Quantum leap: How quantum sensors are revolutionizing robotics Quantum leap: How quantum sensors are revolutionizing robotics
Head over to our on-demand library to view sessions from VB Transform 2023. Register Here The recent Ant-Man movie did a great job of... Quantum leap: How quantum sensors are revolutionizing robotics

Head over to our on-demand library to view sessions from VB Transform 2023. Register Here

The recent Ant-Man movie did a great job of putting quantum up in lights, but the future of quantum science shines even brighter than fiction. One application, quantum sensors, is already the basis of some of the most important systems and technologies in our world — global positioning systems (GPS) and magnetic resonance imaging (MRI) scanners are prime examples.

Quantum sensors and quantum AI are just the beginning: Robots are now getting the quantum sensor treatment too. Quantum sensors will supercharge the way robots work and how we apply them to important 21st-century challenges.

Why quantum sensors are a big deal

Modern technology is full of sensors that measure heat, light, movement, pressure or other aspects of the physical environment. Quantum sensors add something new. They use the quantum properties of how particles behave at atomic scale to detect tiny movements or changes in gravitational, electric or magnetic fields. 

Because they work at such a small scale, quantum sensors can measure light or other observable phenomena extremely accurately. It also means they can provide a highly precise and stable measurement, as they measure properties like the structure of atoms or spins of atomic particles, which never change.


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This accuracy and reliability make quantum sensors very useful. They make sure the tick of atomic clocks stays true to the beat of time, a quality which puts them at the heart of GPS and other Positioning, Navigation and Timing (PNT) systems. They are also widely used in MRI scanners to provide clinicians with finely detailed diagnostic images. And they are also helping improve the environmental data available to scientists and industry, a vital aspect of global sustainability efforts.

Challenges of noisy data

It’s important to mention, though, that sometimes being so precise and sensitive can be less useful. That’s because it results in a lot of noise in the data. Noisy data is a challenge that teams like our EY quantum data science team are tackling by implementing AI to separate insights from the noise.

In fact, combining quantum sensing with other technologies is a strategy with lots of potential. Quantum sensing and robotics is a good example. The tiny size of most quantum sensors, plus their high sensitivity, have already led to their use as tactile sensing elements in fiber optic cables for robotic arms — helping the robot arm to perceive its environment by detecting precise information about pressure, vibration, temperature or texture.

Other potential applications of this powerful combination are also emerging. For example, we are starting to see quantum sensors combined with mobile robots. Information about the environment detected by the sensors, such as small changes in temperature or magnetic fields, can enable the robot to make more precise movements and decisions, as well as gather valuable data for other purposes.

Mobile robots make great quantum sensor transporters

We tested this ourselves by attaching a quantum sensor to Spot, a quadruped robot designed to move around and collect data. The quantum sensor we tested is designed to measure the type of light that influences plant growth, called photosynthetically active radiation (PAR). More precisely, the sensor measures the number of photosynthetically active photons at a particular location at a point in time to see how much PAR a plant in that location would receive.

Because the sensor is robust and reliable in environments like artificially lit greenhouses both underwater and underground, attaching it to mobile robots like Spot has valuable potential in agriculture, where monitoring and managing light is vital. It could also help model emerging large-scale bio-ecosystems, such as plantations in the desert or underground farms, to help use them address global food security.

We are already seeing pioneering research in this area, such as a Qatari project studying optimal growing strategies for very light-sensitive greenhouse plants like tomatoes, a project feeding into the country’s food security focus on locally grown rather than imported produce.

Great potential in pairing quantum sensors with mobile robots

For a simple proof of concept, we attached our sensor to Spot with a standard GoPro mount and programmed the robot to move around our office garden so the sensor could take light measurements. Our first finding was that wintertime in Denmark is “not optimal” for our plants, unfortunately!

Our second was to see first-hand why pairing quantum sensors with mobile robots has such potential. We saw particular value in the ability to program Spot to take regular measurements around the garden over time.

Beyond agricultural uses for robot-mounted PAR sensors like Spot’s, robots with quantum gravity sensors could transform our ability to map underground structures. By measuring differences in gravitational fields more precisely, these sensors could help reduce construction risks through more accurate mapping of tunnels, caves or sinkholes, as well as helping environmental scientists to model and predict patterns of magma flow or groundwater levels to manage eruption and flood risks.

Robots are also in line for a quantum sensor upgrade

Getting quantum sensors into and around challenging environments isn’t the only benefit of the robot-quantum sensor pairing. Quantum sensors could also help robots navigate better. It’s critical for autonomous robots like Spot or self-driving cars to be able to navigate safely and accurately.

Here, too, quantum sensors look set to play a part. In December 2020, the SPIDAR project received UK government funding to develop quantum-based LiDAR systems for autonomous vehicles. By detecting single photons emitted by an object and using this to measure the detected object’s distance, SPIDAR will be able to sense how close an object is to a vehicle with far greater precision than existing 3D camera systems.

Compared to current LiDAR systems that measure laser beam travel time to and from objects with accuracy to the range of 100 milliseconds, quantum LiDAR like SPIDAR will measure photon travel time to the trillionths of a second. They will also be able to detect objects through fog or potentially around corners, which current LiDAR cannot do. The quantum LiDAR upgrade certainly sounds like a positive move towards autonomous vehicles we can feel safe being in or alongside.

Away from every day road users, quantum sensors will also help robots like drones and autonomous military vehicles navigate in environments where GPS systems either don’t work or could be an exploitable weakness. These non-GPS PNT systems often use cold trapped ion quantum sensors that measure tiny changes in gravity and atomic acceleration. As the technology gets smaller and more rugged, experts believe these systems will have significant potential in commercial and defense industries.

The quantum and robot pairing doesn’t stop with sensors

Our whistlestop tour of quantum sensors and robotics shows how much opportunity this combination will offer as the technology continues to develop. But what makes the quantum and robotics pairing even more exciting is its broader potential, particularly when you add AI into the mix. 

AI technologies like computer vision and machine learning (ML) are vital to how autonomous mobile robots perceive and avoid obstacles and plan their activities within a particular environment. But making AI processors small and light enough to integrate within smaller robots is a big technical challenge. That’s because processes like machine vision require huge amounts of computing resources.

Experts believe quantum computing could overcome this challenge by running algorithms much faster, dramatically reducing the processing power required. Doing so could open up many more opportunities to take advantage of mobile robots. This is just one example of quantum AI applied to robotics — others, such as quantum ML’s potential to help robots learn faster, are also being explored, and no doubt other fruitful pairings will follow.

All in all, it’s clear to us that quantum robotics is a dynamic field that innovators, scientists and governments are keen to expand. We are confident that quantum sensors and quantum AI are just the beginning. We will be watching closely as quantum robots take ever-bigger steps toward realizing their potential. As they do, they will join the raft of quantum applications taking quantum science well beyond the realms of fiction.

Jeff Wong is global chief innovation officer at EY.

Kristin Gilkes is global innovation quantum leader at EY.

The views reflected in this article are the views of the authors and do not necessarily reflect the views of the global EY organization or its member firms.


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