Editorial Feature

Which Industries will Quantum Technology Impact Most?

While physicists have been researching quantum mechanics for over a century, it is only in recent decades that this research has been used for practical applications. The emerging field of quantum technology has a lot of potential, and this article will explore which technologies are the front runners in this new wave.

quantum technology, quantum industries, quantum technology in industry

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Quantum Computing

Quantum computing is the best-known and fasting developing quantum technology, thanks to investment from tech giants such as IBM and Microsoft. As classical computers reach the limit of their capabilities, these companies hope that quantum computers can take over.

One of the limitations of classical computing is that information can only be transferred in binary form; each packet of information, known as a bit, is either on or off. This is fine for dealing with absolutes, such as transferring text from one device to another or determining the answer to a calculation, but classical computers struggle with tasks such as complex simulations.

Quantum computers utilize the principle that particles can simultaneously exist in two different states at once. These quantum information packets, or qubits, aren’t either on or off; they’re a mixture of the two. This allows quantum computers to process information far faster than even the largest supercomputers, and they can be used to implement ideas that would have only ever been pipe dreams without them.

Quantum Sensors

The abnormal behavior of particles on the smallest scales puts a hard limit on the precision of classical sensors. Quantum sensors fight fire with fire, leveraging our understanding of this abnormal behavior to measure with more precision than was ever possible before. There are two broad categories of quantum sensors: photonic and solid state.

Photonics is the use of individual photons to sense disturbances. It uses quantum entanglement, which occurs when two particles become linked so that changes to one will affect the other. By intentionally entangling particles, one can measure the changes to the photons and thus determine the nature of disturbances.

Solid-state sensors are quantum systems that will respond to outside influences. These sensors can be set up to be in an initial known state, and then observed after an interaction. By comparing the initial and final state, the nature of the interaction can be determined, with far greater precision than sensors currently in use. There are multiple types of sensors in development, which can be used to measure a wide range of fields and forces.

Quantum Imaging

Quantum imaging uses similar principles to photonics (taking advantage of entangled photons) and applies it to image objects that have previously been elusive. This is important for understanding quantum structures, and as with quantum sensors, there are many methods of imaging, each suited to a different purpose. Using these techniques, it is possible to image objects that would be invisible classically and reduce the effect of quantum noise on imaging. 

Which Industries Can Benefit from Quantum Technology?

It’s important to state that quantum technology is a very long way from being accessible at a consumer level. Quantum sensors and imaging are tools that will only make a difference in specialized research, and our normal computers are far better suited for day-to-day use than quantum computers. However, quantum technology will almost certainly impact the world, as researchers and businesses will be able to take advantage of these advances to achieve the impossible.

Cryptography: Quantum computers have the potential to be far more secure than classical computers. They’re capable of processing current security protocols with great speed, allowing more layers of security to be used without slowing systems down, and have access to novel forms of protection such as quantum key distribution. This will be of great benefit to any organization for which data security is paramount, such as international banks and national intelligence services. Groups such as these are in a constant race with hackers, having to find new methods of protection as old ones get cracked; quantum computers could give them the protection that can’t be overcome with classical technology.

Chemistry research: Chemical reactions are inherently a quantum process, and this makes them difficult to understand with classical tools. Previously, corners had to be cut when simulating chemical processes, but quantum computers offer near-perfect simulations. Their probabilistic method of storing data means they can accurately model chemistry. Pair this with the deeper understanding of molecules that quantum imaging provides, and this new era of technology could usher in a new era of chemistry.

Biology and medicine: Advances in chemistry will often be followed by advances in medicine, but there are other ways biology will benefit from quantum technology. As discussed above, quantum computers are fantastic for chemical simulations (which are important for drug design). They are also well suited to tasks such as genome sequencing, as they can model and test protein stands far more efficiently than classical computers. Quantum sensors have also been used for neurological imagining, with the capability of detecting brain signals. 

quantum technology, quantum industries, quantum technology in industry

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Geophysics: Quantum sensors can be used to measure minute variations in the Earth’s gravitational field, and geophysicists are able to infer a lot from these measurements. While there are existing ways of taking such readings, they are inconsistent, and research shows that quantum sensors could be effective. This detailed picture of the earth below would benefit civil engineering, climate science, and natural disaster mitigation. 

Machine-Learning: While machine-learning quantum computers are still in an early stage of development, the theory supporting them looks promising. Because quantum computers “think” differently to classical computers, there is a good chance that they will be accel at learning tasks. Currently, there are hardware and software limitations slowing progress, but with time and investment, these should be overcome. Machine learning is an incredibly complex field, but there are many industries and areas of research that are set to benefit once it is more developed.

More from AZoQuantum: Regional Spotlight: The Quantum Tech Market in the USA

References and Further Reading 

IBM. (2022). What is quantum computing? https://www.ibm.com/my-en/topics/quantum-computing

Pirandola, S. Bardhan BR. Gehring, T. Weedbrook, C. Lloyd, S. (2018) Advances in Photonic Quantum Sensing.  Nature Photonics, 12 (724-733). https://www.nature.com/articles/s41566-018-0301-6

Degen, CL. Reinhard, F. Cappellaro, P. (2017) Quantum Sensing. Rev. Mod. Phys., 89 (035002). https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.89.035002

Genovese, M. (2016) Real applications of quantum imaging. Journal of Optics, 18 (073002). https://iopscience.iop.org/article/10.1088/2040-8978/18/7/073002

Pirandola, S. Ottaviani, C. Tomamichel, M. Usenko, VC. Vallone, G. et al. (2020) Advances in quantum cryptography. Advances in Optics and Photonics, 12/4 (1012-1236). https://opg.optica.org/aop/abstract.cfm?uri=aop-12-4-1012

Cao, Y. Romero, J. Olson, JP. Degroote, M. Johnson PD. et al. (2019) Quantum Chemistry in the Age of Quantum Computing. Chem. Rev. 19/119 (10856-10915). https://pubs.acs.org/doi/10.1021/acs.chemrev.8b00803

Outeiral, C. Strahm, M. Shi, J. Morris, GM. Benjamin, SC. Deane, CM. (2020) The prospects of quantum computing in computational molecular biology. WCMS. https://wires.onlinelibrary.wiley.com/doi/10.1002/wcms.1481

University of Sussex. (2021) Researchers build first modular quantum brain sensor, record signal. Phys.org. https://phys.org/news/2021-06-modular-quantum-brain-sensor.html

Stray, B. Lamb, A. Kaushik, A. Vovrosh, J. Rodgers, A. et al. (2022) Quantum sensing for gravity cartography. Nature 602(7898).  https://www.nature.com/articles/s41586-021-04315-3

Biamonte, J. Wittek, P. Pancotti, N. Rebentrost, P. Wiebe, N. Llloyd, S. (2017) Quantum Machine Learning. Nature 549 (195-202). https://www.nature.com/articles/nature23474

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