Quantum Computing in Drug Discovery: Myth and Reality

What is quantum computing?

In the early 1980s, Richard Feynman noted that it is exponentially costly to compute the behavior of large quantum mechanical systems. He further pointed out that this proves that at least some computations can only be practically performed by a quantum system, namely, trivially, the prediction of the behavior of the said system. Since then, much progress has been made to formulate more abstract problems that can be proven to require a quantum computer for a practical solution. 

One such problem is the factorization of large numbers. A certain class of cryptographic algorithms, specifically the RSA algorithm, are secure only because it is impossible to factor a sufficiently large number with practical resources. In 1994, Peter Shor described what is known as Shor’s Algorithm, which could be used to practically factor large numbers on a quantum computer and thus break all RSA-based cryptography.

This would be a very big deal because a lot rests on the security of cryptography. While there are cryptographic algorithms that do not rest on factoring numbers, most believe that those would be similarly susceptible to different, not yet described quantum algorithms. The implications for information security are apparent, but the rise of cryptocurrencies adds another, very direct incentive: If someone clandestinely broke the algorithm used in a cryptocurrency network, they could conceivably walk away with billions of dollars in profits before the breach was detected and the value of the currency collapsed.

However, Shor’s algorithm requires a very large quantum computer, much larger than can be practically built today or in the foreseeable future. More importantly, it depends on error-free quantum computation, which can be achieved on real hardware only with sophisticated error correction mechanisms that are the subject of ongoing research and will further increase the required system size by many orders of magnitude. For this reason, it is generally believed that practical quantum computers that can solve useful quantum problems are many decades in the future.

Progress in quantum supremacy

In order to make more immediate progress, then, current research is mainly directed towards proving quantum supremacy, i.e. finding a problem that demonstrates quantum supremacy in practice on actual quantum computer hardware. Because of the large gap between current hardware and what would be needed for useful problems, such problems tend to be highly esoteric and of little practical value. Still, they are essential for the field to move forward.

These efforts have recently had some success. In particular, Google reported in 2019 that they had achieved quantum supremacy on a 53-qbit quantum computer named Sycamore. The problem that was solved is to predict the probability distribution of a quantum random number generator, which is classically hard (10,000 years), but practical (200 seconds) for a quantum computer. The catch here is that there is nothing useful about a random generator with that particular distribution.

Google’s quantum computer is based on superconducting Josephson junctions, one of a variety of implementations of quantum computers. It is beyond the scope of this article to survey the hardware landscape, but we will point out one other promising technology for quantum computing, the photonic chip

Will it help with Drug Discovery?

Drug discovery has many different areas in which computing can play an important role, and, in general, any of them could be amenable to quantum computing. Here, we want to mention just two of them: Quantum chemistry and machine learning.

Non-practitioners can be forgiven to get confused by the co-occurrence of two entirely different concepts that may be relevant in drug discovery: Quantum chemistry and Quantum computing. Quantum chemical simulation has been around for decades. It involves the approximate computation of the properties of electronic orbitals, which in turn could, in theory, fully describe the behavior of molecules from first principles. Normally, when a lab or company does quantum chemistry, there is no connection to quantum computing. However, quantum chemistry does illustrate Feynman’s original recognition that quantum systems are very hard to simulate classically, and research is underway to bring quantum computing to quantum chemistry. 

Quantum machine learning is another promising application of quantum computing in drug discovery, simply because machine learning itself has found numerous applications. If machine learning could be sped up substantially using quantum computing, drug discovery would undoubtedly benefit. Researchers from IBM and MIT have recently proposed quantum machine learning algorithms. The broad applicability of such methods guarantees that this will be a very active field of research in the foreseeable future.

What does all this mean for today’s practitioners in computational drug discovery? It is important to realize that there is a lot of hyperbole in the mainstream press, and also, unfortunately, in certain parts of the scientific literature, on how likely or how soon quantum computing will change everything. Amongst the more serious voices, some are creating the impression that this will happen anytime now; others advise caution and patience. Still, others think that the path to utility is entirely too steep

Regardless of attitude, it is clear that there are enormous hurdles in realizing practical quantum computers, both in the hardware and in the error correction mechanisms that will be necessary. Any real benefits from the application of quantum computing in drug discovery are very likely decades away. However, those potential benefits are huge, so it is prudent for the practitioner to keep mind and eyes open, while also maintaining skepticism in the face of breathless hyperbole.

Cyclica’s Position

Enormous hurdles exist in realizing practical quantum computers, both in the hardware and in the error correction mechanisms that will be necessary. Thus, any real benefits from applying quantum computing in drug discovery are very likely decades away. However, those potential benefits are huge, so it is prudent for the practitioner to keep mind and eyes open while also maintaining skepticism in the face of breathless hyperbole.

We should keep an eye on Xanadu, who impresses by their photonic chip and their open approach to quantum software development. An alliance of some form could be beneficial, demonstrating that we are looking beyond the present and are willing to invest in the future, even when there is no imminent practical benefit.

Dr. Andreas Windemuth, Chief Innovation Officer

Dr. Andreas Windemuth, Chief Innovation Officer

Andreas is the Chief Innovation Officer, and guides Cyclica's vision in creating a scientifically rigorous platform that's integral in the drug discovery pipeline.

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