Do We Really Need Quantum Computing in Chemical R&D?

So there I was, at the Quantum Computing @ Work, an event organized by SIBB and BearingPoint Berlin. The room was packed with folks from both research and industry, all eager to explore the possibilities of quantum computing for businesses. Naturally, I was there representing Quantistry, excited to share our perspective on this cutting-edge technology. Then, out of the blue, a well-dressed attendee, who looked like he’d been through his fair share of triumphs and headaches, raised his hand and asked a simple but weighty question: “Do we really need quantum computing in chemical R&D?” The short answer is: Yes, we do. The long answer is what this story is about.

Why Quantum Computing for Chemical R&D?

Quantum computing in chemical R&D: Hype Or Reality?

If you’ve been browsing the internet for quantum computing lately, you’ve probably come across a whole lotta hype. It’s enough to make your head spin. Everywhere you look, it’s the same old story: When quantum computing arrives, we’ll be able to [insert miracle here].

It’s like a never-ending game of fill-in-the-blank.

For example, in logistics, the famous travelling salesman problem? No problemo, because quantum computing will help companies figure out the best routes for every shipment and save billions. And don’t even get me started on financial forecasting. With all that complexity, quantum computing is gonna swoop in and solve all the world’s economic problems. Piece of cake.

And then, of course, there’s the hyper-hype stuff. Quantum artificial intelligence. As if regular old artificial intelligence isn’t good enough, now we gotta throw quantum computing into the mix. The idea there is to use quantum computing power to train your AI model more efficiently. And hey, why stop there? Let’s talk advertising. Before you know it, quantum computing will be able to help companies do advertising without spending a fortune. How? Heck if I know.

But does it really matter? Quantum computing can do anything, right?

Quantum Computing for Chemical R&D - hype

Don’t get me wrong, while the hype may be overwhelming, there’s some serious potential here. Learning about quantum computing and making it work means we’re getting closer to understanding the fundamental laws of the universespacetime anybody? Plus, quantum computing could be a disruptive technology in many industries.

The holy grail is real. Just maybe not today, or even tomorrow. We’re perhaps talking 5, 10, 15 years down the line.

Think for instance about the ultimate holy-grail combo: quantum chemistry on quantum computers. This magic duo promises to enhance drug discovery, battery development, alloy design, renewable energies – just to name a few use cases in chemical R&D.

But here’s the thing. When it comes to quantum computing for chemical R&D, we can’t forget the decades of work in quantum chemistry and computational chemistry that have come before. We can’t ignore the fact that atomistic simulations are already tested, established, and mostly accurate.

What I’m saying is this: we don’t have to sit around twiddling our thumbs waiting for quantum computing to arrive. We can start simulating today. We, computational people, have been doing it for at least 50 years.

So, the million-dollar question is:

What hinders the widespread use of atomistic simulations in chemical R&D?

Well, it’s not because quantum chemistry, computational chemistry, or atomistic simulations aren’t useful. Quite the opposite.

These established methods already have the potential to revolutionize R&D, allowing companies to screen thousands of different compounds and materials before even stepping foot in the lab. So, besides some notable examples, why aren’t these tools being employed more often?

The answer is simple: because it’s hard.

For companies to make use of these simulations, they need to overcome significant barriers. And in a commercial setting, overcoming barriers means risk. Companies must invest in computing power and software, hire computational experts, chemists, and IT personnel to set up the necessary systems. Even with all that money and expertise, there’s still a ton of technical hoops to jump through.

In concrete terms, there are (at least) three main barriers holding R&D back from using atomistic simulations:

  1. Accessibility – Most current software requires a lot of coding. You need to prepare inputs, parse outputs, standardize and transform data from one software to another, read through countless tutorials and articles… it’s a whole thing.
  2. Expertise – Even if you’ve got powerful software at your disposal, you still need to know what to do with it. You need to have the expertise to make sense of the calculations you’re running. It’s not exactly something you can just pick up overnight.
  3. Computing – And of course, you need access to computing power. For R&D companies, this can be a significant expense. You need (in-premise) high-performing computers, IT and technical personnel, software installation, and even after all that, you still haven’t started running calculations yet.

So yeah, atomistic simulations are a headache. Ask any PhD in computational chemistry. But the benefits they offer are huge. If we can figure out a way to make these tools more accessible, more user-friendly, and less resource-intensive, we could be looking at a whole new era of industrial R&D.

Now, even the perfect quantum computing won’t magically eliminate these barriers. What we really need are smarter and more accessible solutions that don’t require everybody to get a PhD in computational chemistry. That’s why I believe in what we do at Quantistry. We’ve created an easy-to-access, curated, cloud-based chemical simulation platform – no code, no headaches. All you need is a web browser.

But even with these advancements, we still need to overcome critical challenges to bridge the gap between atomistic simulations and the experimental world.

What are they, you ask? Well, let’s dive deeper and find out.

Disclaimer: For the sake of simplicity, I have used quantum chemistry, computational chemistry, and atomistic simulations as synonyms. They do largely overlap, but it should be noted that they are not exactly the same thing. I plan to write a story about their differences one day, but for now, have fun with a previous story, where I touch this topic.

Atomistic simulations of nanoparticles, polymers, reactions, and adsorption on surfaces. QuantistryLab view

Today’s Challenges for quantum chemistry and atomistic simulations

Already in the 1960s, quantum chemical methods were making waves. They were successfully describing the structure and properties of very simple molecules. And now, with today’s programs and classical computers, we can accurately calculate the structure of molecules with hundreds of atoms routinely. That’s no small feat. We’re at a point where quantum chemical calculations are being taken as alternatives to experimental methods.

But let’s be real. You won’t always reach chemical accuracy. It’s just not that easy. However, quantum chemistry can inspire new ideas, guide decisions within an experimental setting, and rationalize observed phenomena. That’s pretty damn impressive if you ask me.

But. There is a but. It’s not always sunshine and rainbows in the world of quantum chemistry and atomistic simulations. Despite decades of progress, the field is still facing many challenges.

For example, we need to bring quantum accuracy to larger models of thousands, even millions of atoms to capture more and more realistic systems of industrial interest. And that’s a tall order. And when you throw solvents, environmental effects, or systems as complex as a virus or polymers on metal surfaces into the mix, we very quickly hit the limits of current quantum mechanics-based methods.

So, what do we need?

More powerful computers and smarter algorithms, my friends. We need them to increase the model size for quantum chemistry, extend high-level calculations to realistic systems, and accurately incorporate the dynamics of molecular and physical phenomena as well as kinetics, thermodynamics, and environmental effects. Only in this way can we bridge the gap between the model and the experimental world.

Here’s the exciting part. Just like in the 60s, when digital computers came to rescue quantum mechanical applications in chemistry, quantum computing might just be what we need now.

But before we get too carried away, let’s take a step back and have a reality check.

Human adenovirus type 5. Unwinding animation generated via Protein Data Bank. As quantum computing continues to advance, will it eventually become possible to use quantum chemistry to simulate this massive structure?

Quantum computing in chemical R&D: A Reality Check

Remember when I shared that story about our involvement in the Deloitte’s Quantum Climate Change initiative with Quantistry to simulate metal-organic framework structures?

Let me refresh your memory. Metal-organic frameworks, or MOFs, are no joke when it comes to their amazing applications: from storing and separating gases (read carbon capture) to catalyzing chemical reactions like CO2 conversion and water splitting.

But here’s the tricky part: identifying the most appropriate MOFs for a particular application can be a daunting task, as not all of them possess the same qualities. So, how do we design the best possible MOF structures to meet our green goals?

One answer lies in computational design. In fact, there’s been a ton of computational studies on MOFs that have shown how to model and simulate various MOF configurations, so we can select and design the most promising candidates for our specific needs.

Let’s take a look at a quintessential example. Video 1 demonstrates how a standard quantum chemistry approach can be used to investigate the ability of MOFs to adsorb molecules like CO2. By performing similar calculations for other molecules such as water, oxygen, and methane, we could then gather information on how to experimentally modify the MOF structure to selectively capture one type of molecule while excluding others.

Video 1. Energetic profile of CO2 adsorption within MOF’s. Standard quantum chemistry – QuantistryLab view

The natural question is: how does quantum chemistry on quantum computing stack up?

In Video 2, we are using a quantum computing simulator to study the same process. But here’s the catch: we have to dramatically reduce the MOF structures to just one metal atom interacting with CO2.

And that’s where we are at with quantum computing in chemical applications.

I agree, comparing quantum computing, which is still in its infancy, to established quantum chemistry methods that have been around for decades is not entirely fair.

And yet, it’s an underwhelming result. Video 2 provides us with a reality check and reminds us that quantum computing still has ways to go before it can replace established methods.

Video 2. Energetic profile of CO2 adsorption within MOF’s. Quantum algorithm simulator – QuantistryLab view

Towards Realistic applications of quantum computing in chemical R&D

I recently had a Linkedin conversation with my former colleague Michał Bączyk, an expert of the field, about the current status of quantum chemistry on quantum computing. The highlights are:

  • Quantum chemistry awaits the quantum advantage, which requires fault-tolerant quantum computers. Near-term intermediate-scale quantum (NISQ) approaches, such as the Variational Quantum Eigensolver (VQE), face significant limitations due to the overwhelming number of measurements required for high accuracy, leading to unfavorable scaling.
  • To perform a meaningful quantum chemistry calculation, approximately 9 billion quantum gates would be necessary. This requirement highlights the complexity and scale of the computational challenges faced in this domain.
  • For quantum computers to be practically useful in quantum chemistry, there is a need for a dramatic reduction in computation time. Comprehensive calculations should ideally be completed in a matter of days, rather than months, to ensure feasibility and usability.
  • Addressing these complex challenges necessitates a diverse team of experts, with efficient collaboration and synchronization being crucial. Identifying the correct research direction is inherently uncertain and contingent on the unpredictable nature of scientific discovery.

A Final Personal Touch

So, after all this information on quantum computing in chemical R&D, you might be wondering: what’s the key takeaway?

Here it comes.

Atomistic simulations work. But they do have limitations. We can’t easily simulate an entire virus, a battery cell, or a whole polymer surface at the quantum level yet. Quantum computing might be just what we need to take us to the next frontier. The problem is, we’re not sure when this will happen.

But there is hope. Consulting companies are recommending that R&D players get involved with the quantum community and start building a competitive edge for the future. Why? Because once we achieve quantum advantage, quantum chemistry will likely be the first application of quantum computing to profit from. This ultimate holy-grail combo has the potential to disrupt research in chemical R&D.

So, if you’re an R&D player, don’t sit on the sidelines and wait for the future to arrive. Read, research, and look out for what’s to come. But, in the meantime, don’t forget that you can already start reaping the benefits of established chemical simulations.

Circling back to the question of that aged attendee at the Quantum Computing @ Work event: Do we really need quantum computing in chemical R&D?

Absolutely. While we wait for it, let’s start our simulations today.

UPDATE: I have come across a very recent Nature spotlight article written by Michael Brooks titled Quantum computers: what are they good for? According to Brooks, the current answer is clear: For now, absolutely nothing. Check it out. It presents a fair and intriguing viewpoint.


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