Peter Debenham, Senior Consultant at Plextek, looks behind the headlines and explores the realistic future applications of quantum computing
As a buzzword, quantum computing is on par with AI, IoT and Metaverse in terms of hype. But ask people what quantum computing is and what quantum computing means for the future, and most will have trouble. If you dig a little further, people will remember the headlines about how quantum computing threatens to destroy internet security. If quantum computers are fast enough to crack encryption algorithms, it’s game over. But is this a reality?
First, quantum computers are so in their infancy that they don’t currently exist for most practical purposes. It was only in 1980 that physicist Paul Benioff proposed a quantum mechanical model of the Turing machine, and scientists and engineers are only just beginning to develop the physics and engineering needed to build commercial quantum computers. The largest processors contain hundreds of qubits, the basic unit of a quantum computer, where tens of thousands or millions are needed. The problems of building quantum computers are not insurmountable, but will take more time.
A paper from 2021 suggests that a quantum computer with just over 13,000 qubits could factor a 2048-bit RSA integer; the kind of operation required to crack much of the existing Internet encryption. They wanted 177 days for that, but that’s a lot faster than the “not before the heat death of the universe” answer for classic computers. Another paper from 2021 suggests that it would take 20 million qubits just 8 hours to do the same.
Most cybersecurity professionals have been aware of the issues at hand for many years and are developing quantum computing resistant cryptography. In 2015, the US NSA (National Security Agency), followed by the UK NCSC (National Cyber Security Council), announced plans to move to quantum-resistant cryptography and have standardized algorithms ready by 2022-2024. NIST, the US National Institute of Standards and Technology, announced four candidate algorithms on July 5, 2022.
What are they good for?
Given that quantum computers aren’t suddenly going to allow anyone to read the world’s encrypted internet traffic, what good are they? Fast search problems and machine learning for sure, but what else?
Two physicists, Manin 1980 and Feynman in 1981, answered this question by pointing out the simulation of things that cannot be simulated with classical computers, such as quantum mechanical systems. We can accurately model the quantum mechanics of simple systems like a single hydrogen atom with pen and paper and some particles with existing computers. But larger quantum mechanical systems cannot be accurately modeled at all without incredible simplification.
Feynman envisioned trying to use quantum mechanics in our current computers to accurately model a system with a larger number of elements. For a given number of particles, N, over a similar number of locations in space, you need memory to store and process to calculate NN Configurations that quickly become too large. Even for 100 particles you have about 10200 Configurations to save and calculate at each step. Compare that to estimates of 1080 Atoms in the observable universe and it’s impossible for classical computers. But it could be done with a quantum computer on the same order of qubits, namely 100, because the qubits respond exactly like the system being modeled.
Existing simplified quantum mechanical models underpin modern chemistry, materials design, and pharmacy. Fully accurate models would allow many more. New drugs, more efficient chemical processes and new materials.
For example, the first fertilizer production using the Haber-Bosch process consumes about 1% of the world’s total energy production and causes 1.4% of the world’s CO2 generation. Fertilizer is needed to feed a world of 8 billion people, but better modeling offers the high probability of a more efficient process by developing a better catalyst.
The second example is material design. For decarbonization, the world is switching from internal combustion engines to electric motors. A huge problem with motors is the heat from the electrical resistance. Not only is heat an inefficiency, but worse, there is a problem of how to dissipate it. If it gets too hot, the engine will fail or something will catch fire. Less heat allows for smaller, more efficient engines that are also quieter. Replacing a motor’s wiring with high-temperature superconductors eliminates both problems, but applications are limited because high temperature for superconductors means that above liquid nitrogen (77 K or -196OC). Better materials modeling would support the search for useful room-temperature superconductors and bring superconducting motors from large industrial settings to everyday life.
So what will quantum computers do for us? It won’t break internet security, but it might give us better chemical processes and room-temperature superconductors. Quantum computing could change the world – but its future is currently uncertain.
Peter Debenham is a Senior Consultant at Plextek
 Gouzien, E. and Sangouard, N., 2021. Factoring 2048-bit rsa integers in 177 days with 13 436 qubits and a multimode memory. Physical Verification Letters, 127(14), p. 140503.
 Gidney, C. and Ekerå, M., 2021. How to factor 2048-bit RSA integers in 8 hours with 20 million noisy qubits. quantum, 5p.433.
 Alagic G, Alperin-Sheriff J, Apon D, Cooper D, Dang Q, Dang T, Kelsey J, Liu YK, Lichtinger J, Miller C. , Moody, D., Peralta, R., Perlner, R., Robinson, A., and Smith-Tone, D., 2022. Status Report on the Third Round of the NIST Post-Quantum Cryptography Standardization Process. US Department of Commerce, NIST.
 Manin, YI, 1980. Vychislimoe i nevychislimoe (Computable and Uncomputable), Moscow: Sov.
 Feynman, RP, 1982. Simulating Physics with Computers. International Journal of Theoretical Physics, 21(6/7) (Publication of a conference paper dated May 7, 1981)