A Double-field Computation Model to Simulate Physical Systems

Abstract

A double-field computation (DFC) model is proposed to simulate systems on any scale. A single field to DF transformation model is proposed to simulate sampled particles from an external system to an internal system as a heat engine. The DFC measures a lens distance between two thermal events. This distance-based lens coding between events, provides an observer the information needed to detect and predict system phase transitions (PTs). This model simulates the doubling of state transition (ST) probabilities by combining a quantum circuit with a spin Hamiltonian model as the internal system. The lens coding technique results in reducing the uncertainty of the particle energy states by doubling their probability outcome via particle entanglement between both systems. For example, slow particles are sampled from the external system where their speed is reduced to a Bose-Einstein condensate (BEC), as opposed to classical STs occurring within the high-energy range. The DFC simulates a carbon nanotube-lens contact building quantum bit (qubit) registers as ionic traps (of BEC particles) to record particle states. The recorded data shows which portions of the system are occupied by the same particles that are not participating in a PT. Recording this information by qubit registers is valuable when it concerns heat engines. For instance, energy paths for a PT can be created or rerouted by focusing or defocusing the probability distribution of states via lenses in the DFC model to reach an isothermal temperature in the system, ΔT = 0. A thermal event from e.g., a combustion event can be observed with maximum efficiency as particles selectively by the DF lens code burn out and flow into the engine.

Keywords: Double-field computation, Transition probability, Field lens distance, Field lens coding, Lens coding algorithm, Entropy

JEL Classification: C00, C02, C15, C18, C25, C32, C6, C73, C8, C9