18193082. SHORT-DEPTH ACTIVE LEARNING QUANTUM AMPLITUDE ESTIMATION WITHOUT EIGENSTATE COLLAPSE simplified abstract (International Business Machines Corporation)

SHORT-DEPTH ACTIVE LEARNING QUANTUM AMPLITUDE ESTIMATION WITHOUT EIGENSTATE COLLAPSE

Organization Name

International Business Machines Corporation

Inventor(s)

Ismail Yunus Akhalwaya of Emmarentia (ZA)

Kenneth Clarkson of Madison NJ (US)

Lior Horesh of North Salem NY (US)

Mark Squillante of Greenwich CT (US)

Shashanka Ubaru of Ossining NY (US)

Vasileios Kalantzis of White Plains NY (US)

SHORT-DEPTH ACTIVE LEARNING QUANTUM AMPLITUDE ESTIMATION WITHOUT EIGENSTATE COLLAPSE - A simplified explanation of the abstract

This abstract first appeared for US patent application 18193082 titled 'SHORT-DEPTH ACTIVE LEARNING QUANTUM AMPLITUDE ESTIMATION WITHOUT EIGENSTATE COLLAPSE

Simplified Explanation

This patent application describes techniques and a system that combine quantum and classical methods to estimate the expectation value of a quantum state. The system includes components for encoding and learning to determine the expectation value based on an uncollapsed eigenvalue pair.

• The system facilitates estimation of a quantum phase or expectation value of a quantum state.
• It utilizes a hybrid of quantum and classical methods.
• Components include an encoding component and a learning component.
• The learning component uses stochastic inference to determine the expectation value based on an uncollapsed eigenvalue pair.

Key Features and Innovation

• Hybrid approach combining quantum and classical methods.
• Encoding component for storing expectation values.
• Learning component utilizing stochastic inference.
• Estimation based on uncollapsed eigenvalue pair.

Potential Applications

This technology can be applied in quantum computing, quantum information processing, and quantum algorithms.

Problems Solved

• Facilitates estimation of quantum phase and expectation values.
• Combines quantum and classical methods for more accurate estimations.

Benefits

• Improved accuracy in estimating quantum states.
• Enhanced efficiency in quantum computations.
• Potential for advancements in quantum algorithms.

Commercial Applications

• Quantum computing software development.
• Quantum information processing systems.
• Research and development in quantum technologies.

Prior Art

Prior art related to this technology can be found in research papers on quantum estimation and quantum algorithms.

Frequently Updated Research

Stay updated on the latest advancements in quantum computing, quantum algorithms, and quantum information processing to enhance the application of this technology.

Questions about Quantum Phase Estimation

How does this technology improve quantum phase estimation compared to traditional methods?

This technology combines quantum and classical methods to provide more accurate estimations of quantum states, leading to improved quantum phase estimation.

What are the potential implications of this technology in the field of quantum computing?

This technology could revolutionize quantum computing by enhancing the efficiency and accuracy of quantum computations, leading to significant advancements in the field.

Original Abstract Submitted

Techniques and a system to facilitate estimation of a quantum phase, and more specifically, to facilitate estimation of an expectation value of a quantum state, by utilizing a hybrid of quantum and classical methods are provided. In one example, a system is provided. The system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include an encoding component and a learning component. The encoding component can encode an expectation value associated with a quantum state. The learning component can utilize stochastic inference to determine the expectation value based on an uncollapsed eigenvalue pair.