View source for GOOGLE LLC (20240296359). CLASSIFICATION USING QUANTUM NEURAL NETWORKS simplified abstract

=CLASSIFICATION USING QUANTUM NEURAL NETWORKS=


==Organization Name==

[[:Category:GOOGLE LLC|GOOGLE LLC]]


[[Category:GOOGLE LLC]]

==Inventor(s)==

[[:Category:Edward Henry Farhi of Venice CA (US)|Edward Henry Farhi of Venice CA (US)]][[Category:Edward Henry Farhi of Venice CA (US)]]

[[:Category:Hartmut Neven of Malibu CA (US)|Hartmut Neven of Malibu CA (US)]][[Category:Hartmut Neven of Malibu CA (US)]]

==CLASSIFICATION USING QUANTUM NEURAL NETWORKS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240296359 titled 'CLASSIFICATION USING QUANTUM NEURAL NETWORKS

The abstract describes a method for training a classifier on a quantum computer by preparing qubits in an input state, applying quantum gates to transform the state, determining the predicted classification, comparing it with the known classification, and updating the gates accordingly.

* Training a classifier on a quantum computer
* Preparing qubits in an input state with a known classification
* Applying parameterized quantum gates to transform the state
* Determining predicted classification using readout qubits
* Updating gate parameters based on the comparison

Potential Applications:
- Quantum machine learning
- Quantum data analysis
- Quantum pattern recognition

Problems Solved:
- Enhancing classification accuracy
- Leveraging quantum computing power for machine learning tasks

Benefits:
- Improved classification performance
- Faster processing speeds
- Harnessing quantum computing capabilities

Commercial Applications:
Title: Quantum Machine Learning Solutions for Enhanced Data Analysis
Description: This technology can be applied in industries such as finance, healthcare, and cybersecurity for more accurate data analysis and pattern recognition, leading to better decision-making processes.

Prior Art:
Researchers can explore existing patents and scientific literature on quantum machine learning, quantum classifiers, and quantum gates to understand the advancements in this field.

Frequently Updated Research:
Stay updated on the latest developments in quantum machine learning algorithms, quantum gate optimization techniques, and quantum computing hardware advancements to enhance the performance of classifiers. 

Questions about Quantum Machine Learning:
1. How does quantum machine learning differ from classical machine learning methods?
2. What are the key challenges in implementing quantum classifiers on quantum computing systems?


==Original Abstract Submitted==

this disclosure relates to classification methods that can be implemented on quantum computing systems. according to a first aspect, this specification describes a method for training a classifier implemented on a quantum computer, the method comprising: preparing a plurality of qubits in an input state with a known classification, said plurality of qubits comprising one or more readout qubits; applying one or more parameterised quantum gates to the plurality of qubits to transform the input state to an output state; determining, using a readout state of the one or more readout qubits in the output state, a predicted classification of the input state; comparing the predicted classification with the known classification; and updating one or more parameters of the parameterised quantum gates in dependence on the comparison of the predicted classification with the known classification.



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