Preventing Satellite Collisions with Faster Predictive Modeling

How many satellites can we safely and efficiently place in orbit—especially in a world responding to rapidly evolving geopolitical and environmental demands? With the rapid proliferation of spacecraft and the growing threat of orbital debris, predictive modeling has become the backbone of mission planning and satellite safety. Learn more about quantum optimization for satellite imaging mission planning

However, traditional classical computing systems and algorithms are built on classical physics and numerical approximations. These are no longer fast or flexible enough to handle the complexity of today’s space environment.

BQP’s quantum-powered simulation platform, BQPhy®, is advancing predictive modeling by delivering faster, more adaptive capabilities that account for uncertainty, real-time variability, and orbital congestion. By shifting risk analysis and trajectory forecasting to the pre-launch phase, BQPhy helps satellite operators avoid costly delays, prevent near misses, and proactively mitigate collision risks.

Currently, more than 35,150 objects are tracked in Earth’s orbit, with only about 25% being operational satellites. This number is expected to grow to over 100,000 spacecraft by 2030, significantly amplifying the computational burden and collision risk for satellite operators. Traditional predictive systems are reaching their limits—unable to process data at speed or adapt to rapidly evolving threats in real time.

Three Key Challenges Undermining associated with limiting predictability

• Unforeseen orbital events
• Inconsistent or incompatible risk analyses
• Divergent thresholds for acceptable risk

Events such as satellite breakups from battery failures or propulsion anomalies are becoming more frequent—and more unpredictable. Collision assessments can vary widely between models, leading to indecision, delayed maneuvers, or false alarms. Operators typically act when the probability of collision exceeds 1 in 10,000, but even risks approaching 1 in 5 have gone unaddressed due to model disagreement and processing delays.

Compounding Factors of Uncertainty in predictive modelling

• Model bias from oversimplified assumptions
• Sensor noise and tracking inaccuracies
• Uncertainty propagation over longer time horizons
• Solver limitations in computing close approaches

In such a volatile landscape, even millimeter-sized debris can disable a small satellite. These platforms—critical for communications, weather monitoring, and national security—have tight fuel budgets and limited maneuverability. Every decision to alter course must be justified by accurate, timely predictive modeling.

Problem Formulation: Predicting Rogue Celestial Object Hazards

Space is not empty—it’s teeming with rogue celestial objects like meteorites and asteroids that can collide or fragment, creating secondary threats. 

Data sources: Deep-space satellites gather imaging and radar data on these bodies, which is then transmitted to Earth for astrometrical analysis.

Astrometric Data processing for problem formulation:

• Two-body orbital solutions
• Kepler’s laws for elliptical motion

Key parameters involved in predictive modelling:

• Absolute magnitude
• Diameter
• Albedo (reflectivity)
• Minimum Orbit Intersection Distance (MOID)

Let’s denote the extracted data as K.

Probabilistic Collision Prediction Using PINNs

The extracted dataset K is processed through Quantum Assisted Physics-Informed Neural Networks (PINNs) to estimate the probability of collision. These models fuse data-driven learning with physics-based constraints to provide more robust predictions. For a real-world Use case of Optimization in Aerospace with Quantum, see our detailed study.

Inference Function

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• Kₚ (K_phi): Object’s physical characteristics
• Kᵥ (K_vartheta): Orbital characteristics
• Kₑₐᵣₜₕ: Earth’s gravitational and atmospheric parameters

By encoding both empirical data and the laws of celestial mechanics, QA-PINNs offer higher accuracy even in data-sparse environments—a common challenge in early mission planning.

Quantum Machine Learning for Faster, Adaptive Risk Prediction

BQP has developed a Quantum-Assisted Physics-Informed Neural Network (QA-PINN). This architecture combines classical physics-based modeling with quantum-enhanced machine learning to solve critical bottlenecks.

QA-PINN Advantage

BQPhy®’s QA-PINN introduces a quantum layer into the PINN architecture, using parameterized quantum gates as nonlinear feature transformers before the classical layers.

Quantum-enhanced feature extraction: Captures complex orbital relationships with fewer classical parameters.

Faster convergence: Speeds up training time without sacrificing accuracy.

Improved generalization: Especially effective in modeling Adaptive Flight Vehicles (AFVs) and variable mission scenarios.

Application to Satellite Collision Avoidance

QA-PINNs support:

• Near real-time evaluation of orbital trajectories and collision risks.
• Scalable simulations across large satellite constellations and irregular debris fields.
• Pre-launch mission planning, enabling dynamic adjustments during design and deployment.

Integration with CNN and Probabilistic Risk Estimation in LEO

To further enhance accuracy, QA-PINNs are paired with Convolutional Neural Networks (CNNs) for spatial pattern recognition and probabilistic models to refine collision predictions.

This hybrid setup enables:

• Improved covariance tracking over time.
• Identification of high-risk zones using historical patterns.
• Smarter maneuver planning based on robust risk estimations.

Simplified LEO Collision Probability Framework

Collision likelihood is calculated based on the intersection of two spherical bodies at their Closest Point of Approach (CPA). Key assumptions include:

• Linear motion with constant velocity:
  x(t) = x₀ + tv
• Stationary position uncertainty.
• Independent, multivariate statistical distribution of initial positions.
• Uncorrelated uncertainty between objects.

These simplifications enable fast, reliable collision risk assessments within the QA-PINN pipeline.

In real-world tests, BQPhy’s QAPINNs have reduced trainable parameters by 20% while maintaining 99% accuracy for solving complex equations, demonstrating their readiness for deployment in mission-critical space applications.

As orbits grow more congested and mission timelines tighten, the limitations of classical models become untenable. Organizations require smarter, faster tools to stay ahead of emerging threats—and BQP’s quantum-powered approach is setting the new standard.

Experience Quantum-Inspired Optimization Today

Why wait for quantum hardware?

BQPhy® QIEO delivers quantum-like performance on existing HPC/GPU infrastructure—no specialized hardware required.

Join leading satellite operators who are already leveraging BQPhy®, the only platform engineered for space’s complex predictive modeling needs.

See how BQPhy® can:

• Slash mission planning time.
• Reduce collision risks with advanced predictive modeling.
• Align satellite operations with global sustainability and safety goals.
• Leverage Quantum Algorithms for Complex Optimization to tackle the toughest collision-avoidance challenges.

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