Google llc (20240296359). CLASSIFICATION USING QUANTUM NEURAL NETWORKS simplified abstract
Contents
CLASSIFICATION USING QUANTUM NEURAL NETWORKS
Organization Name
Inventor(s)
Edward Henry Farhi of Venice CA (US)
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 a predicted classification, comparing it with the known classification, and updating parameters based on the comparison.
- Training a classifier on a quantum computer
- Preparing qubits in an input state with a known classification
- Applying quantum gates to transform the state
- Determining a predicted classification using readout qubits
- Updating parameters based on the comparison of predicted and known classifications
Potential Applications: - Quantum machine learning - Quantum data analysis - Quantum pattern recognition
Problems Solved: - Enhancing classification accuracy on quantum systems - Improving quantum computing capabilities in data analysis
Benefits: - Faster and more accurate classification on quantum computers - Enhanced performance in complex data analysis tasks
Commercial Applications: Title: Quantum Machine Learning Solutions for Enhanced Data Analysis This technology can be applied in industries such as finance, healthcare, and cybersecurity for advanced data analysis and pattern recognition tasks, leading to more efficient decision-making processes.
Questions about Quantum Classification Methods: 1. How does quantum computing enhance classification accuracy compared to classical methods?
Quantum computing utilizes superposition and entanglement to process information in parallel, allowing for more complex calculations and potentially higher accuracy in classification tasks.
2. What are the key challenges in implementing quantum classifiers in real-world applications?
Some challenges include error rates in quantum systems, scalability issues, and the need for specialized knowledge in quantum algorithms and programming languages.
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.
