Quantum Machine Learning for Prioritized Blockchain Transaction Routing
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Solution Overview
Problem
Blockchain systems face latency issues in the block mining process due to limited block capacity, leading to delays and inefficiencies when transaction volumes are high.
Innovation Solution
A system and method utilizing quantum machine learning to prioritize and route data blocks by clustering transactions, generating transaction placement schemas, and determining prime computer hardware configurations through parallel simulation testing with a quantum computer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If block capacity is increased to handle more transactions, then transaction throughput is improved, but block size and network bandwidth requirements increase
Solution Approach 1:
The patent segments transactions into different priority levels and routes them to different mining pools based on their importance and time sensitivity. Critical transactions are separated from standard transactions, allowing the system to handle high-volume traffic without increasing individual block sizes, thus resolving the contradiction between throughput and block volume.
Solution Approach 2:
The patent introduces a new dimension of transaction routing by creating multiple parallel mining pools with different specialization characteristics. Instead of increasing block capacity in a single dimension, the system distributes transactions across multiple dimensions (different pools), achieving high throughput without expanding individual block sizes.
2Productivity
If quantum computing resources are deployed for transaction optimization, then mining efficiency is improved, but system complexity and computational overhead increase
Solution Approach 1:
The patent introduces a quantum computing intermediary that acts as a middle layer between transaction submission and mining execution. This quantum intermediary optimizes transaction routing and pool selection without requiring the entire mining infrastructure to become quantum-based, thus improving efficiency while containing system complexity through selective quantum application.
Solution Approach 2:
The patent creates virtual copies of mining pools with different characteristics and uses quantum computing to determine the optimal copy for each transaction. This allows efficient optimization without physically duplicating entire mining infrastructures, managing complexity through virtual rather than physical replication.
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.