Sub Quantum Lab and QBCPU Lab together form a unified research initiative exploring the foundations of physical reality and the future of computation.
Sub Quantum Lab focuses on hypothesis-driven investigation of field interactions through AI-assisted modeling and ultra-sensitive measurement systems, establishing experimentally grounded constraints and validated methods.
QBCPU Lab builds on these insights to design next-generation computational architectures, integrating quantum-informed principles, artificial intelligence, and scalable processing frameworks.
Together, the labs bridge physical laws and computational systems, advancing measurement, modeling, and the architecture of intelligence.
Exploring below 10⁻⁵⁰ with research in foundational physics using AI-enabled modeling and ultra-sensitive measurement systems.
Field Particle (FiPa) Research
Sub Quantum Lab investigates whether physical field interactions can be represented by effective beyond nano structural elements, known as Field Particles (FiPa).
The research evaluates whether such representations yield measurable deviations or constraints relative to conventional continuous-field models.
Our approach integrates theoretical modeling, AI-assisted simulation, ultra-sensitive measurement systems to test these hypotheses under controlled conditions.
Magnetic Field Particle (MFiPa) Research
Sub Quantum Lab investigates whether magnetic field interactions can be represented through effective beyond nano structural elements, referred to as Magnetic Field Particles (MFiPa).
Quantum Bit Central Processing Unit Lab to build the architecture of Intelligence. next-generation computational architectures at the intersection of quantum systems, information processing, and artificial intelligence.
Speed of Light Processing
Designing physics-informed computing frameworks that integrate quantum-inspired structures, AI-driven processing, and scalable system design.
Full-System Temporal Emulator
Exploring the feasibility of large-scale computational frameworks capable of reconstructing past system states and simulating future evolution under physically constrained models.
The research examines how AI, simulation, and physics-based computation can be integrated to approach comprehensive temporal modeling of complex systems.
Full Universe History and Future Dynamic Processing Emulator.