Bank of America Corporation

Raghavendra Veerupakshappa

Suki Ramasamy

Samson Paulraj

Ravi Ranjan Shandilya

Iruvanti John Dinakar

Nikhil Ram

Phillip Matt Hancock

Selvaraj Muthurakkianan

Antony Robert Raj

Nagaraja Peddi

Umme Kulsum Shaik

Christophe M Marin

Cynthia D Adams

Kenneth William Schmidt, Jr

Kavitha Ganapathi Raman

Garima Dhir

Gina L Tammelleo

Joshua C Wolfe

Karrie A Loatman

Santanu Sarkar

Scott B Desalvo

Timothy Krak

Balasubramanian Bagavathiappan

Arrangements for quantum-based encryption are provided. In some aspects, input data and a superposition collapse value associated with the input data may be received from a user computing device. In addition, the superposition collapse value may include a threshold percentage indicating a percentage of quantum bits to be collapsed. The input data may be converted into a set of quantum bits. Superpositions of the set of quantum bits may be collapsed based on the superposition collapse value. In addition, collapsing the superpositions may include forcing each quantum bit of the set of quantum bits to take a single value. An encoded result of the input data may be obtained by determining quantum states of the collapsed set of quantum bits. The encoded result of the input data may be converted into alphanumeric code and stored in a data store with the superposition collapse value associated with the input data.

Quantum-based encryption

A system is provided for process monitoring using a quantum artificial intelligence powered distributed server network. In particular, the system may continuously monitor processes being executed within the network environment. Upon detecting an issue with the process, the system may use a quantum AI oracle to identify the root cause of the issue. Once the root cause has been identified, the system may adjust a process reliability score associated with the faulty computing device or application. By generating process reliability scores for the various computing devices and applications within the network environment, the architecture of the network environment may be modified by selectively including components that function reliably.

Artificial intelligence powered distributed server network

Embodiments of the invention are directed to intelligently and dynamically controlling both changes made within EUC applications and the control rules associated with such changes. A similarity index is calculated/assigned for each data entry field (i.e., cell/intersection) and the controls implemented when a changes to data in the entry fields occurs is based on the similarity index. In other embodiments, a change to data entry fields dynamically prompts analysis of the change based on historical approval and/or denial patterns specific to the EUC application, the data entry field(s) and/or the user of the application. In response to the analysis the control rules may be dynamically updated, and applied to the current change. In other embodiments, inputs, such as reviewer's comments, are the basis for determining a need to update existing controls or add new controls associated with data entry field(s) and the conditions associated therewith are determined and applied.

Intelligent controls for end-user computing

Growing community of inventors

Telangana, India

Raghavendra Veerupakshappa