Quantum Computers Will Usher in a New Age of Chemistry

An IBM quantum computer. (Image:  Lars Plougmann via  flickr  CC BY-SA 2.0)
An IBM quantum computer. (Image: Lars Plougmann via flickr CC BY-SA 2.0)

The introduction of quantum computers is set to revolutionize the field of chemistry by offering new ways to solve complex chemical problems that were hitherto impossible using existing digital computers.

Why quantum computers?

Simulation and analysis of molecules, even though it involves a straightforward calculation, is a difficult task for even the most powerful digital computers. This is because molecules contain hundreds of electrons that interact with each other in a quantum mechanical way. For digital computers, tracking and analyzing millions of such transactions is not possible. However, with quantum development, we now have a way to explore new areas of chemistry.

‘These [quantum] computers could provide new insights into such chemical systems by doing something today’s computers cannot — perform exact simulations of molecules with complex electron behavior. Today’s simulation methods, such as density functional theory, work well for many problems, especially in organic chemistry. But they fall short when it comes to inorganic systems, blurring important details about their electronic structure through approximations needed to simplify calculations so that traditional processors can handle them,” according to C&EN.

The power of a quantum computer is indicated by the number of qubits — the higher the better. Big tech firms, like IBM and Google, have developed quantum processors that have up to 72 qubits of processing power. As far as the hardware approach to quantum computers goes, companies are currently sticking with superconducting since this allows for easy scalability.

The power of a quantum computer is indicated by the number of qubits, the higher the better. (Image: D-Wave Systems, Inc. via flickr CC BY 3.0)

The power of a quantum computer is indicated by the number of qubits — the higher the better. (Image: D-Wave Systems, Inc. via flickr CC BY 3.0)

A success story

In early October, Cambridge Quantum Computing (CQC) announced that they have been successful in calculating the excited states of molecules that account for multi-reference characteristics. Conventional digital computers find it impossible to deal with multi-reference states. But CQC was able to do such calculations by implementing state-of-the-art quantum algorithms that were run on IBM’s 20 qubit processor. CQC is collaborating with JSR Corporation for the project.

“Successfully implementing quantum algorithms that account for multi-reference states represents, for the first time, a new advance in building a solid foundation for the simulation of more complex quantum chemistry applications… This quantum application, therefore, means that chemical and pharmaceutical companies and anyone who is interested in new material discovery now has a practical way to work with quantum computers that is not merely theoretical in scope,” according to Cambridge Quantum Computing.

CQC and JSR were able to make this breakthrough by following two strategies. First, they converted the computer program into instructions for qubit manipulation using CQC’s proprietary compiler in a most efficient way. Secondly, the team used quantum machine learning, which is designed for low-qubit quantum computers, to offload some of the calculations to a conventional digital computer.

The structure of a molecule. (Image: pixabay / CC0 1.0)

Using quantum computers in chemistry will allow scientists to discover newer, more efficient materials for use in solar panels and batteries. Fertilizers can be made more efficient so as to reduce energy consumption during production. Processes like photosynthesis could be understood more deeply and even replicated. Researchers may also be able to discover high-temperature superconductors that will further advance electronics.

The medical field will benefit through the use of quantum computers in chemistry. Scientists may be able to predict how pharmaceuticals will affect an individual based on their genetic makeup, allowing for the development of personalized medicine. This will ensure that the medication taken by an individual does not end up causing significant side effects. 

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