Aerospace engineers often spend weeks waiting for MATLAB’s genetic algorithm function, called ga, to finish large design runs. Many times the process stops early at local solutions and never finds the best result.
MATLAB now supports more than 1,195 GPU-accelerated functions. Yet its optimization methods like genetic algorithms and pattern search still slow down on complex aerospace problems.
The BQPhy toolbox fixes this problem. It uses the QIO(Quantum-Inspired Optimizer) to run many evaluations at the same time on CPUs, GPUs, or HPC clusters. Tests show it can be up to 3.9 times faster than MATLAB’s genetic algorithm and use 75 percent fewer evaluations.
For Simulink users, this means reducing simulation time from months to days without changing any code or buying new hardware. Setup takes only two minutes from MATLAB File Exchange. Once installed, BQPhy works inside MATLAB like a built-in toolbox.
In this blog, we will explain why MATLAB’s solvers slow down, how the QIEO™ optimizer speeds them up, and how to install and scale it for aerospace and defense projects.
The MATLAB Optimization Bottleneck
The main reason MATLAB slows down is how the built-in solvers test each solution one by one.
Functions like ga, patternsearch, and particleswarm run sequentially. Each step waits for the previous one to finish. If one evaluation takes 15 minutes, a 5,000-step run can take weeks, even on a 200-core cluster.
Complex aerospace problems make this worse. Topology and trajectory optimization have many local solutions. Noise from sensors, manufacturing, or the environment can also force repeated evaluations. Because of this, engineers often reduce the number of tests or accept results that are only “good enough.”
Why MATLAB slows down:
- Sequential evaluations limit speed.
- Many local solutions make global search difficult.
- Real-world noise increases repeated runs.
How BQPhy solves it:
- Explore solutions more efficiently.
- Runs many evaluations at the same time on CPU, GPU, or HPC clusters.
- Cuts total evaluations and time while improving results.
In one airfoil test, MATLAB’s genetic algorithm needed 4,500 evaluations and 90 minutes. QIO finished in 1,150 evaluations and 23 minutes while improving the result from 92 to 98 percent.
Installing & Calling BQPhy Toolbox
BQPhy integrates into existing MATLAB workflows through a single toolbox download from File Exchange—no Python bridges, no container orchestration, no IT tickets. MATLAB Parallel Server's licensing model supports unlimited scaling across cluster nodes without per-node licenses, and BQPhy inherits this architecture seamlessly. Once installed, the bqphy_qieo() function appears in your command window autocomplete alongside native optimizers.
Installation Steps:
- Navigate to MATLAB File Exchange and search "BQPhy"
- Click "Add to MATLAB" (requires R2020b or later)
- Verify installation: which bqphy_qieo should return the toolbox path
- Configure QIEO options: qieo_opts = bqphy_options('MaxIterations', 100, 'DeltaTheta', 0.3);
The toolbox respects MATLAB's existing parallel pool settings. If you've already configured parpool for your HPC environment or cloud cluster, QIEO™ automatically distributes population evaluations across available workers. In R2025a and later, you can develop parallel code locally and scale to clusters without recoding—BQPhy leverages this capability natively, so your laptop prototypes translate directly to production HPC runs.
Installing & Calling BQP Toolbox
BQP integrates easily into your existing MATLAB workflow. You only need to download a single toolbox from MATLAB File Exchange. There is no need for Python bridges, containers, or IT support tickets.
The toolbox works with MATLAB Parallel Server, which allows scaling across cluster nodes without extra per-node licenses. BQP uses this setup to run smoothly on your current system. Once installed, the bqp_qio() function appears in MATLAB autocomplete alongside MATLAB’s built-in solvers.
The toolbox respects MATLAB’s existing parallel pool settings. If you have already set up a parpool for your HPC cluster or cloud, QIo will automatically distribute evaluations across all available workers.
In newer MATLAB versions, you can develop parallel code locally and run it directly on larger HPC clusters without changing your scripts. This means your laptop prototypes can scale to production systems seamlessly.
You can also watch a 3-minute video walkthrough to see installation and first use in action:
BQPhy Toolbox Installation & First Run
Drop-In Replacement Syntax
BQP’s function works just like MATLAB’s built-in optimizers. If you have used ga() or fmincon() before, the syntax will feel familiar. You can switch to BQP without rewriting your models or changing your code.
The solver handles constraints and bounds directly, including:
- Nonlinear constraints
- Upper and lower bounds
- Integer variables
QIo runs many evaluations at the same time, making it faster than sequential solvers. It also provides a stats output for each iteration, showing population diversity, constraint satisfaction, and parallel efficiency.
The Delta Theta parameter controls how the solver explores the design space. Lower values focus on refining solutions, and higher values explore more widely. Evaluating constraints in parallel removes the slow sequential checks that limit MATLAB solvers, especially for expensive simulations.
This setup lets aerospace and defense teams test more designs faster while improving solution quality.
Performance Benchmarks: QIO vs MATLAB Natives
Synthetic test problems often hide the real differences in solver performance. These benchmarks come from actual aerospace optimization tasks run on a 64-core AMD EPYC cluster with NVIDIA A100 GPUs.
QIo reduces evaluations by 70–75 percent and improves solution quality. Parallel execution speeds up computations as more cores are added. Feasibility-preserving operators keep constraints satisfied, avoiding wasted runs.
These results show QIo is faster and more reliable. It explores large design spaces without getting stuck in local optima. Engineers can test more designs in less time and achieve better solutions than with MATLAB’s native solvers.
Top 10 Applications of MATLAB + BQP
BQP and QIo can speed up many complex aerospace and defense optimization tasks. These applications show how engineers can explore more options, test designs faster, and get better results.
- Electric Propulsion Battery Sizing
Optimize battery pack layout, thermal management, and charging profiles at the same time. QIo handles multiple objectives like energy density, cycle life, and cost, while working directly with Simulink battery models.
- Hypersonic Vehicle Aerothermal Design
Use CFD meshes from Fluent and parameterize leading-edge shapes in MATLAB. Parallel QIo evaluations test hundreds of nozzle and inlet combinations in hours, not months.
- Multi-Agent Mission Planning
Optimize UAV swarms and defense logistics. QIo ensures collision avoidance and communication limits while planning waypoints and timing. Works directly with Simulink for real-time hardware-in-the-loop testing.
- Composite Layup Optimization
Find the best ply angles, thickness, and stacking sequence for airframes. Coupled with ANSYS, QIo reduces evaluations from 10,000 to under 2,000 and discovers lighter designs than gradient methods.
- Satellite Constellation Design
Optimize orbit slots, altitude, inclination, and coverage. QIo handles discrete variables and trade-offs across 50+ satellites, minimizing launch costs and revisit times.
- Gas Turbine Combustor Tuning
Optimize fuel injector patterns, air staging, and cooling. Parallel QIo runs update surrogate models in real time, learning from physical test data.
- Radar Waveform Design
Shape low-probability-of-intercept pulses while keeping detection range. QIo explores high-dimensional, non-convex spaces without getting trapped in local optima.
- Landing Gear Topology Optimization
QIo discovers load-path-optimized structures that reduce weight by 30–40%. Outputs feed directly to 3D printers for additive manufacturing.
- Thrust Vectoring Control Laws
Tune PID gains and feedforward schedules across flight envelopes. QIo tests thousands of combinations in parallel, including Monte Carlo wind and failure scenarios.
- Supply Chain Logistics
Optimize stockpile locations, transport routes, and reorder policies under uncertainty. QIo handles large combinatorial problems that stall MATLAB’s native solvers.
Explore detailed case studies for these applications in BQP Aerospace Use Cases.
Scaling to HPC Clusters
BQP is built on MATLAB Parallel Computing Toolbox, so any parfor-enabled workflow automatically distributes QIo evaluations. Your code can run on desktops, clusters, or cloud systems without changes. Engineers can start locally to test and debug objective functions and constraints before moving to larger HPC setups.
When scaling to clusters, QIo works seamlessly across multiple environments:
- Azure HPC clusters or AWS EC2 Spot instances
- Google Cloud TPUs
- On-premises HPC systems
For expensive simulations, BQP supports adaptive surrogate models. These train on early iterations with Gaussian process models and validate periodically with real physics. This reduces the number of full evaluations while keeping results accurate.
QIo also writes per-iteration checkpoints to shared storage. If a run is interrupted, you can resume without starting over. Real HPC setups often use 200+ workers, and BQP scales linearly as more cores are added.
BQP is ready for quantum hardware. Existing QIo workflows can switch to hybrid quantum-classical execution with a simple configuration flag. No algorithm changes are needed, so teams gain quantum benefits while running classical optimization today.
Onboard Now: From Script to Results
Getting started with BQP and QIo is fast and easy. You can integrate it directly into your existing MATLAB workflows without rewriting code.
Integration Checklist:
- Install the BQP toolbox from MATLAB File Exchange (takes 2 minutes)
- Replace your old ga() or particleswarm() calls with bqphy_qio()
- Set the DeltaTheta parameter based on your problem: 0.2 for smooth, 0.4 for rugged landscapes
- Configure your parallel pool for local or cluster runs
- Run a baseline comparison to see time and evaluation improvements
- Enable adaptive surrogate models for expensive simulations
- Review the stats output for convergence and constraint satisfaction
Watch the video walkthrough to see BQPhy Installation & First MATLAB Optimization Run
Start accelerating your aerospace design workflows today:
Start Free Trial – Full BQPhy toolbox access, no credit card
Book a Demo – Live walkthrough with your existing MATLAB models
Why BQP for MATLAB Teams?
BQP is designed to work wherever MATLAB runs. QIo can execute on Windows laptops, Linux HPC clusters, macOS machines, or cloud platforms like AWS, Azure, and GCP. There is no need for Python layers, Docker containers, or separate license servers.
It is production-ready and handles real aerospace constraints, including manufacturing tolerances, regulatory limits, and physics-based simulations.
QIo delivers faster results across your entire design cycle. Topology optimization that once took months now finishes in weeks, and mission planning tasks that required overnight cluster runs can complete in hours. Engineers can explore larger design spaces and discover better solutions faster.
Key Benefits:
- Hardware-agnostic and integrates naturally with MATLAB
- Up to 3.9x speedups over genetic algorithms
- Scales from local machines to HPC clusters and cloud environments
BQP helps your team remove bottlenecks and improve design efficiency without rewriting code. Start accelerating your workflows today.Book a Demo now !!!
Frequently Asked Questions
1. What is QIO and how does it differ from MATLAB’s native optimizers?
QIo is BQP’s quantum-inspired optimizer. Unlike MATLAB’s ga(), patternsearch(), or particleswarm(), QIo evaluates candidate solutions in parallel across CPUs, GPUs, or HPC clusters. This reduces function evaluations and improves solution quality, especially for high-dimensional, non-convex aerospace optimization problems.
2. Do I need to rewrite my MATLAB or Simulink models to use BQP?
No. BQP integrates directly into existing MATLAB workflows. You can replace your old ga() or particleswarm() calls with bqphy_qio() and keep the same constraints, bounds, and objective functions. It respects your current parallel pool settings and scales from local machines to HPC or cloud environments without code changes.
3. Can BQP handle large-scale aerospace simulations and noisy data?
Yes. BQP supports adaptive surrogate models for expensive simulations and maintains feasibility for nonlinear constraints. It handles noisy data, environmental variability, and real-world aerospace design limits, helping teams explore larger design spaces and find better solutions faster.
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