Scientists built a device to simulate movement of the subatomic particles – a task that defies even the best supercomputers. — SCMP
A Chinese team has built a quantum computer that can simulate the movement of electrons in a solid-state material, a task that could be a springboard for applications that are so far well beyond the ability of the world’s fastest supercomputers.
Tracking such subatomic particles is central to a number of scientific questions, such as what makes magnets attract each other. Unlocking such fundamental science could help crack the code to creating high-temperature superconducting materials, potentially revolutionising electricity transmission and transport.
“Our achievement demonstrates the capabilities of quantum simulators to exceed those of classical computers, marking a milestone in the second stage of China’s quantum computing research,” team leader Pan Jianwei said in a statement from the Chinese Academy of Sciences on Thursday.
The research was published in Nature on Wednesday. Pan, from the University of Science and Technology of China, co-authored the paper with USTC colleagues Chen Yuao and Yao Xingcan.
The Nature reviewers described the work as “an important step forward for the field”.
There are three generally accepted stages in the evolution of quantum computing.
The first, known as “quantum supremacy”, is when quantum computers outperform classical supercomputers on specific tasks. This goal has been met with developments such as Google’s Sycamore processor, and China’s Jiuzhang and Zu Chongzhi series of quantum prototypes.
The second – the focus of academic research today – involves creating specialised quantum simulators that can tackle important scientific problems beyond the capacity of classical computers.
The third stage will aim to achieve universal, fault-tolerant quantum computing with the assistance of quantum error correction.
Pan’s team reached the second stage by simulating the fermionic Hubbard model, a simplified model describing electron motion in lattices proposed by British physicist John Hubbard in 1963.
This model is useful in explaining high-temperature superconductivity, and superconductivity can be applied in fields including power transmission, information technology and transport. But even supercomputers struggle to simulate it.
“Simulating the movement of 300 electrons using classical computers would require storage space ... exceeding the total number of atoms in our universe,” Chen said in the CAS statement.
To achieve their goal, Pan – who is best known for leading the construction of the world’s first quantum satellite – and his team had to overcome three major challenges: creating optical lattice with a uniform intensity distribution, achieving sufficiently low temperatures, and developing new measurement techniques to accurately characterise the states of the quantum simulator.
To this end, the team combined machine-learning optimisation techniques with their earlier work on homogeneous Fermi superfluids in box-shaped optical traps to prepare degenerate Fermi gases at ultra-low temperatures.
This enabled the team to observe a switch in a material from paramagnetic to an antiferromagnetic state – or from being weakly attracted to a magnet to largely insensitive to one.
The research lays the groundwork for a deeper understanding of high-temperature superconductivity mechanisms.
“Once we fully understand the physical mechanisms of high-temperature superconductivity, we can scale up the design, production, and application of new high-temperature superconducting materials, potentially revolutionising fields such as electric power transmission, medicine, and supercomputing,” Chen said. – South China Morning Post
