18175702. QUANTUM CIRCUIT LEARNING SYSTEM, QUANTUM CIRCUIT LEARNING METHOD, QUANTUM INFERENCE SYSTEM, QUANTUM CIRCUIT, AND QUANTUM-CLASSICAL HYBRID NEURAL NETWORK simplified abstract (KABUSHIKI KAISHA TOSHIBA)

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QUANTUM CIRCUIT LEARNING SYSTEM, QUANTUM CIRCUIT LEARNING METHOD, QUANTUM INFERENCE SYSTEM, QUANTUM CIRCUIT, AND QUANTUM-CLASSICAL HYBRID NEURAL NETWORK

Organization Name

KABUSHIKI KAISHA TOSHIBA

Inventor(s)

Yasutaka Nishida of Tama Tokyo (JP)

Fumihiko Aiga of Kawasaki Kanagawa (JP)

QUANTUM CIRCUIT LEARNING SYSTEM, QUANTUM CIRCUIT LEARNING METHOD, QUANTUM INFERENCE SYSTEM, QUANTUM CIRCUIT, AND QUANTUM-CLASSICAL HYBRID NEURAL NETWORK - A simplified explanation of the abstract

This abstract first appeared for US patent application 18175702 titled 'QUANTUM CIRCUIT LEARNING SYSTEM, QUANTUM CIRCUIT LEARNING METHOD, QUANTUM INFERENCE SYSTEM, QUANTUM CIRCUIT, AND QUANTUM-CLASSICAL HYBRID NEURAL NETWORK

Simplified Explanation

The patent application describes a quantum circuit consisting of two blocks, each with gate operation layers and measurement layers. The first block encodes input information to construct the first quantum state, while the second block constructs the second quantum state based on the measurement value of the first quantum state.

  • The quantum circuit includes two blocks: the first block and the second block.
  • The first block has a gate operation layer with encoding gate and transformation gate.
  • The encoding gate is parameterized with an encoding parameter for constructing the first HF state.
  • The transformation gate is parameterized with a learning parameter for transforming the first HF state into the first quantum state.
  • The measurement layer in the first block outputs the measurement value of the first quantum state.
  • The second block also has a gate operation layer with encoding gate and transformation gate.
  • The encoding gate in the second block is parameterized with an encoding parameter for constructing the second HF state.
  • The transformation gate in the second block is parameterized with a learning parameter for transforming the second HF state into the second quantum state.

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      1. Potential Applications

- Quantum computing - Quantum information processing - Quantum cryptography

      1. Problems Solved

- Efficient quantum state construction - Improved quantum state transformation - Enhanced measurement accuracy

      1. Benefits

- Increased quantum computing capabilities - Enhanced security in quantum communication - Potential for faster data processing

      1. Potential Commercial Applications
        1. Advancements in Quantum Computing Technology

- Optimizing quantum algorithms - Developing quantum-resistant encryption - Enhancing quantum machine learning models

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      1. Possible Prior Art

There may be prior art related to quantum circuits, gate operations, and quantum state transformations in the field of quantum computing and quantum information processing.

      1. Unanswered Questions
        1. How does the encoding parameter affect the construction of the HF state in the quantum circuit?

The encoding parameter plays a crucial role in encoding input information for constructing the HF state, but the specific mechanism of this process is not detailed in the abstract.

        1. What are the potential limitations of the transformation gate in converting the HF state into the quantum state?

While the transformation gate is designed to transform the HF state into the quantum state, it is essential to understand any constraints or challenges that may arise during this transformation process.


Original Abstract Submitted

Quantum circuit includes 1st block and 2nd block. 1st block includes gate operation layer and measurement layer. Gate operation layer includes encoding gate parameterized with encoding parameter including encoded input information for constructing 1st HF state, and transformation gate parameterized with learning parameter for transforming 1st HF state into 1st quantum state. Measurement layer outputs measurement value of 1st quantum state. 2nd block includes gate operation layer. Gate operation layer includes 2nd encoding gate parameterized with encoding parameter including encoded measurement value for constructing 2nd HF state, and transformation gate parameterized with learning parameter for transforming 2nd HF state into 2nd quantum state.

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