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