Discover use cases

Quantum Computing for Flight Trajectory Optimization


Acubed is the Silicon Valley innovation center of Airbus
San Jose, California
Walkthrough Video

Solving complex optimization problems like flight path optimization provides the aviation industry with new ways to help diminish its carbon footprint while providing more value for airlines.


Tackling the intractablility of flight optimization

qBraid worked with Acubed on flight trajectory optimization where our research and platform was used for net-zero carbon aviation research.

Major players in the global aerospace industry are shifting their focus toward achieving net carbon-neutral operations by 2050. A considerable portion of the overall carbon emission reduction is expected to come from new aircraft technologies, such as flight path optimization. In pursuing these sustainability objectives, we delve into the capacity of quantum computing to tackle computational challenges associated with flight path optimization, an essential operation within the aerospace engineering domain with important ecological and economic considerations. In recent years, the quantum computing field has made significant strides, paving the way for improved performance over classical algorithms. In order to effectively apply quantum algorithms in real-world scenarios, it is crucial to thoroughly examine and tackle the intrinsic overheads and constraints that exist in the present implementations of these algorithms. Our study delves into the application of quantum computers in flight path optimization problems and introduces a customizable modular framework designed to accommodate specific simulation requirements. We examine the running time of a hybrid quantum-classical algorithm across various quantum architectures and their simulations on CPUs and GPUs. A temporal comparison between the conventional classical algorithm and its quantum-improved counterpart indicates that achieving the theoretical speedup in practice may necessitate further innovation. We present our results from running the quantum algorithms on IBM hardware and discuss potential approaches to accelerate the incorporation of quantum algorithms within the problem domain.

Learn more here

Read the paper here

Try qBraid
What we did
Tokyo, Japan

Quantum Chemistry Benchmarking with NVIDIA GPUs on qBraid

San Francisco, California

Intel Quantum SDK Version 1.0

Chicago, Illinois



| Quantum

A web-based IDE interface that provides software tools for researchers and developers in quantum, as well as access to quantum hardware, GPUs, and CPUs.
The qBraid-SDK is a Python toolkit for cross-framework abstraction, transpilation, and execution of quantum programs on hardware and simulators.
Harness 20+ quantum computers and simulators including IBM, QuEra, Oxford Quantum Circuits, Rigetti and Amazon Braket as well as flexible CPUs and GPUs.