QF-LCA dataset: Quantum Field Lens Coding Algorithm for system state simulation and strong predictions

Quantum field lens coding algorithm (QF-LCA) dataset is useful for simulating systems and predict system events with high probability. This is achieved by computing QF lens distance-based variables associated to event probabilities from the dataset produced by field lenses that encode system states on a quantum level. The probability of a state transition (ST), doubles in prediction values at the decoding step, e.g., ST probabilities of $$ into $$ based on a single field (SF) transform into a quantum double-field (QDF). This transformation doubles the ST probability space via the field's scalar $$ , in a published QDF method article. A QDF, as a double-field computation (DFC) model, simulates thermodynamic systems in predicting events by producing datasets from a QF-LCA, using laser cooling methods relative to high energy systems. The dataset can be used to, e.g., train quantum algorithms via quantum artificial intelligence (QAI) for a QF-LCA user. The user then decides after the algorithm predicts and suggests an efficient energy path for the system to choose. For example, an N-particle system simulated as a heat engine, by QAI classifier(s), predicts and reroutes particle energy paths on a logarithmic input-output scale. To determine system's energy change, the QDF lens code (DFC) measures entanglement entropy (EE). The algorithm's classification of EE values distinguishes entangled states in the system. The QF-LCA program employs the dataset to achieve an automated prediction and classification method, rather than dataset's manual use and analysis by the user. As the system evolves in its distribution of states, those particles not reaching a desired energy state (a target state or TS), i.e., the probability of observing a ground or excited state (GS or ES) outcome at the decoding step, can be rerouted by the heat engine to satisfy a TS outcome. This establishes a GS or ES energy profile to access and classify states by a classifier. The data points (qubits) in this profile are inverse distance-based and labelled for a specific class. After learning the profile, the classifier decodes and predicts the next system state. A QDF AI game "Alice & Bob's Quantum Doubles," is developed to validate the dataset as the $$ 's map for a classical/quantum prediction where the $$ 's of states and the user's $$ 's correlate in their value difference, $$ . Dataset validation results are mapped to an intelligent decision simulator (IDS) as a QAI map. This maximizes system efficiency on a TS by EE of energy states (distributed in the system). Future additions to the dataset from the QAI map program can improve quantum algorithms to determine which particles of the system participate in a phase transition after state prediction. QF-LCA applications are in data science, security, forensics, particle physics, etc., such as retrieving or reconstructing information by distinguishing particle states from an evidence sample. Examples are, reconstruct damaged DNA strands of cells to predict a virus's TS, or cancer cell, its spread and growth against healthy cells, identify forged documents from genuine based on QDF's $$ values, and so on.