Variational Quantum Parameter Screening With Uncertainty Prediction

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Solution Overview

Problem

The high computational cost and time required for parameter optimization in variational quantum algorithms (VQAs) create a bottleneck, leading to inefficient use of resources and delays in executing quantum programs.

Innovation Solution

A system and method that utilizes a machine learning model to predict the usability of defined parameters for VQAs, allowing for accelerated parameter optimization by bypassing or utilizing defined parameters based on uncertainty thresholds, thereby reducing the need for extensive optimization processes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If extensive parameter optimization processes are performed for variational quantum algorithms, then the quality and accuracy of quantum program execution is improved, but the computational time and resource consumption increase significantly

Engineering Contradiction:

Improvequality of quantum program executionVSAvoidcomputational time for parameter optimization

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by using machine learning models to predict parameter usability and uncertainty thresholds before executing the full optimization process. This allows the system to determine in advance whether defined parameters can be directly used or if optimization is necessary, avoiding unnecessary computational time while ensuring quality through predictive assessment.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The machine learning model acts as an intermediary between parameter definition and quantum algorithm execution. It assesses the usability of defined parameters and provides uncertainty predictions that mediate the decision-making process, enabling the system to skip optimization steps when confidence is high while maintaining quality standards.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If extensive parameter optimization processes are performed for variational quantum algorithms, then the accuracy of quantum computations is improved, but the computational resources consumed increase

Engineering Contradiction:

Improveaccuracy of quantum computationsVSAvoidcomputational resources for parameter optimization

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary assessment using machine learning models to evaluate parameter usability and uncertainty before committing to resource-intensive optimization processes. This predictive approach allows the system to allocate computational resources only when necessary, maintaining accuracy while reducing overall resource consumption by skipping optimization for parameters that can be directly used.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The machine learning model enables the system to self-assess parameter suitability and make autonomous decisions about whether optimization is needed. This self-service mechanism reduces reliance on exhaustive optimization by using predictive insights to guide resource allocation, thereby maintaining computational accuracy while minimizing resource expenditure.

Inventive Principle:

Principle #25Self-service

3Productivity

If defined parameters are directly used for variational quantum algorithms without optimization, then the execution speed is improved, but the parameter usability and reliability may be compromised

Engineering Contradiction:

Improveexecution speed of quantum algorithmVSAvoidparameter usability

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The machine learning model serves as an intermediary that assesses the reliability and usability of defined parameters before they are used in the quantum algorithm. It provides uncertainty predictions that mediate between the desire for fast execution and the need for reliable parameters, enabling the system to directly use parameters when confidence is high while maintaining speed.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system incorporates feedback loops where the machine learning model continuously learns from the performance of defined parameters and adjusts its uncertainty predictions accordingly. This feedback mechanism allows the system to confidently use defined parameters for fast execution when the model is well-calibrated, while falling back to optimization when uncertainty indicates potential reliability issues.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12468977B2Uncertainty aware parameter provision for a variational quantum algorithm

Publication Date: 2025.11.11 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Systems, computer-implemented methods and/or computer program products that can facilitate providing a defined parameter, determining whether to employ the defined parameter for a variational quantum algorithm, and running the variational quantum algorithm on a quantum system, are provided. According to an embodiment, a 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 comprise a decision component that determines, based upon an uncertainty prediction regarding the usability of the defined parameter that has been output from a machine learning model, whether to employ the defined parameter in a variational quantum algorithm, such as run on a quantum system.