QNFO's ultimate aim is to advance scientific understanding and catalyze positive, systemic global change for the collective benefit of all.

QNFO is a scientific research incubator -- the research identity of Empowering Change, a U.S. 501(c)(3) non-profit founded and directed by Rowan Brad Quni-Gudzinas. We investigate the fundamentals of reality at the intersection of physics, information theory, philosophy of science, and artificial intelligence, leveraging AI-accelerated exploration to drive discovery.

All research is conducted for public good, not private profit. Everything we produce is governed by the QNFO Content License Agreement -- non-commercial use only, attribution required. Our Code of Conduct defines the values that govern all QNFO spaces.

Our mission integrates deep scientific inquiry with a commitment to tangible human benefit:

1. Advance Foundational Science: Rigorously investigate the informational underpinnings of the universe -- developing and testing novel, consistent theoretical frameworks, seeking deeper coherence in physical law, causality, and the nature of spacetime. Findings are disseminated openly.

2. Pioneer AI in Scientific Discovery: Employ state-of-the-art AI as a collaborative partner in research, enhancing our ability to synthesize knowledge, formulate hypotheses, design conceptual experiments, and identify promising avenues for innovation.

3. Develop Ethical & Open Technologies: Translate scientific insights into practical tools and platforms, guided strictly by open-source principles and a robust commitment to ethical AI. Technologies that empower, connect, and are free from biases conflicting with collective well-being.

4. Catalyze Global Benefit: Direct research towards addressing concrete systemic inequities and global challenges, contributing directly to the collective benefit of all humanity by fostering scientific literacy, democratizing access to knowledge, and developing transformative capabilities.

QNFO's work spans multiple domains. Each initiative is a distinct line of inquiry, organized as an independent repository with its own documentation, tests, and publications:

QNFO employs a distinct research methodology characterized by:

• Information as Foundational: Exploring the hypothesis that information plays a fundamental ontological role, seeking the physical laws governing its structure and dynamics.
• AI-Accelerated Knowledge Synthesis: Utilizing advanced AI (especially LLMs) as collaborative tools for rapid knowledge synthesis, conceptual exploration, and accelerated discovery cycles.
• Radical Interdisciplinarity: Actively integrating methods and insights from physics, computer science, information theory, philosophy of science, AI research, and relevant social sciences.
• Data-Informed Theoretical Modeling: Applying principles from data science, predictive modeling, and algorithm design to analyze theoretical frameworks and guide the development of testable hypotheses.
• Commitment to Openness and Ethics: Dedication to open-source development and ethical AI is fundamental. Transparency, reproducibility, and shared access are crucial for scientific integrity.
• Git-Inspired Structure: Intentionally organizing qnfo.org like a Git repository to make the evolution of ideas transparent, treat different inquiries like branches, and document progress like commits.

Our work is guided by principles articulated in the Code of Conduct:

1. Collective Benefit -- All work must serve humanity broadly, not narrow interests
2. Open Science -- Transparency, reproducibility, shared access to knowledge
3. Ethical Integrity -- No work that harms, discriminates, exploits, or concentrates power
4. Intellectual Rigor -- Evidence, falsifiability, and honest labeling of speculation
5. Interdisciplinary Respect -- Insights from all domains are valued
6. Constructive Collaboration -- Good faith in all interactions. Critique ideas, not people.
7. Non-Commercial Foundation -- Research for public good, not private profit

• Zero logical errors at depth 7 -- ternary Bruhat-Tits tree quantum encoding, validated at physical error rates up to 40%. DOI: 10.5281/zenodo.20134944
• 48x error reduction -- at zero additional qubit cost via $q$-ary scatter. DOI: 10.5281/zenodo.20208437
• Glass-box AI outperforms transformers -- 6.6x on hierarchical classification with 100% verification detection. DOI: 10.5281/zenodo.20287742
• The Tree Is Real -- Scale-free network evidence from 673-node knowledge graph. DOI: 10.5281/zenodo.20325850
• 40-atom neutral atom hardware specification -- within demonstrated experimental capabilities (Harvard, Caltech, PASQAL)

QNFO is a solo deep-tech research program founded and directed by Rowan Brad Quni-Gudzinas (ORCID: 0009-0002-4317-5604). Rowan operates at the intersection of physics, information science, data science, and AI, with a career spanning:

• National-scale data initiatives: Managed the AARP Livability Index and co-directed the $10M US DOT National Household Travel Survey (NHTS)
• Patented quantum computing technology: Holds foundational US patents
• AI & data science leadership: Led predictive analytics deployments at Deloitte and Publicis
• Published thought leadership: Authored books and papers exploring the nexus of physics, philosophy, AI, and information

The non-profit structure (Empowering Change 501c3) ensures our motivation remains scientific advancement and societal benefit.

All papers are open-access on Zenodo with registered DOIs. See the full publication catalog (35+ publications, filterable by domain). Community archive: zenodo.org/communities/qwav/ (145+ records).

Representative publications:

• Computational Validation of Ultrametric Error Confinement (2026-05-12)
• Ultrametric Quantum Computing Foundations (2026-05-15)
• Symmetric Extension -- Ternary Tree Architecture (2026-05-16)
• Q-PNA Research Specification v2.0 (2026-05-19)
• Convergence, Consilience, and the Hierarchical Architecture of Reality (2026-05-20)
• The Tree Is Real (2026-05-21)

• Home: qnfo.org
• QWAV Technical Hub: qnfo.github.io/QWAV
• QWAV Marquee: qwav.tech
• License: QNFO Content License Agreement v1.1
• Code of Conduct: CODE_OF_CONDUCT.md
• Contact: rowan.quni@qnfo.org

QNFO -- advancing scientific understanding for the collective benefit of all. Everything open. Everything accountable. Everything for the good.