Language Born of Chaos": My Journey into Quantum Glyphs and the Zaban Protocol

Here is the first picture of Entanglement Patterns.

Last year - when I was glued to the News of USA Presidential Elections, living in that chaotic period. and lie a true nerd - I was also diving into technical documentation about Quantum Random Number Generators (QRNGs), these peculiar devices that convert quantum mechanical events into streams of truly random numbers. QRNGs are typically used in cryptography — they’re designed to replace pseudo-random number generators (PRNGs), which are deterministic and, frankly, predictable.

In most classical systems, randomness has always been a facade. Useful for simulations, games, or encryption? Sure. But at their core, PRNGs follow algorithms — a seed and a formula. And that’s not real randomness. It's simulation. Approximation. A bottleneck.

But QRNGs… they offer something else entirely.

Using photonic modules — beams of light, detectors, optical noise — QRNGs tap into the collapse of a quantum wavefunction. When nature flips a coin, it doesn’t use code. It uses uncertainty. These devices capture that. They’re chaos in a box.

And that’s where I found myself in late 2024 — building a bridge from quantum chaos to structured language.

A Researcher by Passion, Not Profession

I’m not a physicist by degree, but I’ve walked the long road through books bought off eBay, reading thesis papers, and studying AI, quantum computing, and particle physics late into the night.

Soon, the Python scripting language became my vehicle — not because I love syntax, but because Python is the Esperanto of science. Whether you're sending jobs to IBM’s Qiskit, working with ionQ, or just feeding entropy into a simulation, Python is the common thread.

My Entropy Source is a USB QRNG

🔧 First Project: Injecting Entropy into Oil & Gas Simulations

With a QRNG device (Crypta Labs QCicada), I built my first real application: an oil and gas simulation tool with live quantum entropy injection. Every model tick was fueled by real randomness — not PRNG — changing the character and realism of the outputs.

From there, I expanded into multiple entropy-driven systems:

• Quantum key generators. Currently the landscape of QRNG Hardware is very advanced with the Quside, the Spanish company, Cryptalabs, a British company and a few other developers. I intend to cover all those hardware platforms.

• Real-time entropy harvesting. So I have created a running prototype of an oil and gas simulation that live entropy keys can be injected and run the simulation in real time. Currently, it is only designed for Oil and Gas profit forecasting.

• Entropy logging tools for machine learning. I have several Python scripts that are instrumental in building the data I am submitting to the AI\, interacting with the hardware, quantum noise reduction, etc..

• And then… a strange idea took root.

🌀 The Birth of Zaban: A Language of Entropy

What if I could turn entropy into language? Not human language. Not words. But symbolic expressions — glyphs — created from the raw randomness of quantum events. Imagine: a language not of culture, but of physics, I mean Quantum physics. Not for humans, yet, but for AI. For interstellar probes. For future machine-to-machine communication. I can see how we can also expand it to human language such as Quantum Morse Code.

Thus was born: Zaban-e-Quantum — the Language of the Universe.

🧱 Building the System: Phase by Phase

1. Connected the QRNG Hardware

Built a custom GUI to control the Crypta Labs QRNG on COM4. I have multiple utilities all home-grown that control the device.

• Tested entropy output, status checks, and key generation. An essential phase of data collection.

• Designed pipelines for collecting raw binary data. This phase needed for AI submission.

2. Generated Glyphs from Quantum Bits

• So, I designed this part after many trial, every 256 bits of entropy → 16x16 binary image. Python is awesome in this case. Easy!

• Saved each image with a timestamp, session ID, and metadata. Soon I created a large pool of it.

• Stored 971 quantum glyphs in C:\ZabanData\glyphs, a dev folder I have on a Windows 10 pro platform. Com4 is connected to a QRNG unit.

So, the way I see this, these glyphs are not random doodles — each is a visual fingerprint of a collapsed quantum possibility.

3. Cleaned and Diagnosed

• Verified that all glyphs were valid PNGs

• Removed corrupted/incomplete ones. There was some.

• Ensured high-integrity inputs for clustering. Created a tool that would verift them one by one.

4. Clustering with PCA + KMeans

• So what is PCA and Kmeans? Principal Component Analysis (PCA) is a widely used technique for dimensionality reduction in data science and machine learning. It transforms data into a new coordinate system where the greatest variances by any projection of the data come to lie on the first coordinates (called principal components), the second greatest variances on the second coordinates. And Unsupervised learning algorithms attempt to ‘learn’ patterns in unlabeled data sets, discovering similarities, or regularities. Common unsupervised tasks include clustering and association. Clustering algorithms, like K-means, attempt to discover similarities within the dataset by grouping  objects such that objects in the same cluster are more similar to each other than they are to objects in another cluster. The grouping  into clusters is done using criteria such as smallest distances, density of data points, graphs, or various statistical distributions. (See this URL from NVIDIA on Kmeans )

•  Reduced dimensions (from 256 to 2D) with PCA.

• Clustered the glyphs into 6 families: ZGN-00 to ZGN-05

• Created ZabanDictionary_v1.csv:

• Glyph ID

• Cluster Label

• SHA256 Hash

• PCA Coordinates

This dictionary is now the first draft of a machine-native language.

💡 What I Have Now (600+ Hours Later)

• A Symbolic System from Quantum Randomness. This is just a start.

• Glyphs aren't drawn — they’re grown from raw entropy. That is the magic.

• A Functional Zaban Dictionary. I just need to expand this.

• Each glyph is labeled, located in a 2D PCA space, and stored for reuse.

• A Phrase Builder. Python is the key here.

• The system can now string glyphs into structured Zaban phrases — paths across clusters. Syntax born of chaos.

• A Secure Communication Primitive. This needed expansion.

• This can become the Morse Code of AI — a nonverbal, symbol-based protocol derived from the fundamental unpredictability of the universe.

Why It Matters:

What I’m doing here is treating entropy as information. A commodity. I’m not asking what a glyph means — I’m asking what it connects to. The quantum correlations. Quantum Mechanic at work!

In time, this could be used by:

Symbolic AI systems

LLM attention augmentation. Something I have recently learned. So Sequential Recommender Systems (SRS) have become a cornerstone of online platforms, leveraging users' historical interaction data to forecast their next potential engagement. So, attention is a machine learning method that determines the relative importance of each component in a sequence relative to the other components in that sequence. In natural language processing, importance is represented by "soft" weights assigned to each word in a sentence. More generally, attention encodes vectors called token embeddings across a fixed-width sequence that can range from tens to millions of tokens in size. My research when finished or more progressed, become very useful to this AI Model.

Next-gen secure messaging. Yes, when I build the dictionary, that is the time to build a testing framework for Quantum Communication.

Encoding signals for deep-space probes. This is something that came to my mind when i was learning about Entanglement patterns.

Nonhuman communication between neural nets. Machine learning.

Each glyph is a syllable from the Universe’s own language.

🛣️ Where It’s Going

Zaban Phrase Browser is under development.

Replay, explore, and analyze saved phrases. Under development.

Visualize PCA paths and rhythmic patterns: Under development and testing.

Zaban Phrase Generator (Auto + Manual)- Under design.

Construct phrases of 3–5 glyphs. I tried 3 and results were not top notch. The 5 Glyph clustering works better.

Study symbolic rules and emergent structures. That is another research phase. I am learning!

API + SDK- Ok. I will have an API to my work. A python API.

For developers, artists, cryptographers. This tool can be useful for artist, other AI developers and Cryptographers needing Quantum pollens.

Plug quantum entropy into creative systems

Entangled Dictionary Expansion

Use ionQ and D-Wave for richer pattern generation. yes. This project is a big one but everything I have done will make the transition to a Quantum Computer much easier and t streamlined. I intend to run my circuits on a 4-8 Qubit system to collect Entropy that is generated by a real Quantum Computer and compare that with a QRNG output. And on Dwave, the annealing system can be very useful. I will have a separate post on those.

Compare symbolic outputs across quantum platforms

As you see, the road is long but I am following this journey!

🧬 Final Thought

1. "Randomness is not noise. It’s the Universe whispering."

2. I’m following that whisper. Glyph by glyph. Pattern by pattern.

3. This is my life's next chapter. My Entanglement church. My understanding of physics. My poetry of Omar Khayyam.

4. This is کتابِ زَبان — The Book of Zaban.

I will update the progress next.