Datasets Comparison

Online mathematical tools from Ref. [94] in the paper entitled: "Quantum Double-field Model and Application" were used to conduct computations to validate the data generated from the QDF model and evaluate the fmv (four main variables) equations. In addition, the method paper Ref. [7] for MethodsX J, implements and further validates the QDF model proving its universality for a set of CPTs and QPTs observed in a thermodynamic system. This includes the summary of the design and quantum thermodynamic aspects to implement and validate the "Quantum Field Lens Coding and Thermodynamic Metrics" algorithm (model) as a supplementary file to this paper. The paper entitled: "A Double-field Computation Model to Simulate Physical Systems" introduces this algorithm and its application whereas the method article discusses its implementation and results. The method article as the quantum algorithm code evaluates the entanglement entropy of this model. Secondly, the QDF game demo and relevant data are available in this dataset. For instance, animating the data used in the communication steps 1-7 of the QDF system model in the article is presented via "QDFGTAnim.pptx" and "QDFGTAlgorithm.nb" files. Lastly, expert highlights of peer-reviewed QDF Game Theory aspects expanding to the universality of the model is part of this dataset as "Expert_Model Highlights (2023).pdf" document with discussion on how this model's universality was achieved in the current article's version throughout its QDF examples and proof of its theory.

1- As directed in the "QDFCalc (2022).pdf" file, online simulators and software calculators were used, specifically from Ref. [94], S. Wolfram, Mathematica Computational Systems at: https://www.wolframalpha.com/pro/ such as WolframAlpha CI calculator at: https://www.wolframalpha.com/ 2- You may click on the active hyperlink(s) to reproduce and test the parametric/variable values tried in the "QDFCalc (2022).pdf" file. 3- Certain dataset conclusions in the article were derived from Microsoft’s Azure Quantum at: http://docs.microsoft.com/en-us/quantum [Accessed, 2015–2021]. 4- Reference [93] also was adapted to the QDF transition model to establish transition matrices presented in the proof section of the article. This reference is [93]: D. A. Kofte, Lecture Notes on Transition Probability Matrices, from: Molecular Simulation at: http://www.eng.buffalo.edu/∼kofke/ce530/Lectures/Lecture8/index.htm, Buffalo Univ., USA (2000) [Accessed, 2016-2021]. 5- Supportive trial-based computations were also conducted in the Wolfram file "QDFGTAlgorithm.nb" for further study of the matrices. 6- Quantum communication model and Game Theory are presented in the PowerPoint file with animation on the QDF model and its communication protocols' participants such as Alice, Bob, Eve and the audience. 7- The method to compute and implement the QDF system as a QDF circuit is for Methods X J (contact authors for further details on obtaining this data), which is in qubit code, quantum computation and application: 7.a- A zip file named "QDF-LCode Files.zip" is provided to demo the example of a QDF lens code, discussed and proven in our method article (given permission, you may request this from the authors). This file is updated in this revision by updating the Graphical Abstract for the MethodsX Elsevier journal: 7.b- Graphical Abstract for publishing our Double-field Computation method in MethodsX J is added to the list named as "Graphical Abstract 2023.png" to give an overall preview of the method used for the two papers related to this dataset for this revision.

University of Victoria

Probability Theory, Theory of Computation, Condensed Matter Physics, Particle Physics, Thermodynamics, Quantum Computing, Quantum Communication, Engineering Physics, Quantum Field Theory, Quantum Statistical Mechanics, Computational Engineering

Creative Commons Attribution 4.0 International

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.

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.

University of Victoria

Probability Theory, Artificial Intelligence, Theory of Computation, Condensed Matter Physics, Particle Physics, Game Theory, Thermodynamics, Quantum Computing, Business Intelligence, Quantum Communication, Engineering Physics, Decision Theory, Quantum Field Theory, Quantum Statistical Mechanics, Computational Engineering, Computer Game

Creative Commons Attribution 4.0 International