Quantum ML Transaction Routing for Blockchain Latency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Blockchain systems face latency issues due to limited block capacity, leading to inefficiencies and delays in transaction processing, with existing solutions either exacerbating network congestion or undermining decentralization.
Innovation Solution
A system utilizing quantum machine learning to cluster and prioritize distributed ledger transactions, determining optimal computer hardware for mining through parallel simulation testing, thereby optimizing transaction routing and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If block capacity is increased to process more transactions, then transaction throughput is improved, but network congestion and latency worsen
Solution Approach 1:
The system performs preliminary actions by continuously monitoring telemetry data and pre-calculating optimal hardware configurations before transactions are assigned. The quantum machine learning model prepares multiple potential hardware assignments in advance, and when a block needs to be mined, the optimal hardware is already identified, reducing latency without requiring larger block capacity.
2Productivity
If quantum machine learning is used for transaction routing optimization, then mining efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces a telemetry database as an intermediary layer between the quantum machine learning model and the distributed ledger network. This intermediary pre-processes and stores hardware performance data, allowing the quantum model to query optimized results without direct complex interactions with the underlying hardware infrastructure, thereby managing system complexity while maintaining mining efficiency.
Data Source
AI summary
Systems, computer program products, and methods are described herein for data block analysis prioritization and routing via quantum machine learning. The present disclosure includes retrieving distributed ledger transactions, retrieving a stream of telemetry data of computer hardware, clustering, based on the transaction metadata, the distributed ledger transactions using a clustering engine, generating, using a machine learning model, a predetermined number of transaction placement schemas of the computer hardware, determining, from a probability output by parallel simulation testing via a quantum computer, a prime schema and a configuration of the prime computer hardware, and routing, based on the prime schema, a transaction cluster to the prime computer hardware.