Given the long-standing hype around AI and machine learning (ML) in pharma R&D, it can be easy to overlook innovations that potentially promise just as much for the pharmaceutical sector. Quantum computing is one area expected to have far-reaching impact within the field, but is at the moment almost singularly misunderstood. 

To find out more about this technology with such great promise, Proventa spoke to experts Dr Emir Roach, Senior Director of Strategic Programs and Head of Emerging Technologies at Takeda, and Dr Philipp Harbach, Head of In Silico Research at Merck KGaA, Darmstadt, Germany, about the future of quantum computing and what lessons the pharmaceutical industry should be learning right now. 

The Distinction Between Quantum and Classical Computing

Quantum mechanical methods have been around for almost a hundred years, with recent advancements in computer hardware making it possible to extend these methods – including combinatorial optimization, differential equations, linear algebra, and factorization to classical computers which can simulate quantum systems in real-life applications. These simulations are already being run in most pharma companies by computational chemists, and applied to day-to-day problems. The problem with this scenario is that running quantum mechanics on classical computers is enormously costly. 

To overcome the issue of costly computations, the use of ‘qubits’ in quantum computers might be a solution. While classical computers use bits with a value of either zero or one, quantum computers use qubits with superpositions allowing a much wider range of possible states than a simple binary. These qubits all act as a group (entanglement) to achieve much higher information density than a regulatory computer, speeding up computation by a vast margin.

It is important to understand that quantum computers will not replace classical computers: they are used for separate issues, not simply improving on current technology – though quantum computers may have an advantage in optimisation and ML problems. They might also have an advantage over classical systems in their ability to tackle quantum simulations, as they are already inherently quantum.

One benefit of quantum computers over alternatives, as Dr Philipp Harbach, Head of In Silico Research at Merck KGaA, Darmstadt, Germany, pointed out, is that when it reaches cost-efficiency the new technology will also be highly environmentally sustainable, as quantum computers need almost no energy compared with a classic computer. 

What is Quantum Computing?

Dr Roach set out roughly how a quantum computer is designed to work: 

“Fundamentally, when you’re performing a chemistry computation on a traditional computer, you’re defining a math problem that the computer needs to solve. The computer pushes bits through arrangements of gates which enable performing calculations and provides the results. 

“With quantum computers, at the high level, you are attempting to represent the initial state of a problem, for example a chemical structure in qubits which can be made to be interrelated – or entangled. Then, you manipulate those qubits using radio waves, light etc. Depending on the arrangement and interrelations of the qubits, the system undergoes changes. Then you read the latest state of the qubits to see where things landed. In effect, you’re creating an analogous representation of the chemical and manipulate it to observe changes.

“What that means is, for every problem, we need to figure out what it means to create that initial state of qubits, figure out how to build bridges between those qubits, what it means to fire light/radio waves into those qubits, and then figure out after we’ve done a reading of the new state of those qubits, how to interpret them. It’s a different translational discipline.”

Quantum computing is a natural fit for the pharmaceutical sector. The field’s need to model molecules and determine their properties can be greatly enhanced by quantum computing, which will hugely increase the ability to understand the effect of a given molecule on the body through simulations of its mechanics and properties. Use cases of quantum computing in pharma can extend drug development however, into supply chain, clinical trial scale-up etc.

Current Challenges Facing Quantum Computing

This ‘quantum advantage’ is currently muzzled by a number of complex challenges. First and foremost, as Dr Harbach noted, there is no existing solution yet for scaling the hardware of quantum computers while also reducing the exponential problem of noise (e.g. error) that increases too. 

The next challenge is simply that currently, few companies know what to do with quantum computers. While theoretical algorithms exist for the technology, they’re few in number and require complex and expensive hardware. In contrast to this, quantum methods on classical computers date back several decades: there are thousands of algorithms that can be applied. 

Dr Roach set this out more clearly: “Once we have a problem in mind, translating that into a quantum algorithm to run on a computer is challenging. That translation is something for which there is not much expertise in the world.”

Dr Roach expounded on this problem by pointing out that for most current problems within pharma R&D, a solution already exists besides quantum computing: “There are many models out there that solve current problems, from finding the right target, to generating a molecule and observing its interactions with the targets.”

“We as an industry need to zero in on the most pressing, highest-value problems we want a quantum computer to solve. There are some high-level understandings of where we want to go, be it protein-folding or generating drug designs, but no clear view as of yet.”

These problems are not, however, simple binary issues that will usher in a new era of quantum computing when overcome. Each type of challenge – hardware, expertise etc – contains categories of difficulties within them. Within the hardware problem, there are architectural challenges (for example, how qubits engage with experts’ designs) which influence the type of computations which can run on the hardware. 

Within translation, smaller challenges exist around how to initialise a quantum computer, and then how scientists should read outputs and feed them back into the quantum model. 

The Quantum Advantage

According to Dr Harbach, a rough estimation of the time quantum computing would gain an advantage over classical technology somewhere in the pharma product life cycle – not necessarily just in R&D – would be around five years from now. This still would not be cost-efficient, however, and would only be used to solve extremely complex, vital problems. To reach a mass market level, Dr Harbach estimated that perhaps ten to 15 years would need to pass, though this is by no means certain. 

Lessons for the Pharma Industry

The good news is that the industry is working as one to solve the quantum issue. While big headlines in recent months – the Boehringer / Google development, Roche’s partnership with UK firm Cambridge Quantum Computing – have made the area seem an increasingly heated competition, in reality pharma companies are working together in this pre-competitive phase, and do not expect real value on short-term, according to Dr Harbach. 

Most companies in the pharma sector are connected by QuPharm, an interest group dedicated to sharing research around quantum computing. Dr Roach spoke more about the organisation: “As an industry, I’d argue we recognise that there are many steps left to take before we overcome the challenges of developing quantum computers.

“To get past these challenges will require a number of disciplines to come to together, from hardware specialists and translators who can turn chemistry problems into quantum algorithms to computational chemistry experts who can find where exactly limitations are, through to business strategists who can determine how much should be invested on a problem.”

This cautious, collaborative approach to quantum computing leads back to an earlier point about one of the main challenges in the area: the lack of questions that can be currently answered with the technology, and the lack of a business problem for quantum computing to solve that would reinforce the need to invest in that area. 

As Dr Harbach said, it is dangerous to promise that all current simulation problems or even some business problems can be answered by quantum computers: “This is dangerous, it’s a scorched earth policy. When computer models first appeared in the 1980s, the promise that they would revolutionise pharma development became overhype, and that false promise caused a lot of damage. It is very important to be careful in this regard.”

Joshua Neil, Editor
Proventa International