Calbee Quantum (USA)
Team leader: Garnet Chan, California Institute of Technology.
Application focus: Develops a new approach to quantum simulating materials that provides a speedup of system sizing even when targeting approximate accuracy.
Suggested real-world impact: Provides practical speedups in materials simulation, especially for semiconductor applications such as optoelectronic simulations.
Gibbs Samplers (Hungary)
Team leader: András Gilyén, HUN-REN Alfréd Rényi Department of Mathematics
Application focus: A new quantum algorithmic approach for simulating the thermalization of finite and low-temperature quantum systems.
Suggested real-world impact: Accelerating the discovery of next-generation materials by narrowing candidate parameter spaces for experimental validation.
Phasecraft – Materials Team (UK)
Team leader: Toby Cubitt, Phasecraft Ltd.
Application focus: Develops a new way to use quantum simulations to improve classical methods for modeling quantum chemistry
Suggested real-world impact: Enables faster and more reliable discovery of clean energy materials for advanced batteries, efficient solar cells and carbon capture.
The QuMIT (USA)
Team leader: Alexander Schmidhuber, MIT
Application focus: Develops a new algorithm that significantly speeds up a computer science problem known as community detection over hypergraphs.
Suggested real-world impact: Enables improved protein-protein interaction analysis to improve risk stratification and targeted therapies for polygenic diseases.
Xanadu (Canada)
Team leader: Juan Miguel Arrazola, Xanadu
Application focus: Develops a new representation and algorithm for simulating the time evolution of certain molecular processes.
Suggested real-world impact: Aiding the discovery of more efficient organic solar cells with broad implications for solar cells and photodynamic therapies.
Q4Proteins (Switzerland)
Team leader: Markus Reiher, ETH Zurich
Application focus: Develops a highly detailed framework to boost the power of quantum simulations of chemistry via combination with classical machine learning.
Suggested real-world impact: Creating a first-principles simulation pipeline for large-scale biochemical applications, from drug discovery to explaining systems like biomolecular condensates.
QuantumForGraph problem (US)
Team leader: Jianqiang Li, Rice University
Application focus: Introduces a new quantum algorithm for solving systems of linear equations that is free from a problematic dependence on condition number that has plagued previous approaches.
Suggested real-world impact: Opens possibilities for a wide range of applications with significant quantum advantages.