Dec 10, 2025

These 7 teams demonstrate the potential to pioneer quantum algorithms that can outperform classical computers and solve real-world problems.

General summary

The XPRIZE Quantum Applications competition has named seven finalists who are developing quantum algorithms to solve real-world problems. These teams will share $1 million now and compete for an additional $4 million in 2027. The finalists will now focus on performance assessments and establishing real-world advantages, while other teams can re-enter in 2026.

Summaries were generated by Google AI. Generative AI is experimental.

Today we’re announcing the seven finalists in the XPRIZE Quantum Applications competition, a 3-year, $5 million global challenge backed by Google Quantum AI, Google.org and GESDA.

Chosen from 133 submissions worldwide, these seven finalist teams are pioneering quantum algorithms that can outperform classical computers and solve real-world problems, such as those outlined in the United Nations Sustainable Development Goals (SDGs). They will share a $1 million prize at this stage, with an additional $4 million to be awarded in March 2027, including $3 million for a grand prize winner.

Google Quantum AI’s mission is to build best-in-class quantum computing for otherwise impossible problems. We have made progress with recent breakthroughs on our Willow chip, including on error correction and our Quantum Echoes algorithm, the first demonstration of verifiable quantum advantage. As we outline in our five-stage framework for application development, developing use cases for quantum computing is a multi-step process. We believe the XPRIZE will accelerate progress through these difficult middle stages:

• Finding the right problem instances (Stage II): This requires pinpointing concrete, verifiable problems where quantum computers will outperform the best classical methods, rather than just abstract problems.
• Establishing real-world advantage (Stage III): This connects the mathematically proven quantum advantage to specific, valuable use cases in industries like materials science or drug discovery.

Finalists were selected by an independent and renowned, cross-sectoral panel of quantum and domain experts, and announced during Q2B Silicon Valley.

Meet the finalists

The Finalist cohort is tackling some of the world’s most complex problems across materials science, health, and optimization:

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.

What’s Next: Phase II and the Road to Deployment

Finalist teams now move to Phase II of the competition, which is focused on performance assessments, including benchmarking against state-of-the-art classical methods, establishing real-world advantage, and detailed resource estimates of the computational cost and feasibility of deployment.

We are excited about the results of the competition so far, and energized by recent progress in quantum computing applications development. Teams not selected as Finalists may re-enter through the Phase II wildcard round, opening in early 2026.