20240054379. Parallel Data Processing using Hybrid Computing System for Machine Learning Applications simplified abstract (Rigetti & Co., LLC)
Parallel Data Processing using Hybrid Computing System for Machine Learning Applications
Organization Name
Inventor(s)
Mark James Hodson of Highgate (AU)
Maxwell Phillip Henderson of Philadelphia PA (US)
Parallel Data Processing using Hybrid Computing System for Machine Learning Applications - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240054379 titled 'Parallel Data Processing using Hybrid Computing System for Machine Learning Applications
Simplified Explanation
The abstract describes a method for performing machine learning using data-parallel quantum processing in a hybrid computing system.
- Machine learning model operated in a hybrid computing system
- Quantum computing resource and classical computing resource utilized
- Quantum computing resource includes QPU sublattices with qubit devices
- Quantum logic circuits defined for machine learning model parameters
- Quantum control programs generated and executed on QPU sublattices
- Readout samples obtained and activation parameters calculated based on samples
Potential Applications
- Quantum machine learning
- Hybrid computing systems
- Quantum processing in data-parallel applications
Problems Solved
- Enhancing machine learning performance
- Leveraging quantum computing resources
- Improving efficiency of data-parallel processing
Benefits
- Faster machine learning model training
- Increased accuracy in model predictions
- Utilization of quantum computing capabilities in machine learning applications
Original Abstract Submitted
in a general aspect, a machine learning process is performed using data-parallel quantum processing. in some cases, a machine learning model is operated in a hybrid computing system. the hybrid computing system includes a quantum computing resource and a classical computing resource. the quantum computing resource includes quantum processing unit (qpu) sublattices, each including a subset of qubit devices. methods for operating a machine learning model include defining quantum logic circuits to be executed on the respective qpu sublattices, wherein each quantum logic circuit is configured according to parameters of the machine learning model; translating the quantum logic circuits into quantum control programs for the respective qpu sublattices; determining control parameters for the respective quantum control programs; executing the quantum control programs on the respective qpu sublattices to obtain readout samples from the respective qpu sublattices; and calculating activation parameters of the machine learning model based on the readout samples.
