Our Hybrid Quantum Platform
CogniFrame Financial Service Operating Layer is built on top of Quantum Cloud solving often intractable and computationally intensive non-convex and stochastic optimization as well as simulation type problems. Multiple financial use-cases are supported for a number of leading organizations and our solutions set include Pure Quantum, Near Quantum, and Hybrid Quantum based approaches. We can handle a number of problems at commercial scale.
Standard Classical Optimization algorithms take too long even to find good solutions. Multi-period optimization problems are NP-Hard. Quantum Computing has demonstrated advantages over classical methods. In fact, all classical risk problems can also be solved using Quantum once economic viability of pure quantum is established. Quantum represents the future direction.
We leverage the power of the most powerful Quantum hardware available today. Our focus is on threshold optimization to solve problems that are NP Hard and solving Simulation problems using a Simulated Quantum Approach.
CogniFrame’s OFS Algorithm helps helps solve what is traditionally considered an NP-Hard problem (due to high dimensionality) and It helps reduce features of the data without losing on quality of outcome leading to improved Machine Learning Training Time. It also helps uncover mutual information between weaker features that cannot be uncovered using traditional approaches and existing algorithms optimally.Read More
While it is beneficial for organizations to share data with each other, a key concern is protecting the sensitive information without sacrificing the quality of information. The problem then become transforming a high-sensitive dataset into a privacy-preserving, low risk data which can be safely shared with any entities ranging from researching teams to business partners. CogniFrame’s anonymization algorithm ensures that the identify of an individual is non-distinguishable among other records, providing an optimal anonymization method without sacrificing the information in the data.Read More
There are inherent problems with clustering because it is a NP-Hard problem with High Dimensionality. This is because as you increase the number of unlabelled examples, clustering becomes too complex for classical methods. Many traditional clustering techniques do not provide the optimal solution for data mining scenarios. CogniFrame’s hybrid quantum solution can solve this NP-hard problem, finding the optimal clusters for client needs.Read More
CogniFrame algorithms leverage the power of Quantum Computing to derive optimized samples of potential optimal solutions for Asset Liability Management to help Financial Institutions improve their Return on Assets (ROA) on their Banking Book.Read More
The solution focuses on minimizing the cost associated with trades and collateral deployment while maximizing profitability. In a centralized collateral environment, the application solves the collateral management and allocation problem.Read More