Iceberg Model

The Iceberg Model presents an innovative two-layer computational architecture aimed at addressing the complexities of quantum mechanics and relativity. This model facilitates the emergence of spacetime, gravity, and expansion through a fixed-compute substrate, complemented by an append-only ledger that encapsulates the state of entanglement as a shared contract. Measurement functions as a settlement, and the universal wave function is stored in a factorized manner.

This repository serves as a comprehensive resource for researchers and enthusiasts interested in exploring these theoretical constructs. The foundational paper outlining the model's principles can be accessed here (also available as a PDF and through the LaTeX source). Future iterations and related papers will continuously expand on the model’s development.

The repository includes a versioned framework to track the evolution of the model, where the details can be found in model/card.md. This document serves as the authoritative source for understanding the different layers of the model, including:

• Postulates of the architecture (model/architecture.md)
• Mechanisms and the mapping from row to lab (model/implementation.md)
• Configuration options for fine-tuning the model (model/configuration.md)

Running Examinations

To assess the model's validity and performance, exams can be executed via the provided command:

pip install -r requirements.txt
python labs/run_exams.py all --record solaris-1.1.0 --expect-red GR-34,GR-35,GR-36

The expected outcome includes 150 rows - with 147 passing and exactly 3 marked as red, which represent pre-registered findings documenting specific complexities within the theory. The experiment's integrity is maintained by defining row parameters in tests/definitions/, and results are systematically recorded in tests/results/.

Quantum Engine Swapping

The architecture allows for easy swapping of the quantum engine. The amplitude arithmetic can be modified by accessing the small port within labs/engine/. Various backends can be utilized for the quantum battery tests, ensuring consistent output across different configurations:

python labs/qm_battery.py                    # Default: standard library
pip install -r requirements-engines.txt      # Then:
python labs/qm_battery.py --engine numpy
python labs/qm_battery.py --engine qutip
python labs/qm_battery.py --engine qiskit    # Aer statevector

This design supports hash-fed deterministic draws, guaranteeing that output remains identical across executions. Any differences encountered during verification enhance the utility of this repository, as they contribute to further understanding and development of the model.

For more information, please reach out to the author, Marijus Masteika, an independent researcher dedicated to advancing knowledge in this domain. Connect with them through their LinkedIn or via email at masteris@gmail.com.

Engagement in discussions regarding the repository's issues is welcomed, particularly those that highlight behaviors not reproducible by the model. See the CONTRIBUTING.md document for guidance.