Quantum computers and other advanced quantum technologies rely on special quantum materials that behave abnormally under the right conditions. In some cases, scientists can even create entirely new quantum properties by carefully altering the structure of materials. One impressive example is stacking sheets of graphene and twisting them into a moiré pattern, suddenly turning the material into a superconductor.
Researchers can arrange these layers into even more complex structures, such as quasicrystals or supermoiré materials. However, it is very difficult to predict how these unusual materials will behave. Quasicrystals are so mathematically complex that their simulations can require more than 1,000 trillion, a scale far beyond the reach of today’s most powerful supercomputers.
Quantum algorithms solve mass material problems
Scientists at Aalto University’s Department of Applied Physics have developed a quantum-inspired algorithm that can process these vast amounts of aperiodic quantum matter almost instantly. Assistant Professor José Lado said the study also highlights a promising feedback cycle within quantum technology itself.
“Importantly, these new quantum algorithms can enable the development of new quantum materials to build a new paradigm for quantum computers, creating a productive two-way feedback loop between quantum materials and quantum computers,” he explains.
This advance could ultimately support the development of dissipative electronics that conduct electricity without energy loss. Such systems could help reduce increasing heat and energy demands in AI-driven data centers.
The research team was led by Lado and included postdoctoral researcher Tiago Antan, who served as lead author of the paper. QDOC postdoctoral researcher Yitao Sun; Academy researcher Adolfo Fumega; Their discovery recently physical review letter As an editor’s suggestion.
Simulation of topological quasicrystals
The researchers focused on topological quasicrystals, rare materials that give rise to unconventional quantum excitations. These excitations are particularly valuable because they help protect electrical conductivity from destructive noise and interference. However, they are distributed unevenly throughout the already highly complex structure of the quasicrystal.
Rather than directly calculating the complete structure of the material, the research team reformulated the challenge using methods similar to those used in quantum computers.
“Because quantum computers operate in exponentially large computational spaces, we used a special family of algorithms to encode these spaces, known as tensor networks, to compute quasicrystals with more than 268 million sites. Our algorithm shows how huge problems in quantum materials can be directly solved by the exponential speedup obtained by encoding the problem as a quantum many-body system,” says Antão.
Although the study remains theoretical at this stage and was carried out through simulation, the researchers say experimental tests and future applications are already on the horizon.
“The quantum-inspired algorithm we have demonstrated allows us to create supermoiré quasicrystals that exceed the capabilities of traditional methods by several orders of magnitude. This is a useful step toward designing topological qubits using supermoiré materials for use in quantum computers, for example,” Rad says.
Towards practical application of quantum computing
Rad says that if the hardware advances enough, the algorithm could eventually be run on an actual quantum computer.
“Once our method reaches the required scale and fidelity, it can be adapted to run on real quantum computers. In particular, the new AaltoQ20 and Finland’s quantum computing infrastructure could play an important role in future demonstrations,” says Rad.
The discovery suggests that the study and design of exotic quantum materials could become one of the earliest practical applications of quantum algorithms and quantum computing systems.
The project will also integrate two main areas of Finnish quantum research: quantum materials and quantum algorithms. It is part of Rad’s ERC Consolidator grant ULTRATWISTROICS, which focuses on the design of topological qubits using van der Waals materials, and the Quantum Materials Center QMAT, which aims to advance future quantum technologies.
