Quantum Neural Network Change Prediction Platform

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Solution Overview

Problem

Current systems lack an efficient and accurate method to identify and apply change instructions to event processing requests, leading to missed or incorrectly applied instructions due to the high volume of requests processed, resulting in defects such as wrong event processing or routing issues.

Innovation Solution

A computing platform with quantum neural networks retrieves historical event processing requests and change instructions, encodes them using quantum encoding, and processes them with natural language processing to train a quantum change schema model, which generates change schemas for event processing requests, ensuring accurate application of change instructions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If the system processes a multitude of event processing requests in a given period of time, then the productivity is improved, but the reliability deteriorates due to missed or overlooked change instructions

Engineering Contradiction:

Improvevolume of requests processedVSAvoidaccuracy of change instruction application

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary actions by predicting change instructions before they are officially applied to event processing requests. The quantum neural network analyzes historical data and patterns to forecast which change instructions are likely to be missed, allowing the system to proactively identify and alert operators before the actual processing occurs, thus maintaining high productivity while improving reliability.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms by continuously monitoring the application of change instructions and comparing them against predicted patterns. The quantum neural network processes feedback from historical event processing requests and adjusts its predictions accordingly, creating a closed-loop system that improves accuracy over time while maintaining high processing volumes.

Inventive Principle:

Principle #23Feedback

2Device complexity

If the system uses traditional methods to process change instructions, then the device complexity is low, but the measurement precision deteriorates leading to missed change instructions

Engineering Contradiction:

Improvesimplicity of processing methodVSAvoidaccuracy of change instruction identification

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The system changes the fundamental parameters of how change instructions are processed by transitioning from traditional rule-based methods to quantum neural network-based prediction. This involves encoding event processing requests and change instructions into quantum states, using quantum algorithms to analyze patterns, and decoding predictions into actionable insights, thereby dramatically improving measurement precision while managing complexity through structured quantum processing stages.

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If the system applies change instructions manually, then the ease of operation is high, but the productivity deteriorates due to time-consuming processing

Engineering Contradiction:

Improvesimplicity of change instruction applicationVSAvoidspeed of event processing

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system implements self-service by using the quantum neural network to automatically predict and identify change instructions that need to be applied to event processing requests. The system autonomously analyzes historical data, predicts missed change instructions, and generates alerts without requiring manual intervention, thereby maintaining ease of operation while dramatically improving processing speed and productivity.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250103937A1Change prediction platform using quantum neural networks and change schema

Publication Date: 2025.03.27 BANK OF AMERICA CORP

AI summary

Arrangements for a change prediction model using a quantum neural network are provided. A platform may train a quantum change schema model. The platform may receive and process an event processing request and one or more change instructions. The platform may generate a change schema for the event processing request using the model and based on the one or more change instructions. The platform may cause processing of the change schema. The platform may update the model based on receiving results of processing the change schema. The platform may cause the event processing request to be processed based on the change schema. The platform may further update the model based on modified change instructions received as a result of processing the event processing request.