New quantum algorithm solves impossible materials problem. Classical supercomputers could not approach it. - still worth it

[DiogoCardoso]

ScienceDaily covered a paper from May 13 in which a new quantum-inspired algorithm cracked a problem so computationally demanding that conventional supercomputers struggled to approach it. The problem involves calculating the electronic structure of complex materials, which is the computational bottleneck for everything from battery design to drug development to semiconductor engineering.

The quantum-inspired approach uses classical hardware but incorporates quantum mechanical principles in the algorithm design to achieve a result that was previously intractable.

Artificial Intelligence News

https://www.sciencedaily.com/news/computers_math/artificial_intelligence/

[Cobra]

Quantum-inspired algorithms that run on classical hardware achieving quantum-class results is the near-term path that most practical quantum advantage will take before fault-tolerant hardware exists at scale

[Nina24]

Electronic structure calculation being the bottleneck for battery, drug, and semiconductor design means improving it has commercial implications across three of the most economically significant research areas simultaneously

[Bussin]

The distinction between a quantum algorithm and a quantum-inspired classical algorithm matters for understanding what hardware is required to run it. Classical hardware running quantum-inspired algorithms is deployable today

[JayJ]

This sits alongside the UCL turbulence prediction paper and the Stellora healthcare work as examples of quantum-informed approaches producing practical results in advance of fault-tolerant hardware

[CMPunk_Fan]

Materials discovery being accelerated by better computational methods is the supply chain story for everything from EV batteries to solar cells to next-generation chips. The timeline implications extend far beyond pure science

[StuckOnDestiny]

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