computational processes on biological substrates
- Develop database of quantum circuits encoding biological processes and computations running on biological substrates
- Develop equivariant neural network models obtimal for classes of circuits with certain symmetries
- Give overfit model as data compression for all circuits
The goal of this repository is to give a compression (for computational efficientcy) of all quantum circuits running on biological substrates, for example, neurolical processes, digestive processes, photosynthetic processes, etc. Once this is accomplished we want to train several machine learning models which are optimized for the symmetries of these processes in order to (1) over fit some of the models for data compression, and (2) have models predict and compose new quantum circuits that can minimize and entropy and harvest neg-entropy (i.e. reverse entropy) in biological processes at the quantum level. We do not wish to simulate quantum processes, we need to run the computations accurately in order to use the time-reversibility or quantum physics to encode circuits into biological substrates that can reverse entropy. We should builld, using surface code and string theoretic circuits to build molecular sized time crystals and circuits that reverse entropy.