QF-LCA dataset: Quantum Field Lens Coding Algorithm for system state simulation and strong predictions
Notice bibliographique
Résumé
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 P ≥ 1 / 3 into P ≥ 2 / 3 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 κ , 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 P ’s map for a classical/quantum prediction where the P ’s of states and the user's P ’s correlate in their value difference, Δ P . 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 P values, and so on.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».