18648052. CLASSIFICATION USING QUANTUM NEURAL NETWORKS simplified abstract (GOOGLE LLC)
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 18648052 titled 'CLASSIFICATION USING QUANTUM NEURAL NETWORKS
The patent application 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 the gates accordingly.
- Method for training a classifier on a quantum computer
- Preparation of qubits in an input state with known classification
- Application of parameterized quantum gates to transform the state
- Determination of predicted classification using readout qubits
- Comparison of predicted classification with known classification
- Updating of quantum gate parameters based on the comparison
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: - Increased efficiency in classification tasks - Potential for faster and more accurate predictions - Advancement in quantum computing technology
Commercial Applications: Title: Quantum Machine Learning Solutions Description: This technology can be utilized in industries such as finance, healthcare, and cybersecurity for advanced data analysis and pattern recognition, leading to improved decision-making processes and outcomes.
Questions about Quantum Classification Methods: 1. How does this method differ from traditional machine learning algorithms?
This method leverages the principles of quantum mechanics to perform classification tasks, offering the potential for exponential speedup compared to classical algorithms.
2. What are the key challenges in implementing quantum classifiers in real-world applications?
The main challenges include error correction, scalability, and hardware limitations in current 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.
