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An international group of researchers has achieved the world鈥檚 first multi-qubit demonstration of a quantum chemistry calculation performed on a system of trapped ions, one of the leading hardware platforms in the race to develop a universal quantum computer.听

The research, led by University of Sydney physicist , explores a promising pathway for developing effective ways to model chemical bonds and reactions using quantum computers. It is of the American Physical Society.

鈥淓惫别苍 the largest supercomputers听are struggling to model accurately anything but the most basic chemistry. Quantum computers simulating nature, however, unlock a whole new way of understanding matter. They will provide us with a new tool to solve problems in materials science, medicine and industrial chemistry using simulations," Dr Hempel said.

With quantum computing still in its infancy, it remains unclear exactly what problems these devices will be most effective at solving, but most experts agree that quantum chemistry is going to be one of the first 鈥榢iller apps鈥� of this emergent technology.

Quantum chemistry is the science of understanding the complicated bonds and reactions of molecules using quantum mechanics. The 鈥榤oving parts鈥� of anything but the most-simple chemical processes are beyond the capacity of the biggest and fastest supercomputers.

By modelling and understanding these processes using quantum computers, scientists expect to unlock lower-energy pathways for chemical reactions, allowing the design of new catalysts. This will have huge implications for industries, such as the production of fertilisers.

Other possible applications include the development of organic solar cells and better batteries through improved materials and using new insights to design personalised medicines.

Working with colleagues at the in Innsbruck, Austria, Dr Hempel used just four qubits on a 20-qubit device to run algorithms to simulate the energy bonds of molecular hydrogen and lithium hydride.

These relatively simple molecules are chosen as they are well understood and can be simulated using classical computers. This allows scientists to check the results provided by the quantum computers under development.

Dr Hempel said: 鈥淭his is an important stage of the development of this technology as it is allowing us to set benchmarks, look for errors and plan necessary improvements.鈥�

Instead of aiming for the most accurate or largest simulation to date, Dr Hempel鈥檚 work focused on what can go wrong in a promising quantum-classical hybrid algorithm known as variational quantum eigensolver or VQE.

By looking at different ways to encode the chemistry problem, the researchers are after ways to suppress errors that arise in today's imperfect quantum computers and stand in the way of near-term usefulness of those machines.听

Error suppression is at the core of research pursued in the University of Sydney鈥檚 , led by , who recently launched Australia鈥檚 first private quantum start-up, . Dr Hempel, who did the experiments while at the , now hopes to leverage Sydney鈥檚 expertise to improve what can be accomplished with these kinds of simulations.听

The paper, published today in leading journal Physical Review X, was jointly written with Innsbruck , a pioneer in quantum computing, and former Harvard professor , who has since moved to the University of Toronto.

Professor Blatt, from IQOQI in Innsbruck, said: 鈥淨uantum chemistry is an example where the advantages of a quantum computer will very soon become apparent in practical applications."

Head of the quantum science domain, , said: 鈥淭his work is a remarkable implementation of one of the most promising approaches to quantum chemistry, proving its mettle on a real quantum-information processor.鈥�

He said that Dr Hempel鈥檚 decision to move to the University of Sydney in 2016 was an excellent addition to the strong quantum team on campus. 鈥淭heoretical chemistry and materials science are strengths at this university and they will be augmented by these latest techniques in quantum computation,鈥� he said.

Trapped ions

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The two leading hardware platforms in the race to build a quantum computer use either trapped-ions or superconducting circuits as quantum bits or 鈥榪ubits鈥�.

There have been previous quantum simulations of hydrogen and lithium hydride bonds using superconducting qubits (by Google and IBM). The experiments by Dr Hempel and colleagues used laser-manipulated qubits stored in calcium ions trapped in an ultra-high vacuum environment called a Paul trap.

Entanglement is a state of matter in the quantum world where individual components of a quantum system cannot be considered in isolation and become fundamentally linked. It is an essential prerequisite for the successful operation of quantum computers.

An advantage of trapped ion qubits is that they exhibit 鈥榓ll-to-all connectivity鈥�; that is, each qubit can be arbitrarily entangled with every other. Other quantum systems don鈥檛 have this. So, part of the simulation can be achieved with a 鈥榮ingle shot鈥� operation rather than a series of concatenated processes.

Many believe this will be a huge advantage when trying to simulate chemical processes in multidimensional space, beyond 100 dimensions, that will be required for simulating complicated molecules and reactions, like ammonia or caffeine.

While trapped ion qubits retain their coherence, or 鈥榪uantumness鈥� for much longer, they run computational operations slower than superconducting qubits, but are catching up with a 100-fold speed improvement reported in 2018.

Quantum algorithm

Dr Hempel鈥檚 experiment focused on the type of algorithm used to simulate these bonds: a 鈥榲ariational quantum eigensolver鈥�.

The VQE algorithm is a 鈥榮hallow鈥� circuit, which means it needs less coherence time than the longer algorithms used by other approaches. This also means the algorithm needs fewer qubits and can achieve better results with 鈥榣ower quality鈥� qubits, using fewer gate operations.

Further,听VQE is a quantum-classical hybrid algorithm where the quantum computer effectively acts as a 鈥榗o-processor鈥� to a classical machine running the bulk of an optimization problem. This is similar to the way the floating-point unit was added to classical computers in the 1980s to help them to be more efficient with calculations.