Innovative Science and Technology Solutions

This page is for someone asking:

“What has this work actually achieved so far, and where are the real opportunities—especially for quantum computing?”

      Over ~5 months of focused work (spread across a year), I’ve built a complete theoretical stack: the Evans Node Dialect / Matrix Node Theory (END/MNT), plus five companion documents (Axioms & Ontology, Math Lexicon, Structural Proofs, Global Validation, END Companion). Together they form a unified, internally consistent framework that ties together quantum behaviour, gravity, and cosmology in a single node-based picture, designed from day one to be readable and attackable by both physicists and AI tools.

From the same framework, I’ve also derived a concrete qubit-control protocol. In standard Lindblad simulations with realistic parameters, it yields a 1.3–1.4× increase in effective T2T_2T2​ over a fixed baseline, using only low-bandwidth modulation of amplitude, phase, and detuning. This is currently simulation-only, but fully specified in Hamiltonian/Lindblad form and implementable on existing AWG/firmware stacks.

All of this is independent IP, held by me (Jordan Ryan Evans): the framework, parameter choices, and the control protocol.

• Short term: internal reproduction of the simulations and theoretical review.
   
• Mid term: targeted hardware tests and potential control-stack integration.
   
• Long term: patentable control methods and, if the gains hold on hardware, multi-billion-scale impact via reduced quantum error rates and lower fault-tolerance overhead.

Over roughly 5 months of focused work (spread across about a year), the following have been built and iterated:

• Evans Node Dialect / Matrix Node Theory (MNT)
  A unified framework where:
   
  • the universe is described as a lattice of interacting “nodes”,
     
  • quantum behaviour, gravity, and cosmology are emergent from the same underlying structure,
     
  • randomness is replaced by deterministic rules at the node level.
     
• Document set (multiple, coherent, cross-linked pieces):
   
  • MNT Axioms & Ontology – what is assumed fundamental, what is derived, how “nodes” and “fields” are defined.
     
  • MNT Math Lexicon – the complete symbolic vocabulary, so humans and AI can parse everything consistently.
     
  • MNT Structural Proofs – stepwise arguments from node-level assumptions to effective field behaviour (conservation, symmetries, etc.).
     
  • MNT Global Validation – one place where the theory is confronted with data across multiple domains.
     
  • MNT END Companion – context, narrative, design decisions, and corrections that came from months of AI+human debugging.
     

These are not half-finished notes. They are full, self-contained documents designed to be read, checked, and attacked by:

• physicists,
   
• AI research tools,
   
• and anyone who wants to audit the logic from first principles to predictions.
   

The framework has already been pushed (via both manual work and AI assistance) against data and concepts in:

• Particle physics:
   
  • mass hierarchies, coupling structures, mixing, and scaling patterns.
     
• Gravity & astrophysics:
   
  • strong-field behaviour, neutron stars, and gravitational-wave implications.
     
• Cosmology:
   
  • expansion history, dark sector interpretation, and large-scale structure.
     
• Quantum foundations:
   
  • measurement, decoherence, and the emergence of time and causality from node dynamics.
     

Important:

This doesn’t mean “everything is solved”.

It means one coherent structure has been built that can be checked across these areas without having to change its basic assumptions every time you change topic.

This is one of the first full physics frameworks that:

• was co-developed with AI (ChatGPT and others) at every stage, not just used as an afterthought,
   
• used AI to:
   
  • rewrite drafts,
     
  • detect internal contradictions,
     
  • stress-test assumptions,
     
  • and generate code-style checks and sanity tests.
     

The human did:

• the conceptual design,
   
• the ontology choices,
   
• the “keep / discard / refine” decisions.
   

The AI did:

• compilation,
   
• cross-checking,
   
• and fast iteration.
   

So the output is a human-driven theory, but one that has already been through multiple AI-level consistency passes.

If you care about money, impact, and real-world tech, this is the part worth paying close attention to.

From the same thinking that built MNT/END, a qubit-control protocol has been derived and documented:

• It modifies:
   
  • drive amplitude,
     
  • phase, and
     
  • detuning
    in a structured, low-bandwidth way on top of a standard driven open-system qubit model.
     
• In Lindblad-type simulations with realistic parameters:
   
  • a baseline effective dephasing time (T_2) of about 50 μs is obtained,
     
  • under the protocol, (T_2) increases to about 70–72 μs.
     

That’s roughly a 1.3–1.4× improvement in simulated (T_2) with the same underlying noise model.

Currently:

• All of this is simulation-only.
  No hardware experiments have been done yet.
   
• The protocol is described in standard Hamiltonian + Lindblad language and is precise enough for a serious lab or company to implement and test.
   

For many platforms:

• dephasing-driven error scales roughly like ( \text{error} \sim t / T_2 ) for short gates and idles,
   
• so a 30–40% increase in (T_2) can mean:
   
  • noticeably lower physical error per gate / idle,
     
  • more room for complex gate sequences,
     
  • lower overhead for reaching a target logical error.
     

In plain language:

If this protocol survives hardware testing with even a conservative fraction of its simulated gain, it directly improves the economics and feasibility of fault-tolerant quantum computing on existing or near-term devices.
 

That is commercial, not just academic.

On the quantum side, the work already includes:

• A technical specification written so that a hardware team can:
   
  • plug the protocol into their simulation stack,
     
  • try it on real devices,
     
  • and compare to their own baselines.
     
• A clear IP framing:
   
  • what is owned,
     
  • what can be evaluated internally,
     
  • what would require licensing or a separate agreement for production use.