Quantum Gate Parametrization for Data Encoding and Variational Learning

Solution Overview

Problem

Conventional quantum machine learning algorithms face inefficiencies and ineffectiveness due to the dichotomy between variational and data-encoding gates, limiting the predictive power of quantum representations.

Innovation Solution

A systematic parametrization scheme is introduced to convert arbitrary quantum gates into data-encoding and variational gates, allowing them to perform both functions, thereby bridging the gap between conventional variational and data-encoding gates.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional variational gates are used without data-encoding capability, then the circuit structure remains simple, but the predictive power of quantum representations is limited

Engineering Contradiction:

Improvepredictive powerVSAvoidgate functionality

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent applies universality by designing quantum gates that can perform both variational optimization and data-encoding functions simultaneously. The parametrization scheme allows any quantum gate to be configured as a variational data-encoding gate, eliminating the need for separate gate types and enabling a single unified gate structure to achieve multiple objectives: representing classical data in quantum space while maintaining variational flexibility for optimization.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Productivity

If separate variational gates and data-encoding gates are used, then the roles are clearly defined, but the system becomes more complex and less efficient

Engineering Contradiction:

Improvetraining efficiencyVSAvoidgate parameterization

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent merges the previously separate functions of variational gates and data-encoding gates into a single integrated gate structure. The parametrization scheme combines variational parameters with data-encoding capabilities, allowing one gate to simultaneously perform both data representation and optimization functions. This consolidation eliminates redundant gate types and reduces overall system complexity while improving training efficiency.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The patent applies universality by designing quantum gates that can perform both variational optimization and data-encoding functions simultaneously. The parametrization scheme allows any quantum gate to be configured as a variational data-encoding gate, eliminating the need for separate gate types and enabling a single unified gate structure to achieve multiple objectives: representing classical data in quantum space while maintaining variational flexibility for optimization.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Reliability

If additional data-encoding gates are added to existing variational circuits, then data representation capability improves, but computational cost increases

Engineering Contradiction:

Improvedata representation qualityVSAvoidcomputational cost

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent applies universality by designing quantum gates that can perform both variational optimization and data-encoding functions simultaneously. The parametrization scheme allows any quantum gate to be configured as a variational data-encoding gate, eliminating the need for separate gate types and enabling a single unified gate structure to achieve multiple objectives: representing classical data in quantum space while maintaining variational flexibility for optimization.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250278653A1Parametrizing a quantum gate into a data-encoding variational gate

Publication Date: 2025.09.04 STATE FARM MUTAL AUTOMOBILE INSURANCE COMPANY

AI summary

A method, program code, and computing device including at least one memory, and at least one processor in communication with the at least one memory, wherein the at least one processor is programmed to generate a quantum feature map corresponding to a quantum circuit, wherein the quantum feature map includes a plurality of gates; employ variational data-encoding parameterization to the quantum feature map to convert at least one gate of the plurality of gates into a variational data-encoded gate; and execute machine learning processes using the quantum feature map to determine a value for a variational data-encoded parameter associated with the at least one variational data-encoded gate.