The quantum nature of complex molecules and materials makes them very challenging to simulate. To learn about their properties, a classical computer must store and process huge amounts of data. Quantum computers bypass this by manipulating quantum systems directly, which in theory gives them an advantage over their classical counterparts. In practice, however, today’s quantum devices are sensitive to noise due to interactions with their environment, greatly diminishing their potential advantages.
In recent years, several teams (including Google in 2019 and researchers at the University of Science and Technology of China in 2020) have claimed that their quantum devices have “quantum advantage” over classical ones. However, the design of such experiments has largely played to the strengths of the quantum technologies in question rather than focusing on practical applications. This has made it difficult to assess how quantum devices would fare when applied to problems that are considered “useful” and intractable to classical computers, such as the simulation of complex quantum chemistry.
A team led by Garnet Chan of the California Institute of Technology, US, has now provided some insight into this question by performing simulations of two quantum chemistry problems on Google’s 53-qubit Weber quantum processor. The first simulation centred on a cluster of eight atoms inside the enzyme nitrogenase. This enzyme is an important component in a chemical process called nitrogen fixation, and a better understanding of its chemical properties could revolutionize fertilizer manufacturing. The second simulation focused on α-ruthenium trichloride, a material that may exist in an exotic quantum phase known as a “spin liquid” at low temperatures. Such materials are not well-understood and could have applications in data storage, topological quantum computation and even high-temperature superconductivity.
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