Quantum-Assisted Interaction Validation for Real-Time Confidence Scoring
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Solution Overview
Problem
Cloud-computing environments face threats and cyberattacks, and existing systems lack efficient and accurate methods for securing and validating interactions and recommendations in real-time.
Innovation Solution
A combined classical computing and quantum computing system utilizing generative machine-learning models to identify intent and named entities, assign confidence scores, and generate recommendations based on historical interactions and metadata, leveraging quantum parallel processing to improve efficiency, accuracy, and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a classical computing system is used to search historical interactions and generate recommendations, then the system structure is simple, but the processing speed and execution time are slow
Solution Approach 1:
The patent combines classical computing systems with quantum computing systems to create a hybrid architecture. The classical system handles data storage, retrieval, and basic processing, while the quantum system performs parallel search operations on historical interactions. This merging allows the system to achieve quantum speedup in processing speed while maintaining the structural benefits of both computing paradigms.
Solution Approach 2:
The patent introduces quantum bits (qubits) that can exist in multiple states simultaneously through superposition, adding a quantum dimension to the classical binary state space. This allows the system to represent and process information in a higher-dimensional space, enabling parallel exploration of multiple historical interactions simultaneously and significantly reducing execution time.
2Loss of time
If quantum computing is used to perform parallel operations on historical interactions, then the execution time is reduced significantly, but the system complexity increases
Solution Approach 1:
The patent divides the computational task into distinct segments: classical computing handles data preparation, retrieval, and post-processing, while quantum computing performs the core parallel search and analysis operations. This segmentation allows each subsystem to operate in its optimal regime and simplifies the overall system architecture by assigning specific functions to specific computing platforms.
Solution Approach 2:
The patent introduces quantum algorithms as intermediaries that bridge the classical and quantum systems. These algorithms translate classical problem statements into quantum operations, enabling efficient communication and coordination between the two computing paradigms while hiding the complexity of quantum operations from the classical system.
3Measurement precision
If classical computing systems process interaction requests sequentially, then the system is easier to implement, but the accuracy and completeness of validation are reduced
Solution Approach 1:
The patent implements continuous validation by simultaneously exploring multiple historical interactions through quantum parallel operations. Rather than sequentially checking interactions one by one, the quantum system continuously processes multiple candidates in superposition, ensuring comprehensive and accurate validation without interruption or omission.
Data Source
AI summary
A system includes a memory configured to store a plurality of instances of a software application executable on a computing device and a set of confidence scores. The system includes one or more processors operably coupled to the memory and configured to receive an interaction request for initiating an execution of an interaction, in which the interaction request includes metadata. The one or more processors may further execute one or more generative machine-learning models trained to identify, based on the interaction request and the metadata, an intent and one or more named entities included within the interaction request or the metadata, assign, based on the identified intent and one or more named entities, a confidence score to the interaction request, and generate, based on the confidence score assigned to the interaction request, a generative response including a recommendation to initiate the execution of the interaction to satisfy the interaction request.