Innovative Patent Concept Framework: The Convergence of AI and Quantum Computing (Ver. 2026.02.07)

 Introduction

​The following concepts represent a strategic framework of technical ideas designed to address the critical bottlenecks in the future integration of Artificial Intelligence (AI) and Quantum Computing. By timestamping these ideas today, we establish a definitive record of these technical philosophies.

​1. Dynamic Synchronization for Hybrid Quantum-Classical Learning

​Core Concept: A specialized control circuit that predicts quantum processor "idle time" to dynamically throttle the weight update timing of classical AI models.  

​Objective: To eliminate latency bottlenecks where high-speed quantum calculations are stalled by slower classical data preparation.  

​2. Quantum Noise as a Source of Hardware Fingerprinting

​Core Concept: A system that utilizes AI to learn the specific entropy and noise patterns of a quantum device, converting "computation errors" into a unique hardware-based ID or cryptographic key.  

​Objective: To create unforgeable authentication protocols inherent to the physical characteristics of specific quantum chips.  

​3. AI-Driven Quantum Circuit Pruning and Optimization

​Core Concept: An AI-based quantum compiler that reconfigures and minimizes the number of required gates while maintaining the target AI model's accuracy.  

​Objective: To enable complex algorithms to run on NISQ (Noisy Intermediate-Scale Quantum) devices by reducing gate-induced errors.  

​4. Advanced Entanglement-Preserving Memory Swap Protocols

​Core Concept: A memory management technology that segments large-scale AI models into sub-units, enabling high-speed swapping between quantum registers and classical memory without collapsing entanglement states.  

​Objective: To overcome the physical limitations of restricted qubit counts in early-stage quantum processors.  

​5. Probabilistic Reliability Scoring for Quantum AI Outputs

​Core Concept: A real-time verification AI that analyzes the statistical distribution of quantum computation results to assign a "Confidence Score (%)" for downstream decision-making.  

​Objective: To provide the necessary safety and reliability layers for mission-critical applications in finance and auton


omous systems.  

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