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

Description

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.

Steps to reproduce

1- Download from the root directory the relevant files to run code, revise code and update datasets on your computer according to the "folder structure" presented in Fig. 1 of the DIB article, or <ref> folder under <model> directory. Or simply download <root> as a zip file for this. 2- By default, Windows OS for processing files as *.py, *.ipynb, *.cq, *.txt, *.png, *.csv, *.pdf files (see data format description and Sec. 1 of the DIB article) 3- Visual Studio Code (VSC) to run current and updated versions of the Code Listings 4 and 5 of the DIB article in *.py or take in *.csv and *.text outputs from QInspire and IBMQ platforms as *.ipynb, *.cq: 4- Run QAI-LCode_QFLCC.py program code via command line interface (CLI), see Fig. 7 of the DIB article. For example, to install the python imports in the code defined and installed on Windows OS is: pip3 install --upgrade pip 5- Run the QAI-LCode_QFLCC.py program to generate real-time datasets via its PAnalysis_model function. Process/analyze datasets in e.g., *.csv or tabulated text with histograms and circuit diagrams. A successful installation is shown by Figs. 7(a-c) from the DIB article. 6- “Alice & Bob’s Quantum Doubles” game as part of the QAI-LCode_QFLCC.py code, is run via optional functions to validate QF-LCA datasets obeying the QDF game model which has communication protocols’ participants as Alice, Bob, Eve, and the audience to win a/the prize (TS), see lower Fig. 8(a) of the DIB article. 7- Large scale data require asynchronous execution of threads within the *.py code for large and multiple dataset processing. Example is, run one class taking inputs of the largescale dataset, process it based on if-else conditions, after basic installation of the dataset (as a *.csv file) and analysis of P’s of events (predicted events, or see dataset of Step # 4) in program Listing 4, lines #526 to #1020 of the DIB article. 8- For generating a QAI map and solution, follow Sec. 3.4 in order to reach reliably strong prediction of an event with high fidelity in ⟨M(P,ψ_ij)⟩ outcomes [5]. Manual calculations and validation of datasets from [1, 3-5]: 1- As directed in the “QDFCalc (2022).pdf” file, online simulators and software calculators were used from ‎[8], i.e., S. Wolfram’s Mathematica Computational Systems and Alpha calculator. 2- Click on the active hyperlink(s) to reproduce and test variables tried in the “QDFCalc (2022).pdf” file. 3- Supportive trial-based computations were conducted in the Wolfram file “QDFGTAlgorithm.nb” for further study of the ST matrices. The method to compute and implement the QDF system as a QDF circuit is in qubit code, quantum computation and application: 4- For large datasets, the QAI-LCode_QFLCC.py file can be programmed to import and export from scripts like *.nb scripts as discussed in the DIB article, Sec. 3.3. It can then process IBMQ outputs coded in cQSAM language while computed in the background by Wolfram during Python program runs.

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