From Holographic Storage to Quantum Predictive Coding AI — Technical Specification 6

Abstract

A practical engineering roadmap that transforms an 800‑particle holographic attractor network into a hetero‑associative, reward‑driven Quantum Predictive Coding (QPC) system. The specification defines data schemas, default parameters, safety protocols, and a four‑sprint implementation plan (Semantic Layering, Reward‑Modulated Plasticity, Temporal Dynamics, Quantum Logic) together with testing, telemetry, and scaling strategies.

Summary

This technical specification documents a step‑by‑step plan to evolve a particle‑based auto‑associative memory into an inference engine capable of learning, predicting, and computing via phase dynamics. It details the required data model extensions (region identity, directed forward/feedback matrices, delay buffers, STDP timestamps), explicit parameter defaults to ensure stable training, and a Teacher Mode API for interactive reward‑modulated learning. Each sprint includes concrete implementation guides, acceptance criteria, and ready patch sketches so developers and automated agents can implement and validate features incrementally.

The roadmap also prescribes deterministic CI practices (seedable RNG, mocked timing), automated safety protocols (plasticity circuit breakers and rollback rules), and a clear scaling path (sparse adjacency, spatial indexing, WebWorker offload) to reach high‑fidelity simulations at N > 2000 particles. Included are test suites for region enforcement, snapshot integrity, sequence recall, and phase‑based logic gates, plus telemetry metrics and an agent reporting template to standardize progress reports.

Suggested keywords

Quantum Predictive Coding; Holographic Memory; Attractor Network; Hetero‑Associative Learning; STDP; Phase Interference; Reinforcement Learning; Simulation Roadmap; Particle Physics Engine; Semantic Layering

Suggested citation blurb

1. Rawson, Roadmap: From Holographic Storage to Quantum Predictive Coding AI — Technical Specification 6.0, Zenodo (2025).