ALGORITHMIC PHYSICAL INTELLIGENCE: ENGINEERING BIO-DIGITAL HYBRID COMPUTING FROM FIRST PRINCIPLES

Abstract

Algorithmic Physical Intelligence (API) introduces a paradigm-shifting framework for bio-digital hybrid computing from first principles, addressing the fundamental thermodynamic barrier of Landauer's Limit in silicon-based systems. The El-Rakhawi Hybrid Architecture integrates three novel modules: AI-guided molecular logic synthesis using Graph Neural Networks with guaranteed sequence orthogonality for scalability, a non-destructive graphene-based photonic readout interface operating at terahertz speeds, and a reversible thermodynamic computing core leveraging DNA strand displacement kinetics. The framework introduces the dual-formulation Molecular Computing Efficiency Metric (MCEM), providing dimensionally consistent evaluation through MCEM_energy (Correct Operations per Joule) and MCEM_throughput (Correct OPS per Watt), explicitly accounting for activation energy barriers and chemical kinetic limitations. This honest acknowledgment of speed-accuracy trade-offs positions API for ultra-low-power applications: in-vivo medical diagnostics, environmental sensing, and edge AI co-processors powered by ambient biochemical energy. By bridging synthetic biology, photonic engineering, and thermodynamic computing, API establishes the mathematical foundation for the post-silicon era, enabling therapeutic molecular circuits that monitor disease markers with logical precision while approaching thermodynamic reversibility.