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Can quantum technology assist in the next COVID crisis? Part 1

Julian van Velzen
July 21, 2020

Although quantum computing is seen as the next generation of computing, many companies and governments are still trying the grasp the application and its meaning beyond the buzz words. Without losing sight of the tragedy COVID-19 pandemic, the current crisis provides a valuable stage for zooming in on and questioning those potential applications of quantum computing in high-impact and complex situations, such as zoonotic epidemics and pandemics.

Even before the novel coronavirus, zootonic diseases such as SARS and Ebola caused more than a billion illnesses each year and this isn’t likely to decline in the future [Kreuder Johnson, C., Hitchens, P., Smiley Evans, T. et al. Spillover and pandemic properties of zoonotic viruses with high host plasticity. Sci Rep 5, 14830 (2015)]. As interaction between wildlife and humans grows and fast-long distance travel becomes the norm, the threat of zoonotic diseases becomes more pervasive. So too does the use of high-performance computing in battling these diseases, as evidenced by the birth of the COVID-19 High Performance Computing Consortium.

Within high performance computing, quantum is anticipated to be a game-changer in the field of quantum chemistry, because of its ability to provide exponential speed-up to crucial calculations and enhance optimization. Hence, what could be the role of quantum computing in the next pandemic?

In a series of three articles we will zoom in on three potential applications of quantum computing in light of the COVID-19 pandemic. In this article (first one of three) we zoom in on vaccine discovery.

Some good-to-know concepts when reading this article Quantum computer: a new way of computing that uses elements from theoretical physics to provide order of magnitude performance increase Qubits: the fundamental building block of quantum computers that is used for processing and storing quantum data. It is the equivalent of a classical bit. Quantum Noise: Quantum computers are prone to error due to noise from the environment. NISQ computers: Noisy intermediate scale quantum computer Circuit depth: the number of operations run sequentially on a quantum computer – the more operations, the more likely that the algorithm is affected by errors due to noise.

Creating a vaccine

The development of a vaccine for COVID-19 has an expected timeline of 12 to 18 months. Although this seems excruciating long, international collaboration and the use of new technologies have shaved off several years in comparison with traditional vaccine development timelines. That said, it does raise the question whether quantum computing has the potential to reduce these timelines even further in the future.

There are various methods of developing vaccines. However, what all these methods have in common is that they deal with complex computing during the design phase of the vaccine. During this phase, molecular simulations often have to be performed to understand the protein structure of the virus – either how it inserts itself into the cells or identifying binding sites. However, moving forward to phase 2 of the testing – testing if the vaccine works consistently – and phase 3 – testing the vaccine’s efficiency – new discoveries are often made sending the vaccine back to the drawing table. This begs the questions whether the ability to run more complex simulations, taking more actors into account, could reduce the chance of vaccines being ruled out in the second- or even third testing phase.

To see where quantum technology plays a role it is important to spot where classical computing falls short. Most of physics, and all of chemistry are based on a single equation – the Schrödinger equation. The current state-of-the-art technology in computational chemistry is based on either truncating that equation or reformulating the problem to a simpler problem. In the first class of methods, arbitrarily high precision can be realized by increasing the allowed computation time. However, problems easily get out of hand, and only small systems can be simulated. With this method, the simulation of protein structure is often out of reach. The second class of methods is dominated by density functional theory (DFT). In DFT, the problem is simplified by considering the density of electrons, instead of the full electronic configuration. This works very well for a large number of systems but starts to break down when the interaction between electrons increases. Unfortunately, for many interesting properties such as protein folding, DFT doesn’t provide enough accuracy. This is where quantum computing comes in.

“Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical, and by golly it’s a wonderful problem, because it doesn’t look so easy.” With those memorable words Richard Feynman introduced the concept of quantum computing back in 1981. Essentially, he meant that to simulate nature, we need a quantum computer.

Quantum computers are extremely good at calculating the Schrödinger equation. Instead of truncating the equation, or simplifying the problem, quantum computers can simulate systems with much higher accuracy. There are two quantum algorithms in particular, that are being actively researched: the variational quantum eigensolver (VQE) and quantum phase estimation (QPE). VQE is a hybrid quantum algorithm. In hybrid algorithms, the computational workload is distributed between classical computers and quantum computers. On the quantum processor, the Schrodinger equation is parametrized and solved, whereas on classical computers, the parameters are iteratively optimized. The circuit depth is therefore much smaller than on other quantum algorithms, which means that fewer errors will occur. This makes VQE a potential algorithm for near-term quantum (NISQ) computers. QPE is a pure quantum algorithm, which means that the whole algorithm runs on a quantum processor. This requires a circuit depth that is well beyond the scope of near-term processors, but it does unlock the full potential of quantum computers. The QPE algorithm provides an exponential speed-up over classical algorithms.

Quantum phase estimation is a game changer for simulations of systems with lots of interaction between electrons. By calculating the Schrödinger equation to arbitrary accuracy, we can now determine the energy surface of molecules. From the energy surface, we can learn about chemical properties such as reaction rates and binding energies. A better understanding of protein structure and dynamics can hence speed-up the fabrication of vaccines. Unfortunately, quantum computers do not have the required power to run these algorithms, and it is likely that we have to wait at least ten more years to see their full potential.

In conclusion, looking at the impact that COVID-19 is having on society, economy, and healthcare, we can envision future use cases for the role of quantum computing in vaccine development. Within these topics, quantum will claim its role thanks to its ability tackle larger problem spaces with higher accuracy.

Even though the added value of quantum computing is still a couple of years down the line, we should prepare for it. Without dismissing the great technological developments that have been made before, and during the current pandemic, different skillsets will likely be required in applying quantum computing versus using classical computing. This stems from the fact that the very foundation of quantum computing is different and therefore the layers building on that foundation, such as programming languages, will be different too. If quantum computers are to become mainstream in ten years, students should enroll today.

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This article is the first out of a series of three, co-authored by Renate Wolters and Julian van Velzen. Stay tuned for part 2; the bullwhip of corona and part 3; corona evolution as a machine learning application. In part 3 we will also zoom in on the overall conclusion of the potential of quantum computing in complex situations like COVID-19.