Mario Motta, intersecting Quantum computers with Quantum chemistry

Industries within the realm of materials science — be those that develop pharmaceuticals, semiconductors, batteries, polymers and more — make use of computational chemistry as it enables them to formulate and test ideas without any real experimental work. For researchers in these fields, computational chemistry can help provide certain insights and verify ideas that may be of interest. 

The main focus in the field of computational chemistry is determining how quantum mechanics can make the calculations more precise and expedient. Advances in computational chemistry merit attention due to the field's broad applicability, as improving the field's ability to do calculations promises more accurate results and the ability to solve more complex scenarios. This furthers the mission most industries have with their use of computational chemistry.

On September 26th, Dr. Mario Motta, a principal research staff member at IBM Quantum's T.J. Watson Research Center, held a colloquium as a part of USC’s Quantum/Physics Joint Seminar Series. The crux of Motta’s research is how computational methods for chemistry can be improved through the use of quantum computers. 

In a paper on quantum simulations for chemistry and materials science, theoretical physicist Bela Bauer explained that, “The interest in quantum computing for quantum simulations of molecules and materials stems from the fact that in many cases, the chemistry and physics of molecules and materials is best described using quantum mechanics.” 

To carry out this idea, physicists have turned to quantum computing, a slow yet highly anticipated field of research that seeks to explore the potential benefits of applying theoretical ideas in quantum mechanics to the functionality of a computer. 

In the simplest terms,  quantum computers have the ability to carry out various calculations that would otherwise take regular computers a very long time. Many fields of research, including computational chemistry, deal with equations and formulations that are often so complex that they often necessitate supercomputers. It’s very common for simulations that these hyperpowerful computers take days to compute, if not longer. 

Unfortunately, that's the limit with our current modern computer technology, leading to an obvious hindrance for any field that attempts to analyze deeply complicated ideas. 

The key idea of Motta’s research is to use quantum computers as a tool for the computationally intensive issues within computational chemistry formulations, while still relying on traditional computers for more baseline functions. 

The use of both traditional and quantum techniques stems from the highly experimental nature of quantum computers, which cost millions of dollars to create and must be kept at incredibly cold temperatures in order to function properly. Additionally, while quantum computers may be apt for extremely complex calculations, they struggle with certain consistencies that regular computers can accomplish without issue. 

Due to this, using them in cohesion seems to be the best method for applying quantum computing to computational chemistry, where quantum computers are treated as very advanced calculators. 

Further understanding of Motta’s research and the use of quantum computers in computational chemistry requires a better understanding of quantum mechanics itself.

The word “quantum,” literally meaning “how much” in Latin, was first coined by physicist Max Planck. He hoped to describe the science of properly quantifying small, fundamental particles. Quantum mechanics seeks to describe particles as “quantized,” or as individual objects. 

The major issue with quantum mechanics arises upon attempts to describe the nature of these quantized particles, which is inherently unpredictable. This idea is most popularly described through Schrödinger's Cat, a grim thought experiment where a cat placed inside a closed box could be either dead or alive until observed.

Because the cat could be either dead or alive, it is considered to be in an “in between” state. 

Applying this idea to any quantum particles leads to the idea of quantum superposition, where an electron “shot” towards a wall, for example, could exist with equal likelihood of landing on the wall at many different locations until its position is experimentally determined. Attempting to experimentally determine this exact location, however, leads to inconsistencies, and the electron will seemingly behave randomly.

Superposition is, by and large, what makes quantum mechanics special. However, while a particle may be impossible to precisely predict, its general location is still possible to determine. In the experiment where an electron is shot at a wall,  the electron will land inconsistently, but the areas it lands in will all cluster in the same general area. 

Picture a circle on the wall now. The electron could very well land anywhere in the circle. Any attempt to predict where in the circle is futile,  but probabilistically, the particle must be inside the circle. In quantum mechanics, this idea is known as normalization, where an electron's movement is considered to be wavelike, meaning that it will oscillate and vibrate through space and time unpredictably. 

But this “wavefunction” can be normalized to a general area, such that the probability of the electron’s existence is 100% known, even if the precise state of the particle is impossible to predict.

As Motta says at the beginning of one of his studies, “One of the grand challenges in modern science is the accurate treatment of interacting many-electron systems.” Quantum chemistry, in effect, is the science of quantum mechanics taken to a much larger scale, where many different electrons are interacting inside a more complicated system. 

In many cases, the probabilistic nature of quantum mechanics can work to the advantage of scientists to produce accurate results and predictions in the realm of physics. It’s often more relevant in quantum chemistry, as the subject studies many-body interacting systems, all with their own quantum tendencies.  

For example, atoms are composed of three main particles: protons and neutrons, in the nucleus, and electrons, which exist around the nucleus. The classic Bohr Model gives us a simple idea of the structure of the atom, but quantum mechanics tells us that the model is completely incorrect.

While neutrons and protons do converge together to form a nucleus, electrons’ actual movements are quite complicated. Electrons are described as existing in a state pertaining to their wavefunction, which depends on initial conditions. For atoms, this gets complicated fast: one must not only consider how the specific state and motion of one electron move, but also how it moves in relation to the wavefunctions of the particles in the nucleus. 

However, these shortcomings are not detrimental. Limits do indeed exist in computational chemistry, but this doesn't mean that the field hasn't worked with high accuracy and precision — these limits simply mean improvements can be made. 

Bauer explains in her paper that, “One must carefully identify areas where quantum advantage may be achieved.” 

Motta’s research into the use of quantum computing for computational chemistry aims to combine the old and the new, utilizing both traditional and quantum computing techniques to explore two highly complicated fields of study. As quantum computing becomes more advanced, the possibilities can be explored in a meaningful and efficient way to yield a contribution to computational chemistry. 

While the direct advantages still may be subtle, the path has been paved for many more studies into quantum computing's applications for research, with the promise of a major payoff.