QF-LCA Dataset for Quantum Double-field Model, Game and Application

The data on this repository are for the DIB article entitled: "QF-LCA dataset: Quantum field lens coding algorithm for system state simulation and strong predictions" by P. B. Alipour and T. A. Gulliver. The dataset presents an overall preview of the method used for [1, 5] that produce the dataset.

QDF measurement data are acquired from IBMQ and QInspire platforms, and stored as an internal data collection, so to compare it to the data collected from measurement variables manually calculated and presented in the QDF articles [1, 3, 4] as *.pdf, *.pptx, *.txt, *.nb, …, and image files. The QDF system model is simulated to generate an external data collection stored on IBMQ, QInspire, or on this repository, as a QDF dataset. The dataset is examined to validate QDF state correlation and entanglement entropy (EE) relative to uncertainty measures (errors) discussed in the QDF’s method article [1]. The data are examined based on QDF’s four-main variables, defined and discussed in the QDF model article [4]. System energy states were profiled as the weighted statistical data for an intelligent decision simulator (IDS) in ‎[1]. This dataset was proposed for a quantum AI (QAI) method to classify states, and make a strong prediction of the next system state. The IDS uses the dataset to further analyze and classify states based on the expected success probability values 〈P_success〉 ≥ 2/3 (doubling the probability space from at least P ≥ 1/3 to P ≥ 2/3), for a strong system state prediction. Other statistical and probability data are based on classical and QDF computations using simulators like Mathematica and IBMQ, uploaded onto this repository, which contains the QDF circuit simulation and its datasets. The file structure is presented in Fig. 1, e.g., *.cq, *.csv, *.htm, *.ipynb, *.png, *.py, …, of the DIB article, each referring to a statistical methodology of QDF vs. classical states by the QFLCA programs. The file content and the corresponding methodology are summarized in Table 1 of the DIB article.

The QFLCA datasets are further validated by classifying energy states and generate a QAI map to make a strong prediction based on weighted probabilities of quantum vs. classical states in a quantum game called: “Alice & Bob’s Quantum Doubles” written in Python as a QDF game [1, 4]. The QFLCA website documentation and demo files in *.mp4 under the <QFLCC classifiers\site> directory show how to run the game and the QFLCC program.

The manual calculation of the QDF model was conducted via Wolfram Alpha online based on the measurement data compared between ES and GS states as a P indicator generated for measurement samples. Small dataset samples denote:
a. A particle pair’s energy state in a QDF (different GS states or sublevels of a GS, or see Table 2),
b. a particle state in an SF, an ES relative to a GS from (a.) prior to a field transformation, and,
c. the expected transformation of fields (ES ←→GS) and ⟨M(P, ψ_ij)⟩, as in Table 2.