Bank of America Corporation (20240330727). SYSTEM AND METHOD FOR DYNAMICALLY ANALYZING AND CONFIGURING NODES OF A DISTRIBUTED NETWORK LEVERAGING PHOTONIC QUANTUM COMPUTING simplified abstract
SYSTEM AND METHOD FOR DYNAMICALLY ANALYZING AND CONFIGURING NODES OF A DISTRIBUTED NETWORK LEVERAGING PHOTONIC QUANTUM COMPUTING
Organization Name
Inventor(s)
Shailendra Singh of Thane West (IN)
SYSTEM AND METHOD FOR DYNAMICALLY ANALYZING AND CONFIGURING NODES OF A DISTRIBUTED NETWORK LEVERAGING PHOTONIC QUANTUM COMPUTING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240330727 titled 'SYSTEM AND METHOD FOR DYNAMICALLY ANALYZING AND CONFIGURING NODES OF A DISTRIBUTED NETWORK LEVERAGING PHOTONIC QUANTUM COMPUTING
The abstract describes a system that utilizes photonic quantum computing to dynamically analyze and configure nodes in a distributed network. The system identifies nodes, extracts metadata, monitors for changes, detects anomalies, determines target metrics, creates simulation test scenarios, executes them via a quantum computer, and determines an optimal configuration for the nodes.
- System leverages photonic quantum computing for dynamic analysis and configuration of nodes in a distributed network.
- Identifies nodes, extracts metadata, monitors for changes, detects anomalies, and determines target metrics.
- Creates simulation test scenarios based on anomalies, metrics, and factors associated with nodes.
- Executes simulation test scenarios in parallel using a quantum computer.
- Determines optimal configuration for nodes based on executing simulation test scenarios.
Potential Applications: - Network optimization in telecommunications - Cybersecurity threat detection and prevention - Resource allocation in cloud computing environments
Problems Solved: - Efficiently analyzing and configuring nodes in a distributed network - Detecting anomalies and optimizing network performance - Leveraging quantum computing for complex simulations
Benefits: - Improved network performance and reliability - Enhanced cybersecurity measures - Optimized resource allocation and utilization
Commercial Applications: Title: Quantum-Enhanced Network Optimization System This technology can be used in telecommunications companies, cybersecurity firms, and cloud computing providers to enhance network performance, security, and resource management. The market implications include increased efficiency, reduced downtime, and improved data protection.
Questions about Photonic Quantum Computing: 1. How does photonic quantum computing differ from traditional computing methods? Photonic quantum computing utilizes quantum bits (qubits) and quantum entanglement to perform computations, offering the potential for exponentially faster processing speeds compared to classical computers.
2. What are the challenges in implementing photonic quantum computing in practical applications? Implementing photonic quantum computing faces challenges such as maintaining qubit coherence, error correction, and scalability of quantum systems. Researchers are actively working to overcome these obstacles to realize the full potential of quantum computing technology.
Original Abstract Submitted
a system for dynamically analyzing and configuring nodes of a distributed network leveraging photonic quantum computing is provided. in particular, the system may be configured for identifying one or more nodes of a distributed register network, extracting metadata from the one or more nodes, monitoring the one or more nodes to detect changes to the one or more nodes, determining, via a deep learning network, anomalies associated with the one or more nodes based on the changes, determining target metrics and factors associated with the anomalies, creating simulation test scenarios based on the target metrics, factors, and anomalies, wherein the simulation test scenarios are associated with a plurality of configurations of the one or more nodes, executing the simulation test scenarios in parallel, via a quantum computer, and determining an optimal configuration for the one or more nodes from the plurality of configurations based on executing the simulation test scenarios.
