Turbocharging scientific discovery: with bits, neurons, qubits – and collaboration

A short line down, slow and steady, followed by five more to complete a perfect hexagon.

As a 15-year-old in Madrid, I loved my science classes. I had a particularly inspiring chemistry teacher who challenged us to memorize the entire periodic table. I cherished going to the labs, experimenting with bubbly liquids changing color as I heated up my flask – steamy substances changing phase before my eyes – and drawing funny stick diagrams of molecules.

Decades later, in 2015, I would see the same perfect hexagon in an image of a molecule taken with the Nobel Prize-winning scanning tunneling microscope designed by IBM in the early 1980s. As a teenager, I believed stick diagrams were platonic ideals, an easy way to represent the realm of the small. And here I was, staring at a very real molecule of pentacene – a row of five hexagons. I was transported back to my teenage years, when I peeked into the future. Suddenly, the future was right there in front of me.

Today, the lead scientist of that project, IBM Research chemist Leo Gross, and other researchers around the world routinely image molecules. They can even snap a picture as molecules change their charge state, and before and after a chemical reaction.

But it’s not just chemical imaging that’s making leaps and bounds. The entire scientific method is getting turbocharged. That’s partly due to cutting-edge tools like artificial intelligence (AI) and quantum computers – futuristic machines that look like steampunk golden chandeliers. It’s also due to the changing way we do science. At last, the world is starting to grasp the importance of public-private collaborations to scientific discovery. And the COVID-19 pandemic is a catalyst to several such successful global partnerships.

We should keep the momentum. Classical high-performance computers (HPC), AI and quantum computing on their own are powerful, but the potential is even greater. To truly embrace the Future of Computing, policymakers, industry and academia have to create an infrastructure in which these technologies work together, boosting and complementing each other.

At the nodes of this infrastructure should be strategic national and international partnerships, with industry, academia and governments working jointly to accelerate progress, better prepare for and address global threats, and improve the world. We need more scientists in leadership positions in government and industry. And we need to ensure seamless links between policymakers and researchers, in regular times and during global emergencies.

One global collaboration we should create is what I suggest calling the Science Readiness Reserves (SRR). This organization would help rapidly mobilize researchers who are experts in various global disasters, connecting scientists worldwide with organizations that have cutting-edge technology, such as supercomputers or quantum computers.

The impact will touch every sector of our society and economy. We have all the ingredients to make it happen: bits, neurons and qubits. The secret sauce? They have to work together.

Take pentacene, that simple molecule I once loved to draw, five perfect hexagons connected side to side. With 22 electrons and 22 orbitals, it’s among the most complex molecules we can simulate on a traditional, classical computer.

But there are billions upon billions of molecular configurations – more possible combinations for a new molecule than there are atoms in the universe.

Sifting effectively through this vast chemical space would allow us to rapidly find a specific molecule and create a new material with the properties we want. This could unlock endless possibilities of material design – for life-saving drugs, better batteries, more advanced prosthetic limbs or faster and safer cars, advancing healthcare, manufacturing, defense, biotechnology, communications and nearly every other industry. This design ability would replace our centuries-old reliance on serendipity in material discovery – something we’ve been through with plastics, Teflon, Velcro, Vaseline, vulcanized rubber and so many other breakthroughs. Even graphene – the atom-thick layer of carbon and the thinnest, strongest material known – was discovered by (informed) chance, when physicist Kostya Novoselov found discarded Scotch tape in his lab’s waste basket.

Material design has long been a slow and iterative process. Typically, researchers jog between experiments, theory and simulations – between a computer, perfecting calculations that approximate the behavior of unknown molecules, and a lab, to test if the molecules work as predicted, in a seemingly never-ending loop. Yes, high-performance computing (HPC) can simulate simple physical and chemical processes. Yes, advances in HPC have helped us pinpoint potentially useful molecules for lab tests. And yes, AI is increasingly valuable in screening novel high-performance materials, creating models to assess the relationship between the behavior of matter and its chemical structure, predicting properties of unknown substances and combing through previously published papers.

Still, it takes years to develop new materials. We need to inject quantum into the mix – and get bits, neurons and qubits to play side by side.

We all deal with bits daily, from toddlers aptly manipulating tablets to autonomous robots clearing up the site of a nuclear power plant accident. Bits power smartphones, the brain scanner in our local hospital and a remotely controlled NASA rover on Mars. Artificial neurons, on the other hand, are mathematical functions that help AI’s deep neural networks learn complex patterns, loosely mimicking natural neurons – our brain’s nerve cells.

Then there are qubits, the fundamental units of information. They are bits’ oddball and much younger quantum cousins. Qubits behave just like atoms, with weird properties of superposition (being in multiple states at once) and entanglement (when one qubit changes its state at the same time as its entangled partner, even if they are light years apart). While a classical computer has to sift through potential combinations of values of a bit (0 or 1), one at a time, a quantum computer can make an exponential number of states interact simultaneously.

Molecules are groups of atoms held together by chemical bonds, and qubits are a great way to simulate a molecule’s behavior. For material design, quantum computing will add an invaluable extra dimension: accurate simulations of much more complex molecular systems.

Beyond material discovery, quantum computers will be a boon in any field where it’s necessary to predict the best outcome based on many possibilities, such as calculating the investment risk of a financial portfolio or the most optimal fuel-saving path for a passenger jet. This technology is just entering the phase of commercialization, accessible and programmable through the cloud.

At IBM, we believe quantum computers will reach the so-called quantum advantage – outperforming any classical computer in certain use cases – within this decade.

When that happens, the world will no longer be the same – provided we don’t forget the secret sauce. Bits, neurons and qubits are powerful on their own, but working together, they will trigger a true technology revolution – enabling a new Accelerated Discovery workflow, the default scientific method of the future.

In healthcare, this will impact drug discovery and lead to better personalized medicine, more efficient bioprinting of organs and rapidly developed vaccines. AI is already helping classical computers speed up medical imaging, diagnosis and data analysis. Quantum computers could, in the future, assist AI algorithms to find new patterns by exploring extremely high dimensional feature spaces, impacting fields like imaging and pathology. Together, HPC, AI and quantum computers have the potential to help us deal with dwindling food supplies, pollution, CO2 capture, energy storage and climate change. And this method will complement our own assessments of the risks of global threats that haven’t happened yet – but could at any time.

This brings me to the other element needed to achieve the Future of Computing: national and international collaborations.

The pandemic has shown that public-private collaborations work, even when composed of industry rivals. Formed in March 2020, the COVID-19 High Performance Computing Consortium brought together government, industry leaders and academic labs to pool computing resources to support scientists conducting COVID-19 research. The collaboration also offers critical data sharing and creativity exchange.

This is the kind of collaboration we need on a global scale, beyond pandemics. The boost to the scientific method powered by quantum, HPC and AI can help address and improve many elements of society, from cybersecurity to entertainment to manufacturing. It is time to also reimagine how we use the talent in our science and technology institutions, and explore new ways to foster collaboration. This is why the proposed Science Readiness Reserves could be so important.

Science is vital to our future prosperity and health. It always has been, and always will be. If ever we needed a wake-up call to recognize the urgency of science and the power of collaboration, the time is now.