Significance of Quantum Computing for Autonomous Vehicles

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April 14, 2023


Autonomous Vechicles


Significance of Quantum Computing for Autonomous Vehicles

Quantum computing -whatnextglobal
digitally generated images of quantum computing

Quantum Computing


Since quantum computing shares synergies with the paradigm shift occurring throughout the
automotive industry due to the proliferation of autonomous vehicles, vehicle manufacturers are
starting to show a lot of interest in using this technique to solve complex problems.

Autonomous driving is a multifaceted, complex balance between humanistic responses from other non-
autonomous vehicles and Artificial Intelligence (AI).

Determining how these two aspects blend requires considerable computing power and expertise. Classical computing is too slow in resolving complex algorithms required for safe and efficient autonomous driving.

Hence, many automotive manufacturers are leveraging higher fidelity quantum computing methodologies to
reduce the time to market fully autonomous, self-driving vehicles.

How Can Quantum Computing Help the Development of Autonomous Vehicles?

Safety: Problems with autonomous vehicle safety and dependability can be resolved by
quantum computing. Quantum computing will take a long path to commercializing all levels of
autonomous vehicles easier and simpler.

Traffic Congestion: By evaluating data from sensors inside autonomous vehicles and assuring route
optimisation, quantum computing can help alleviate traffic congestion.

Algorithm Training

computing can be utilised to train algorithms to improve driverless vehicles
safety and fuel efficiency. Some algorithms used by recurrent neural networks can solve orders
of magnitude faster using quantum computing compared to traditional means.

Research and Development: The paradigm shift in propulsion methodology from Internal
Combustion Engine (ICE) vehicles with electrified powertrains can benefit from quantum computing
rapid simulation of new technology.

This is especially pertinent given the necessary improvements to the required battery technology. Furthermore, the jump from Level 0/1/2 to Level 5 autonomous driving can be shortened by adopting quantum computing during a vehicle program& research and development phase.

Current Quantum Computing Programmes for Autonomous Vehicles

In late 2022, Hyundai and Ion, a Maryland, USA-based quantum computing developer, announced
their collaboration& next phases. IonQ & quantum computers will be used for image processing to
perform object detection operations on 3D data from autonomous vehicles.

The organisation wants to replicate the electrochemical reactions of several metal catalysts using Ionquantum computers. Such technology dramatically accelerates the detection and classification of items,
vehicles, people, and buildings along the road by encoding images of road signs into a quantum state
for classification and object detection.

Ford Motors


Ford Motor Company recently agreed to pay NASA’s Quantum Artificial Intelligence Laboratory
(QuAIL) US$100,000 to use the space agency& quantum computer in its driverless car research. The agreement marked the beginning of a year-long project to deploy QuAIL &D-Wave 2000Q quantum
annealers to address autonomous vehicle optimisation issues that are important to the vehicle manufacturer.

According to Ford& technical expert for quantum computing research, the company
will initially focus on the generalisation of the famous travelling salesman problem: finding the most
effective route through a region with several cities using autonomous vehicles.

According to Ford& Chariot micro-transit service, the issue of route management for fleet vehicles arises in real-world situations. There are so many permutations that it would take too long to solve this problem using traditional computing methods.

According to the agreement, the business must provide NASA scientists with two to three optimisation cases to map into Quadratic Unconstrained Binary
Optimisations (QUBO), the type of input that its US$15 million D-Wave annealer will accept.

After that, NASA will offer suggestions, instruct a Ford researcher in using its computer, and grant regular
access to it. The placement of vehicle sensors for autonomous driving was a challenging optimisation problem
that Quantum Computing Inc. (QCI) used quantum computing to resolve, for BMW. The use case was
created as a component of the 2021 Quantum Computing Challenge between BMW and Amazon
Web Services.

Vehicle Sensor Placement (VSP)

Participants in the Vehicle Sensor Placement (VSP) problem were challenged to determine the most cost-effective location for sensors in a particular vehicle to provide optimal coverage for autonomous driving. In just six minutes, QCI was able to complete the challenge – which had more than 3,800 variables – using its Entropy Quantum Computing technology.

To put this into perspective, a problem of similar complexity can be solved using about 127 variables on
today &Noisy Intermediate Scale Quantum (NISQ) computers in a similar time frame. QCI created a
sensor configuration consisting of 15 sensors, offering 96% coverage of the vehicle, thus allowing
more autonomous driving functions to be utilised.

BMW suggested that this problem was highly complex and was not a proof-of-concept study to demonstrate how quantum solutions could be feasible someday. This investigation was a very real and significant problem whose solution could potentially contribute to accelerating the realisation of the autonomous vehicle industry.



To promote electric, autonomous, and connected vehicles, Multiverse Computing has joined an
industrial consortium in Spain that Renault is leading. Quantum-based algorithms developed by the
company will aid in the automotive industry& efforts to reduce carbon emissions and transform digitally,
increase connectivity and reduce the time to market for all levels of autonomy from Level 0 to Level 5.

According to Multiverse Computing, the market for quantum applications will be much larger than
the market for quantum hardware. This is a trend that has been evident in classical computing for

Multiverse& clients also have the option to run quantum-inspired algorithms on traditional
hardware, such as improving the performance of classical artificial intelligence, which has
applications in every sector, especially autonomous driving.

Google and Volkswagen

Google and Volkswagen (VW) have announced extensive research collaboration in the area of
quantum computing for autonomous vehicles. The two businesses will investigate the use of
quantum computers to advance specialized knowledge and conduct application-focused research
regarding sensors for Level 5 fully autonomous driving.

VW and Google are working on two projects:
1.) Developing new machine learning techniques, a key technology for developing advanced AI
systems crucial for autonomous driving.

2.) Simulating and optimising the structure of high-performance batteries for electric vehicles and other materials to provide new information for vehicle construction and battery research and development.

The Future of Quantum Computing for Autonomous Vehicles

The capabilities of the hardware for quantum computing are currently far exceeded by the software.
This is due to classical training algorithms and computing methodologies, which have been
gradually developed since 1930s, are limiting the development of quantum-based AI.

Given the fundamental differences in computational techniques, it is impossible to utilise a quantum computer
to realize the inherent value of these training algorithms (bits vs qubits). To make use of the
computing power of quantum computers, new quantum algorithms must be developed.

Quantum Algorithms


Quantum algorithms based on machine learning, such as the HLL Algorithm (quantum algorithm for linear
systems of equations), have made significant advancements in recent years.

Only recently have research teams started to develop quantum deep neural network training
algorithms that may one day be more beneficial for the development of autonomous vehicles.

Since Quantum computers will offer rapid processing speeds, quantum AI training algorithms can be
created and refined much faster than traditional classical computing.

This will ultimately help to improve the development of autonomous vehicles (in particular, fully self-driving Level 5 vehicles), computer-based chemistry, or even the simulation of future quantum systems.
In summary, quantum computing is a key ingredient in unlocking the potential of autonomous
vehicles for the mobility sector.

Companies should look for potential joint ventures or investment opportunities over the short, medium, and long term, keeping in mind that there are a limited number of targets in the market and that risks are high. Application development and specialised capabilities should be prioritised over the medium term (five to six years from now).

Automotive manufacturers should choose front-runners in quantum computing, scale teams to midsize, and
operationalise the initial prototypes and pilots during the process.

In the long term, over the next ten years, automotive organisations should develop a technological edge through quantum computing, creating a competitive edge in niche markets, and starting to broaden their core

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