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 Quantum World Information and Simulation Environment (QuSAI) is a development approach to a classical-quantum hybrid platform for creating large-scale simulations that can model complex social and environmental systems. The Reference World Information Synthetic Environment (RWISE) platform is the classical system hosting the new quantum computing simulation engine.  QuSAI is designed to support a wide range of simulations, from small-scale experiments to large-scale models of entire regions or countries.

We’re leading the way in quantum/classical Modeling & Simulations with eXplainable AI.  We’ve successfully run simple multi-player simulations with a quantum computer and have integrated quantum simulator with classical computing. Our first use of classical/quantum simulations has been a multi-player, quantum-based simulation of the Indo-Pacific Off-Roads from War where we simulated China, India, Japan, Taiwan, Australia, and the US as named entities.

Our agent-based M&S forecasts how people’s behaviors change with changes to their operating environments.  The extension into quantum computing opens a whole new world in understanding and managing aspects of societies and social interactions. Nothing can be used to explore and address wicked problems better.

The QuSAI platform includes a variety of tools and models for building simulations, including models of physical systems, environmental systems, and social systems. The platform is designed to be flexible and customizable, allowing users to add or modify models as needed to create simulations that reflect specific contexts or scenarios.

One key feature of the QuSAI platform is its focus on using classical reference data to ensure that simulations are accurate and reliable. Reference data is data that has been collected from real-world systems, and it is used to calibrate and validate simulations. This ensures that simulations accurately reflect the behavior of real-world systems and can be used to make predictions about future behavior.

The QuSAI applications may include environmental modeling, disaster management, and campaign planning. It has the potential to provide valuable insights and predictions for a wide range of complex systems and could help to inform policy decisions and improve outcomes for a variety of stakeholders.

Quantum computing is a type of computing that uses quantum-mechanical phenomena, such as superposition and entanglement, to perform operations on data. Unlike classical computing, where data is represented as bits that are either 0 or 1, quantum computing uses quantum bits, or qubits, which can represent a combination of 0 and 1 at the same time. This allows quantum computers to perform many calculations simultaneously, which can lead to significant speedups for certain types of problems.    

Visit: https://qusai.ai   

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Electronically Stored Information Smart Search Technology (ESISST), was originally conceived as Semantic Search Technology (SST) and developed for the US Air Force Research Lab (AFRL). Initially we created our own semantic parsing, search, clustering, virtualizing software code through the AFRL contract and self-funding. The touch labor for maintaining code, monitoring performance, and enabling scalability was too high. We re-engineered the ESISST deployment and execution framework to leverage services for ease of maintenance, containerization to enable deployment to any system, and streaming to auto scale to meet any demand due to volume of data. We offer ESISST as a Service (AaaS) and Software as a Service SaaS) built on the AWS Infrastructure as a Service (IaaS). We’ve developed and are developing different editions to serve different verticals and groups within verticals.

We’ve built the latest versions (Individual and Enterprise Bronze Editions) in Amazon Web Service (AWS) and are making it available through the AWS Marketplace. No other such offering is made in AWS. ESISST has an architecture designed and tested to allow rapid plug-in of different microapps to retain a leading state-of-practice configuration capability for competitive advantage. AWS is constantly upgrading and increasing the mini apps they offer. The AWS hosting and our use of AWS services allows us to update versions simply and quickly. We’ve used the AWS services and mini-apps to offer a user-initiated subscription and use process with no touch labor support required for new customers. AWS performs automated billing at our whole/retail rate to the subscriber’s credit card account and distributes our contribution margin directly to our financial account. We have three premium levels of service editions (Silver, Gold and Platinum) that offer increasing levels of services and security in our development pipeline. The top service levels incorporate security features approved by the US government for Personally Identifiable Information (PII), Personal Health Information (PHI) and Confidential Unclassified Information (CUI).

for more information visit our product website at: Electronically Stored Information Smart Search Technology | ESISST (https://esisst.net)  

Check out our AWS Marketplace: AWS Marketplace: Electronically Stored Information - Semantic Search Technology (silver) 

Email us for more info: cfdayinfo@cfday.net

About Reference World Information Synthetic Environment (RWISE):
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RWISE is an Agent Based Modeling and Simulation capability that allows for the true complexities in life to be maintained in the simulation, allowing for emergent phenomenon that cannot be factored into the simulation with any other type of tools. Other modeling tools base results on repetition of historical data and events loaded into the system to return forced-choice results. RWISE escapes the “forced choice” paradigm. It also provides sentiment analysis and predictive sentiments and accounts for complex interaction of factors. You can’t factor every variable into your model but RWISE can do that for you.

• Uses multiple data sources to provide a broad and deep understanding of likely outcomes
• Derives the individual traits, influences, factors, and milestones that have the most impact on results.
• Models the traits, sensors, environment and well-being of each entity – organization or individual. Well-being creates a predictive connection between events in the entity’s environment and individual motivation, decisions and outcomes.
• Shows how strategies are best customized for groups. Helps us write policy and rules that allow for the desirable variation.
• Keeps a real time geographic view, a needs analysis and testing environment for collaborative leaders solving complex problems with limited resources.
• Use data to build more effective and efficient collaboration among key partners as these entities work to address complex social issues across individuals and place, through more targeted interventions that support a whole person approach
• Maximize previous investments made in organizations and organization of data systems.

This model uses cognitively and socially sophisticated agents that interact in stochastically defined ways based on different applicable theories. When the different agents are overlaid with the geography and information network, formal and informal, the model intrinsically begins to build linkages and relationships. Linkages and effects between agents of various types, variables (constructs) and facets emerge intrinsically during the simulations.

The main features of the simulations:

• Bring to life longitudinal data, creating in a computer the pipeline of individual agents.
• Models the traits, sensors, environment and well-being of our population. Well-being creates a predictive connection between events in the individual’s environment and individual motivation, decisions, and outcomes.
• Introduces events, real or planned, into the environment. Let’s users see how people respond. Organizations and institutions see how their contributions are experienced in people’s lives.
• Derives the individual traits, influences, factors, and milestones that have the most impact on results.
• Shows how strategies are best customized by groups for groups. Helps us write policy and rules that allow for the desirable variation.
• Keeps a real time geographic view, a needs analysis and testing environment for collaborative leaders solving complex problems with limited resources.

We then develop a full scale model of the geographic area representing the population at appropriate levels of granularity, state institutions and organizations, and non-governmental organizations as appropriate. All these entities are configured and calibrated with the real world data and they emulate the real world actions & response to interventions of policy makers and organizations.
This environment provides the features analysts can use to run different scenarios, and be compared to see how a different mix of policy interventions will likely have different impacts on the metrics of interest.  

Email us for more info: cfdayinfo@cfday.net