Google llc (20240193449). Training Quantum Evolutions Using Sublogical Controls simplified abstract
Contents
- 1 Training Quantum Evolutions Using Sublogical Controls
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Training Quantum Evolutions Using Sublogical Controls - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Quantum Evolution Training Systems
- 1.13 Original Abstract Submitted
Training Quantum Evolutions Using Sublogical Controls
Organization Name
Inventor(s)
Ryan Babbush of Venice CA (US)
Hartmut Neven of Malibu CA (US)
Training Quantum Evolutions Using Sublogical Controls - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240193449 titled 'Training Quantum Evolutions Using Sublogical Controls
Simplified Explanation
The patent application describes methods, systems, and apparatus for training quantum evolutions using sub-logical controls. This involves accessing quantum hardware with multi-level quantum subsystems, initializing the quantum system, obtaining quantum system observables and target quantum states, and iteratively training until a completion event occurs.
- Quantum hardware with multi-level quantum subsystems
- Sub-logical controls for training quantum evolutions
- Iterative training process
- Utilization of control parameters related to the physical environment
- Quantum system observables and target quantum states
Key Features and Innovation
- Accessing quantum hardware with multi-level quantum subsystems
- Training quantum evolutions using sub-logical controls
- Iterative training process for quantum systems
- Utilizing control parameters related to the physical environment
- Obtaining quantum system observables and target quantum states
Potential Applications
The technology can be applied in quantum computing, quantum communication, quantum sensing, and quantum information processing.
Problems Solved
The technology addresses the need for efficient training methods for quantum evolutions, especially in complex quantum systems with multi-level quantum subsystems.
Benefits
- Improved training of quantum evolutions
- Enhanced control over quantum systems
- Increased accuracy in achieving target quantum states
Commercial Applications
Title: Quantum Evolution Training Systems for Advanced Quantum Computing Applications The technology can be commercialized in industries such as quantum computing, quantum cryptography, quantum metrology, and quantum simulation.
Prior Art
Further research can be conducted in the field of quantum control and quantum machine learning to explore related prior art.
Frequently Updated Research
Stay updated on advancements in quantum control algorithms and quantum hardware developments for more efficient training of quantum evolutions.
Questions about Quantum Evolution Training Systems
How does sub-logical control differ from traditional control methods in quantum systems?
Sub-logical control in quantum systems involves operating on multi-level quantum subsystems based on control parameters related to the physical environment, offering a more nuanced approach compared to traditional control methods.
What are the potential challenges in implementing quantum evolution training systems in practical applications?
Implementing quantum evolution training systems in practical applications may face challenges related to hardware limitations, calibration issues, and the complexity of quantum algorithms.
Original Abstract Submitted
methods, systems, and apparatus for training quantum evolutions using sub-logical controls. in one aspect, a method includes the actions of accessing quantum hardware, wherein the quantum hardware includes a quantum system comprising one or more multi-level quantum subsystems; one or more control devices that operate on the one or more multi-level quantum subsystems according to one or more respective control parameters that relate to a parameter of a physical environment in which the multi-level quantum subsystems are located; initializing the quantum system in an initial quantum state, wherein an initial set of control parameters form a parameterization that defines the initial quantum state; obtaining one or more quantum system observables and one or more target quantum states; and iteratively training until an occurrence of a completion event.
