Native MATLAB Access to BQPhy®: Enabling Practical Quantum-Inspired Optimization in Aerospace and Defense

In this technical demonstration, Rut Lineswala, CTO and Founder of BQP, explained how engineers can access and execute BQPhy® directly through MATLAB. The session focused on enabling advanced optimization capabilities without disrupting existing engineering workflows.

This integration represents a significant step for aerospace and defense teams seeking scalable optimization methods that go beyond classical evolutionary algorithms.

Why This Integration Matters

Optimization challenges in aerospace and defense are becoming increasingly complex.  Design spaces are high-dimensional, constraints are tight, and simulation-driven decision-making demands faster convergence.

MATLAB remains a core engineering platform across these industries. By enabling native access to BQPhy® within MATLAB, BQP removes the friction traditionally associated with adopting next-generation optimization techniques.

Engineers can now apply quantum-inspired optimization using familiar syntax, tools, and environments.

Overview

The demonstration presented by Rut Lineswala uses a sphere function optimization problem, a standard benchmark in optimization studies.

The Sphere function minimizes the sum of squared design variables across multiple dimensions. Its global minimum is known and occurs when all variables converge to zero.

This example was chosen to clearly illustrate algorithm behavior, convergence characteristics, and result fidelity—without unnecessary complexity.

Problem Definition in Simple Terms

The optimization problem is defined with:

• 10 design variables (dimensions)
• Lower bounds of –10 and upper bounds of +10
• A continuous design space

The objective is to minimize the function value until it approaches zero.

This structure closely mirrors real-world aerospace design problems, such as parameter tuning, trajectory optimization, and system-level trade studies.

Classical Genetic Algorithm as a Baseline

The first part of the workflow uses MATLAB’s built-in Genetic Algorithm (GA).

Key parameters such as:

• Population size
• Number of generations
• Variable bounds

are explicitly defined.

The algorithm executes successfully and converges close to the known optimal solution.
This establishes a reliable baseline for performance and accuracy.

The full walkthrough of this workflow, including MATLAB execution and result comparison, is available in the video below.

 This video provides a clear, step-by-step explanation of how BQPhy® is configured and executed inside MATLAB.

Quantum-Inspired Optimization with BQPhy®

The second phase of the demonstration introduces BQPhy®’s Quantum-Inspired Optimization (QIO).

While the structure resembles classical evolutionary algorithms, several important enhancements are evident.

All optimization inputs are passed as named parameters, allowing flexibility in configuration. The order of inputs is no longer restrictive, improving usability and reducing setup errors.

BQPhy® also introduces Delta Theta, a quantum-inspired control parameter. This serves a role analogous to mutation and crossover in genetic algorithms, enabling more efficient exploration of the design space.

Native Parallel Execution in MATLAB

A key advantage highlighted in the demonstration is native parallelization. BQPhy® executes population members concurrently, depending on available system resources. This capability is available without modifying MATLAB itself.

For aerospace and defense applications, this directly translates into faster optimization cycles and improved scalability for large, multi-dimensional problems.

Performance and Result Fidelity

Both the classical Genetic Algorithm and BQPhy®’s Quantum-Inspired Optimization converge to near-zero objective values.

This confirms:

• Correctness of the solution
• Comparable optimization fidelity

At the same time, BQPhy® offers enhanced configurability and execution efficiency, positioning it as a practical next step beyond classical methods.

Relevance for Aerospace and Defense Engineering Teams

BQPhy®’s MATLAB integration is designed for operational engineering environments—not experimental research alone.

It enables teams to:

• Extend existing MATLAB workflows
• Scale optimization for complex systems
• Reduce computation time without rewriting models
• Adopt quantum-inspired techniques incrementally and safely

This is particularly valuable for organizations balancing innovation with reliability and compliance.

Advancing Optimization Without Disrupting Workflows

As system complexity continues to increase, optimization tools must evolve accordingly.
BQPhy® brings quantum-inspired methods into everyday engineering practice—directly within MATLAB.

As demonstrated by Rut Lineswala, accessing BQPhy® does not require new platforms, new languages, or workflow changes. It simply extends what engineers can already do—more efficiently and at scale.

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