In science, it is our main goal to significantly improve the understanding of the systems we work with, including those which seem to be too advanced for our current methods. By harnessing the power of technology, we can enhance our capabilities in accomplishing this goal. This precise fact is what drives the fields I have dedicated my life to: both chemistry and computing.
One excellent example of this is the case with quantum mechanics.
Although we have been achieving important milestones in the chemical and materials space for decades, science is starting to ask more of us – now, we are required to look further into the electronic structure of chemicals and truly understand how they react. This can only be achieved at a quantum level.
Fortunately, and despite many failed attempts at accurately modeling advanced chemistries in the laboratory, we may be upon the precise solution to our materials science and chemical modeling problems through computing – quantum computing, to be exact.
And, despite the progression of supercomputer technology and the integration of machine learning into the study of chemistry, it is a well-known fact that classical computing is in need of an upgrade when it comes to the most advanced calculations required in materials science, especially that of pharmaceuticals and catalyst design. Thus, we require research and investment into the power of computers built for the most complex applications.
Furthermore, although it initially appears that quantum computing is still in its infancy, companies such as Intel, Google, IBM, Microsoft, and Honeywell are all breaking important ground in terms of progression into this fascinating field.
In truth, we could be just a handful of years away from witnessing the complete digitization of chemistry and exploration of new materials space through quantum computing.
The promise of quantum computing in an evolving technological landscape
When one speaks of the essence of classical computing (the only one we’ve known for decades), we associate it with bits – a binary number with a value of either 0 or 1. This presents us with a complicated situation when attempting to simulate certain systems that contain larger and more complex molecules that cannot be studied with crude mathematical estimations: at best, you may be able to apply the Schrodinger Equation to solve simpler problems, but this can be both too time-consuming and inaccurate for trickier requirements.
Fortunately, this stumbling block was identified several decades ago in 1980, when a physicist by the name of Paul Benioff proposed a quantum mechanical model of the Turing machine, in hopes of leading computers into new applications. From then on, companies in the computer industry have done nothing but invest countless hours of time and effort into unraveling the nature of these next-generation machines that may surpass our best supercomputers in computing power. In fact, as recently as October 2019, a huge claim was made by Google: physicists at their company had achieved “quantum supremacy” by performing a calculation that would have overwhelmed the world’s fastest supercomputer.
Genuine or exaggerated as the claim may have been, quantum computing is certainly on a different level of technology. Based on qubits (or quantum bits) – two-level quantum-mechanical systems which can be ions, photons, atoms, molecules – which exist in a quantum superposition of the two states of bits (again, 1 and 0) through the phenomenon of entanglement, quantum computers can simulate electron systems with ease, allowing them to perform calculations in “different worlds” in instants.
In other words, we are closer to developing a straightforward way of deciphering age-old problems that have previously depended heavily on trial and error methods that classical computers use. In fact, some calculations which would, with our current technologies, take thousands of years… would be reduced to mere moments.
Still, there are constraints that are yet to be taken care of. Qubits are unstable and extremely susceptible to noise (external factors) which cause them to lose coherence through bitflips (errors), altering calculation results. In fact, the current most advanced computers operating under quantum mechanics are limited to using a few dozen qubits at once, as anything more than that can and will collapse into a state of decoherence. However, it is important to note that the problem isn’t limited to a number of qubits available – it is related to the stability of these units within the system.
To be used as logical qubits in computers, these particles must be trapped in control devices. Ion traps, optical traps, quantum semiconductor dots, and superconducting circuits are examples of materials used for this purpose, and studies are still determining which of these options provides the best stability. Advanced error-correcting algorithms are also being developed, as are semiconducting materials and cooling systems for the operation of computers that can handle hundreds of thousands of stable qubits in place of less than a hundred at a time.
Interestingly enough, quantum computing is not an actual replacement to classical computing, but an incredibly powerful addition to it: in the words of Jim Clarke, Director at Intel’s Quantum Hardware Research Group, “I don’t expect quantum computers to cannibalize high-performance classical computers. More than likely you will have a quantum computer stationed next to either a supercomputer or a distributed cloud platform, working in tandem.”
This may be the most relevant fact about quantum computers – research into these new computing creations is not about competing for a foothold in the next stage of computing, but about better servicing fields that cannot be explored with current systems and timescales, such as chemistry.
But how does chemistry work on the quantum level, and what type of obstacles are we looking at?
Quantum chemistry – the last frontier of materials science
Our methods in studying chemical reactions have been effective for centuries, allowing us to make great progress into the fields of materials science and manufacturing, but more recent requirements in these applications have pushed us to look further into what makes these reactions tick – their electrons. In other words, our needs have taken us to apply quantum mechanics to chemistry.
By understanding molecular dynamics and electronic structure, the crucial need of identifying the ground-state energy level (and excited states) of common and not-so-common atoms allows scientists to pinpoint their exact values of stability and/or reactivity. This is especially crucial in the creation of catalysts, for example, which are created precisely for the acceleration of reactions – understanding the inner architecture of a chemical reaction (the interaction of electrons), catalysts with unprecedented rates of selectivity and yield will be produced within the next decade.
In quantum chemistry, it is crucial to possess a level of technology that allows for simulation of molecules and materials before producing them – as always, technology is the closest ally to the chemical manufacturing industry – and it is clear that classical computers have found important stumbling blocks when attempting to provide us with accurate predictions of molecular properties. Small properties have been studied with mixed success using density-functional theory (or DFT) and similar methods based on imprecise estimations, but it has limited applications in the study of proteins, large molecules, and complex solids.
In other words, for all that science has traditionally attempted to estimate quantum mechanical systems in chemical manufacturing and materials science (obviously due to technological restraints), it is clear where we are headed next: integrating quantum computing into chemistry.
The advantages of fusing two quantum universes – chemistry and computing
One significant problem in the chemical industry is the lack of results and feasibility in previous digitization efforts that have been made when attempting to apply a single programming language to manufacturing fields. For example, the analysis of data in chemical laboratories: it is a well-known problem that different companies operate on their lab results under diverse systems, and that there are no widespread operating systems built for chemical manufacturing purposes that can ease the exchange of information between organizations.
Another common issue is the lack of databases in terms of chemical parameters, meaning that each company exploring chemical or materials space must commit investments of time and monetary resources to studies that have already been accomplished by others before them, but which are locked behind patents and personalized software.
Quantum computing can help centralize the analysis of chemical information, thanks to the fact that it is still in a nascent stage and will require plenty of collaboration between chemical companies and computer developers, and because many companies will prefer to outsource their quantum computing needs to a central cloud, instead of investing thousands to millions in bringing such a machine into their own plants or laboratories.
With this integration of quantum mechanics to chemistry and the evolution of modeling simulation, we are certain to see the growth of quite a few fields of chemical and materials manufacturing:
– Companies creating semiconductors, magnets, and superconductors will now be able to more precisely predict and optimize the structure of their solid-state materials.
– Tech companies creating OLED displays will now move away from endless trial-and-error methods to achieve desired brightness and hue of colors: thanks to simulation techniques, materials are simulated accurately before the first stage of production even begins.
– Catalyst design will be made more accurate, reducing research costs and, more importantly, making catalyzed processes less energy intensive. In other words, catalysis will advance exponentially.
– Drug discovery depends heavily on biochemical interactions; by optimizing the simulation process of pharmaceuticals and helping labs accelerate the research process, better drugs will be synthesized in less time.
– The in-depth study of molecular structure allowed by quantum computers will enable researchers to take the investigation of proteins and biomaterials to the next level and will allow for the creation of next-generation optical materials.
Finally, in a fascinating twist of events for those of us motivated by the chemical manufacturing industry’s positive trend towards greener technologies, quantum computing will allow chemical manufacturers to spend less by reducing waste, lowering energetic requirements, creating more efficient fuels, and generally mitigating the effects of chemistry on the environment.
“It's not that we're looking to replace one type of computing with another.
We're simply combining two different forms of computing - each with their own immense advantages - to venture into a dimension that we have never been able to study before.
Quantum computing is the answer for the next age of materials science.”
— Ryan Esner, Environmental Fluids CEO
We will not see all of these changes in the next five to ten years, at least, but it is inevitable: the fields of chemistry and materials science will soon be looking at how electrons interact through the eyes of quantum computing.
Companies in these spaces looking to invest and/or collaborate in this next stage of computing will be among the pioneers of a new age of manufacturing, and it will be crucial to join this trend of digitizing chemistry early, as competition will be fierce by the end of the decade.