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Hybrid-manufactured Energy-Landscape Inference and Operation System

Speculative spintronic research exploring topological magnetism for ultra-low energy computation

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HELIOS-3D is a staged research hypothesis investigating whether spintronic, topological, and thermodynamic-computing mechanisms could support future low-energy inference architectures near fundamental energy-efficiency limits.

It is not a fabricated chip, validated hardware design, or claim of demonstrated sub-Landauer computation. It is a public research notebook for tracking claims, blockers, and evidence across plausible and speculative material/fabrication paths.

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Current Reproducible Demo

To verify the compiler pipeline, tests, and documentation build system locally, run the following commands:

# Set up Python virtual environment and dependencies
uv sync

# Run the test suite (verifying compiler logic, claims taxonomy, and simulations)
uv run pytest

# Install Node.js dependencies
pnpm install

# Compile the documentation and interactive site
pnpm build

What these commands prove:

• Compiler logic consistency: The compiler's coordinate translation and mock Inverse Faraday Effect (IFE) function compile correctly and pass topological charge checks ($Q_H = 1$).
• Taxonomy integrity: The claims validation engine runs successfully, verifying that all claims have matching promotion/demotion criteria and references.
• Documentation pipeline correctness: The MDX-based static site builder compiles all pages, charts, and interactive Three.js components without errors.

What they DO NOT prove:

• No physical validation: These commands run mock physics adapters and compile documentation. They do not run real physical systems or validate micromagnetic dynamics on actual hardware.
• No hardware execution: The MuMax3/OOMMF simulation scripts are templates and have not been executed on a physical device.

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Executive Summary

┌─────────────────────────────────────────────────────────────────────────────┐
│                                                                             │
│  Current Baseline     Planar-first, electrically read using established     │
│                       multilayer spintronic stacks                          │
│                                                                             │
│  Hopfion Advantage    Theoretically bypass Skyrmion Hall Effect via         │
│                       zero net topological charge [INFERRED]                │
│                                                                             │
│  Long-Range Target    operation-accounted fJ-scale physical inference       │
│                       approaching thermodynamic efficiency limits           │
│                       [SPECULATIVE]                                         │
│                                                                             │
│  Strongest Candidate  Compensated ferrimagnet/altermagnet transport with    │
│                       spin-pumping readout                                  │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

Sub-Landauer behavior is treated as a long-range research question, not a demonstrated capability.

Thermodynamic & Operational Validation Guardrails

To maintain physical rigor, HELIOS-3D adopts three validation guardrails based on recent literature in information thermodynamics and classical reversible computing:

1. CRUD Accounting Framework: Recent work in information thermodynamics (Iizumi 2026, DOI: 10.1016/j.physa.2026.131801) frames Landauer's erasure bound as the Delete-specific limit of a broader Create/Read/Update/Delete accounting framework. HELIOS-3D uses this as a validation guardrail: any claimed energy advantage must account for the full physical information lifecycle, including reservoir state preparation, update/perturbation, readout/measurement, overwrite/reset behavior, and protocol-dependent dissipation.
2. Reversible Operation Accounting: Proposed classical reversible computation using quantum-coherent spin dynamics in Ge/Si quantum-dot arrays (Loss 2026, arXiv:2607.06219v1) serves as adjacent evidence for post-Landauer physical-computing architectures where storage, transport, and computation may share the same physical state variable. It strengthens the case for explicit operation-level accounting across reversible logic, readout, reset, transport, and error correction.
3. Reservoir Thermodynamics: Recent theory on the thermodynamics of quantum reservoir computing (Ding & Qiu 2026, arXiv:2607.02157v1) links predictive performance to microscopic energetic cost using Holevo-based memory and predictive capacities. HELIOS-3D treats this as a validation framework: physical reservoirs should be evaluated by how much useful predictive information they retain relative to non-predictive historical information, and by the irreversible work required to maintain continuous temporal processing.

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Dual-Core Architecture

Modern silicon scaling faces severe constraints from inelastic scattering and energy-intensive data shuttling. HELIOS-3D explores migrating information carriers away from electrical charge toward topologically protected spin textures.

  Fabrication Pipeline:  DISH → TPP → ALD
  ─────────────────────────────────────────────────────────────────────────────
  DISH  Digital Incoherent Synthesis of Holographic light fields
  TPP   Two-Photon Polymerization
  ALD   Atomic Layer Deposition

Core	Role	Status
Magnetic Convolutional Accelerator (MCA)	Deterministic sensory preprocessor via Compute-in-Memory spintronics	[PROPOSED]
Brownian Reservoir Computing (BRC) Core	Probabilistic decision-maker using noise-driven thermodynamic processing	[PROPOSED]

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Implementation Status

Phase 0.5: Documentation + Validation Scaffolding (Current)

Component	Description	Status
Topological Compiler	Python mapping layer translating semantic embeddings → 3D magnetization tensors	Scaffold
Compiler Tests	Coordinate mapping fidelity + Hopf Index synthesis ($Q_H=1$) via mock IFE transfer function	Passing
PINN Environment	Physics-Informed Neural Network training infrastructure	Configured
Micromagnetic Sim	MuMax3/OOMMF configuration files	Templates

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Getting Started

# Clone the repository
git clone https://github.com/myrqyry/HELIOS-3D.git
cd HELIOS-3D

# Install dependencies
uv sync

# Run topological compiler tests
uv run pytest tests/test_topological_compiler.py

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Repository Structure

Path	Description
src/content/docs/	Astro + MDX documentation source
research_specifications/	Formal physics and architecture modules
simulations/	MuMax3 and OOMMF configuration files
compiler/	Topological Compiler implementation
analysis/	Data validation and spintronic analysis scripts

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Claims Protocol

Every claim in HELIOS-3D is tagged to distinguish established physics from architectural aspirations:

  ┌──────────────┬────────────────────────────────────────────────────────────┐
  │ [DEMONSTRATED] │ Verifiable in peer-reviewed literature                    │
  │ [INFERRED]     │ Plausible extrapolation from established physics          │
  │ [PROPOSED]     │ Architectural integration suggested by HELIOS-3D          │
  │ [SPECULATIVE]  │ Theoretical target or unverified projection               │
  └──────────────┴────────────────────────────────────────────────────────────┘

See the Claims Matrix for claim-by-claim traceability.

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The AI Energy Crisis

The AI energy crisis is real and accelerating:

• Electricity: Data center + AI + crypto demand projected at ~600 TWh in 2026, potentially surpassing 1,000 TWh by 2030 — equivalent to Japan's annual consumption [DEMONSTRATED]
• Water: Global AI demand will account for 4.2–6.6 billion m³ of water withdrawal by 2027 [DEMONSTRATED]
• Embodied Carbon: Semiconductor fabrication accounts for up to 50% of total lifecycle footprint for AI hardware [DEMONSTRATED]

HELIOS-3D investigates whether topological magnetism can let thermal noise assist computation while 3D scaling reduces physical and embodied footprint.

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Documentation · Roadmap · Contributing · Claims Matrix