Monte Carlo Markov Chain Quantum Program Optimization

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Solution Overview

Problem

Existing techniques for optimizing quantum programs rely on ad-hoc heuristics and manual efforts, limiting their ability to achieve aggressive optimizations while ensuring program correctness, and fail to efficiently generate more optimized quantum programs that execute in shorter time with fewer resources.

Innovation Solution

A method using Monte Carlo Markov Chain based transformations to generate mutants of quantum programs, evaluating their quality and correctness through amplitude vectors and cost calculations, and iteratively replacing the original program with optimized mutants that meet acceptance and storage thresholds, thereby optimizing quantum programs for faster execution and resource usage.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If ad-hoc heuristics and manual efforts are used for quantum program optimization, then program correctness can be maintained, but optimization efficiency and execution speed are limited

Engineering Contradiction:

Improveprogram correctnessVSAvoidoptimization efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs self-optimization through automated Monte Carlo Markov Chain processes that generate and evaluate mutant quantum programs without continuous human intervention. The optimization framework autonomously iterates through mutations, evaluations, and selections to improve quantum program performance while maintaining correctness.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system systematically varies program parameters by generating mutant versions of quantum programs with modified gate sequences, operations, or structures. These parameter changes explore the solution space to find optimized configurations that maintain correctness while improving execution efficiency.

Inventive Principle:

Principle #35Parameter changes

2Loss of time

If aggressive optimizations are applied to quantum programs, then execution time and resource usage are reduced, but ensuring program correctness becomes more difficult

Engineering Contradiction:

Improveexecution timeVSAvoidprogram correctness

Core Design Contradiction:

Loss of timeVSReliability

Solution Approach 1:

The system implements feedback mechanisms where each mutant quantum program is evaluated for both correctness and quality. The evaluation results feed back into the selection process, allowing only mutants that maintain correctness (through amplitude vector comparison) while improving performance to be accepted into the optimized program sequence.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system generates numerous temporary mutant program versions that are evaluated and discarded if they don't meet criteria. These disposable mutant versions allow aggressive exploration of optimization possibilities without permanently compromising the original program's correctness, as each mutant is a temporary test case.

Inventive Principle:

Principle #27Cheap short-living objects (Disposable)

3Reliability

If manual optimization techniques are used, then program correctness can be verified, but the complexity and time required for optimization increases

Engineering Contradiction:

Improveprogram correctnessVSAvoidoptimization process complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system replaces manual mechanical optimization processes with automated computational methods. Monte Carlo Markov Chain algorithms automatically generate, evaluate, and select optimizations, substituting human manual analysis and verification with algorithmic processes that handle complexity while maintaining correctness through systematic amplitude vector comparisons.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Productivity

If existing optimization methods are applied, then some performance improvement is achieved, but the ability to efficiently generate highly optimized quantum programs is insufficient

Engineering Contradiction:

Improveoptimization outputVSAvoidoptimization time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-generating multiple mutant quantum programs and evaluating their quality scores before final selection. This preliminary exploration and pre-evaluation of candidate optimizations allows the system to identify high-potential mutants early, reducing the time needed for final optimization decisions while improving overall optimization output.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11651232B2Monte Carlo Markov chain based quantum program optimization

Publication Date: 2023.05.16 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

From a quantum program a first mutant is generated using a processor and a memory, where the first mutant is a randomly-generated transformation of the quantum program. A quality score, a correctness distance, and a probability of acceptance corresponding to the first mutant are computed. An acceptance corresponding to the first mutant is determined according to the probability of acceptance. Upon determining that an acceptance of the first mutant corresponding to the probability of acceptance exceeds an acceptance threshold, the quantum program is replaced with the first mutant. Upon determining that the quality score exceeds a storage threshold and that the correctness distance is zero, the first mutant is stored. These actions are iterated until reaching an iteration limit.