View source for 17961609. QUANTUM-COMPUTING-POWERED SYSTEM WITH MULTI-DIMENSIONAL SCALING FOR DATABASE BACKUP, SEARCH, AND RECOVERY simplified abstract (Bank of America Corporation)

=QUANTUM-COMPUTING-POWERED SYSTEM WITH MULTI-DIMENSIONAL SCALING FOR DATABASE BACKUP, SEARCH, AND RECOVERY=


==Organization Name==

[[:Category:Bank of America Corporation|Bank of America Corporation]]


[[Category:Bank of America Corporation]]

==Inventor(s)==

[[:Category:Vimal Chandroliya of Gujarat (IN)|Vimal Chandroliya of Gujarat (IN)]][[Category:Vimal Chandroliya of Gujarat (IN)]]

[[:Category:Magaranth Jayasingh of Tamil Nadu (IN)|Magaranth Jayasingh of Tamil Nadu (IN)]][[Category:Magaranth Jayasingh of Tamil Nadu (IN)]]

==QUANTUM-COMPUTING-POWERED SYSTEM WITH MULTI-DIMENSIONAL SCALING FOR DATABASE BACKUP, SEARCH, AND RECOVERY - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17961609 titled 'QUANTUM-COMPUTING-POWERED SYSTEM WITH MULTI-DIMENSIONAL SCALING FOR DATABASE BACKUP, SEARCH, AND RECOVERY

==Simplified Explanation==

The abstract describes a method for database backup, search, and recovery using a quantum-computing-powered system with multi-dimensional scaling. The method involves storing datasets in local and cloud-based databases, routing search queries to a quantum processor during outages, and restoring data using the quantum processor.

* Storing a first dataset in a local database and generating a second dataset from a critical subset of the first dataset.
* Storing the second dataset in a remote, cloud-based database.
* Routing search queries to a quantum processor during database outages.
* Restoring data from the cloud-based database to the local database using the quantum processor.
* Automatically scaling the quantum processor during processing tasks.

== Potential Applications ==
This technology could be applied in various industries such as finance, healthcare, and e-commerce for secure and efficient database management.

== Problems Solved ==
This technology solves the issues of database downtime, data loss, and inefficient search capabilities during database outages.

== Benefits ==
The benefits of this technology include improved database reliability, faster data recovery, and enhanced search performance using quantum computing.

== Potential Commercial Applications ==
One potential commercial application of this technology could be in cloud computing services, offering advanced database backup and recovery solutions to businesses.

== Possible Prior Art ==
Prior art in the field of quantum computing and database management may include research papers, patents, or products related to quantum-powered data processing and storage systems.

=== Unanswered Questions ===

=== How does this method ensure data security during the backup and recovery process? ===
The article does not provide details on the security measures implemented to protect the data during backup and recovery using the quantum processor.

=== What are the scalability limitations of the quantum processor in handling large datasets? ===
The article does not address the potential limitations of the quantum processor in scaling to process large datasets efficiently.


==Original Abstract Submitted==

A method for database backup, search, and recovery using a quantum-computing-powered system with multi-dimensional scaling is provided. The method may include storing a first dataset in a first local database, generating a second dataset from a critical subset of the first dataset, and storing the second dataset in a second remote, cloud-based, database. The method may include receiving a search query at the first database. The method may include receiving a first indication of an outage at the first database and, in response to the first indication, routing the search query to a quantum processor and executing the search on the second dataset. The method may include receiving a second indication of a loss of data from the first dataset at the first database, and, in response to the second indication, restoring the critical subset of the first dataset to the first database from the second dataset at the second database using the quantum processor. The method may include automatically scaling the quantum processor during a processing task.



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