International business machines corporation (20240193448). SHORT-DEPTH ACTIVE LEARNING QUANTUM AMPLITUDE ESTIMATION WITHOUT EIGENSTATE COLLAPSE simplified abstract

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 20240193448 titled 'SHORT-DEPTH ACTIVE LEARNING QUANTUM AMPLITUDE ESTIMATION WITHOUT EIGENSTATE COLLAPSE

Simplified Explanation: The patent application describes techniques and a system to estimate a quantum phase, specifically an expectation value of a quantum state, using a hybrid of quantum and classical methods.

• Key Features and Innovation:

   * Utilizes a hybrid approach of quantum and classical methods for quantum phase estimation.
   * System includes a memory storing computer executable components and a processor executing these components.
   * Encoding component encodes an expectation value of a quantum state.
   * Learning component uses stochastic inference to determine the expectation value based on an uncollapsed eigenvalue pair.

• Potential Applications:

   * Quantum computing
   * Quantum information processing
   * Quantum simulations

• Problems Solved:

   * Facilitates accurate estimation of quantum phase
   * Enhances quantum state analysis
   * Improves efficiency of quantum algorithms

• Benefits:

   * Increased accuracy in quantum phase estimation
   * Enhanced quantum state analysis capabilities
   * Improved performance of quantum algorithms

• Commercial Applications:

   * Quantum computing software development
   * Quantum cryptography systems
   * Quantum machine learning applications

• Prior Art:

   Prior art related to this technology may include research on quantum phase estimation algorithms, quantum state encoding techniques, and stochastic inference methods in quantum computing.

• Frequently Updated Research:

   Ongoing research in quantum algorithms, quantum error correction, and quantum machine learning may be relevant to this technology.

Questions about Quantum Phase Estimation: 1. What are the potential challenges in implementing a hybrid quantum-classical approach for quantum phase estimation? 2. How does the use of stochastic inference improve the accuracy of determining expectation values in quantum states?

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.