18448734. Training Quantum Evolutions Using Sublogical Controls simplified abstract (Google LLC)
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 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Commercial Applications
- 1.9 Prior Art
- 1.10 Frequently Updated Research
- 1.11 Questions about Quantum Evolutions Training using Sub-Logical Controls
- 1.12 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 18448734 titled 'Training Quantum Evolutions Using Sublogical Controls
Simplified Explanation
This patent application describes methods, systems, and apparatus for training quantum evolutions using sub-logical controls. It involves accessing quantum hardware with multi-level quantum subsystems, initializing the quantum system in an initial state, obtaining quantum system observables and target quantum states, and iteratively training until a completion event occurs.
- Accessing quantum hardware with multi-level quantum subsystems
- Initializing the quantum system in an initial state
- Obtaining quantum system observables and target quantum states
- Iteratively training until a completion event occurs
Potential Applications
This technology could be applied in quantum computing, quantum communication, quantum sensing, and quantum simulation.
Problems Solved
This technology addresses the challenges of training quantum evolutions using sub-logical controls, optimizing quantum systems for specific tasks, and improving the efficiency of quantum operations.
Benefits
The benefits of this technology include enhanced control over quantum systems, improved performance in quantum tasks, and advancements in quantum technology research and development.
Commercial Applications
Potential commercial applications of this technology include quantum computing software development, quantum hardware optimization services, and quantum algorithm design consulting for various industries.
Prior Art
Readers interested in prior art related to this technology could explore research papers on quantum control, quantum machine learning, and quantum optimization algorithms.
Frequently Updated Research
Stay updated on the latest research in quantum control theory, quantum hardware advancements, and quantum algorithm optimization techniques to enhance the application of this technology.
Questions about Quantum Evolutions Training using Sub-Logical Controls
What are the key challenges in training quantum evolutions using sub-logical controls?
Training quantum evolutions using sub-logical controls faces challenges such as optimizing control parameters, minimizing decoherence effects, and achieving high-fidelity quantum operations.
How does this technology improve the efficiency of quantum operations?
This technology improves the efficiency of quantum operations by iteratively training quantum systems to achieve desired quantum states, enhancing control over quantum subsystems, and optimizing quantum evolutions for specific tasks.
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.
