Time-Dependent Quantum Artificial Intelligence (TD-QAI) for the Scientific Computing of Deep Future Determinacy

DCCS QAI (QM/QED TD-QAI) have computed the 2024 US presidential election outcome from the mechanical population results of the State of Florida.

Statistical mechanics sampling indicate the 2024 election result according to perturbed simulations of the State of Florida, which is the essential determinant of the national electorate.”

— Aron Workman, CEO

LONDON, UNITED KINGDOM, September 11, 2023/EINPresswire.com/ — DCCS is the applied Differential Computational Central Sciences arm of Arowor Corp. (Sussex, Delaware, United States), which has developed a new time-dependent mode (TD) for its pioneering quantum artificial intelligence (QAI) superstructure (SS).

DCCS runs computed QM/QED probabilities and applied statistical mechanics derived from HPC scientific computing engine run atop QAI and TD-QAI. DCCS QAI exploits a niche specialty in applied mathematics called statistical mechanics, colloquially “quantum stats”.

QAI point computations and TD-QAI, the latter of which are the new time-dependent invention of DCCS, are initialized to analyze the iterations of perturbed permutational AI models having initial fixtures which span public and proprietary records across both open-source and secure information repositories. Initial data fixtures for sample experimental model systems were derived from the US census, DNA sequencing result, electoral populations, and other dataset populations of complex relational logic.

The result is an accurate predictive mode of the complex dataset derived from the prescient sciences of time-dependent quantum mechanics (QM TD-DFT) and applied non-Bayesian statistical mechanics. Aron Workman, CEO, has pioneered time-dependent QM research, including published studies in Computational and Theoretical Chemistry on real-time time-dependent quantum chemistry and time-dependent molecular videography.

The QAI SS were assembled from ab initio QM structures. The parameterizations and approximations of classical mechanics were integrated with a step function for the cost-effective balance of precision, resolution, and accuracy. The rulesets, constraints, controls, and scopes for the QAI SS were drawn from QM fundamentals and formalisms, reduced to relational logic, devised to differential equation, and drafted into programmatic syntax. The new time-dependent mode employs the time-dependent Schrödinger and Hamiltonian for analysis, parameterization, and perturbation of the models.

DCCS routinely and predominantly computes complex scientific datasets, but also developed an exploratory election prediction module.

ELECTION PREDICTION MODULE. Other computed election-prediction scripts are derived from pollsters and exit data which are not utilized by DCCS. Statistical mechanics population simulations and experiments executed by DCCS have accurately predicted every United States presidential election since 2006, including recently both Trump vs. Clinton and Biden vs. Trump. First, a population analysis is prepared in equilibrium. The QAI model were fermions with quantum numbers assigned party, age, sex, race, and other data derived from the US census and additional open source intelligence datasets. A Mulliken analysis of voter models were successively perturbed for model constraints and rulesets. Finally, the QAI engine iteratively computed the model system to conclude the probability density of the event quantum, which in this instance is the election outcome. There is unity non-Bayesian probability density that a reelection candidate will succeed in the 2024 presidential election.

Aron Workman
Arowor Corp.
[email protected]
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Originally published at https://www.einpresswire.com/article/652007316/time-dependent-quantum-artificial-intelligence-td-qai-for-the-scientific-computing-of-deep-future-determinacy