Semifinalist Teams

Overview

XPRIZE’s independent judging panel has recognized 20 teams from around the world as Semifinalists in the XPRIZE Quantum Applications competition. Selected from 133 submissions across 31 countries, these teams reflect a growing global engagement. Their progress is grounded in the independent validation of quantum algorithm innovations for real-world applications. Semifinalists were chosen for the plausibility and quality of their ideas.

The next stage of the competition will involve deeper technical evaluation, where teams must demonstrate credible progress toward measurable quantum advantage, supporting evidence for real-world impact, and clearly articulated technical feasibility to be selected as Finalists. The results will be announced in December 2025. To learn more about the competing teams, click on a team name below.

Semifinalists

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About the Team	Team	City	Country	Website(s)
	4colors Research	Cambridge	United Kingdom	https://4colors-research.com/
We investigate a modular, solver-centric hybrid framework that inserts quantum subroutines into mature classical optimization pipelines. A data-driven process, supported by benchmarking and learning, selects and tunes classical and quantum heuristics from a portfolio to target bottlenecks in energy and logistics instances.
	Algorithmiq	Wilmington	United States	https://algorithmiq.fi/
We develop software to predict aspects of drug behavior in the body, with emphasis on metabolism pathways that affect efficacy and safety. Quantum enabled molecular simulations inform how enzymes transform compounds, supporting earlier detection of issues and better design choices in discovery programs.
	Another Quantum	Exeter	United Kingdom	https://www.anotherquantum.com/
We address detection of hidden symmetries and structure in complex networks relevant to chemistry, scheduling, logistics, and more. Our approach uses compact encodings and structured sampling to amplify signals from symmetries in regimes where standard solvers struggle.
	Austinites	Austin	United States	https://vishnuiyer.org/
We target learning and simulation problems with real valued inputs and outputs, a common practical setting that is less served than Boolean cases. Using physical intuition from basic quantum systems, we design a routine for real valued functions that plays a role similar to foundational primitives in quantum algorithms.
	BQE	Berkeley	United States
We study dissipative state preparation algorithms that use controlled interactions with an environment to cool a system toward target states. This perspective aims at robustness and access to states that are difficult for purely coherent methods, with relevance to materials, energy technologies, and quantum biology.
	Calbee Quantum	Pasadena	United States
We investigate speedups for accurate electronic structure in practical regimes. Our formulation positions quantum routines to accelerate calculations where classical heuristics currently perform well.
	Gibbs Samplers	Budapest	Hungary
Markov Chain Monte Carlo (MCMC) methods are central to simulating complex systems in physics, biology, and finance. Quantum generalizations use superposition and entanglement to explore state spaces more efficiently. We study quantum MCMC design and analysis, and this project extends that line of inquiry. The team lead is a quantum algorithms researcher involving students and collaborators.
	HQS Quantum Simulations	Karlsruhe	Germany	https://quantumsimulations.de/
We work on quantum simulation for nuclear magnetic resonance spectroscopy (NMR), which connects directly to standard workflows in pharma and chemicals and maps naturally to spin system dynamics. We built a strong classical baseline to serve as a benchmark and to identify concrete quantum opportunities in this domain.
	Los Quanta	Los Angeles	United States
We study optimization problems in health, energy, climate, and artificial intelligence, and introduce a continuous variable variant of Quantum Approximate Optimization Algorithm (QAOA) inspired by quantum Hamiltonian methods. The approach targets problems with real valued variables and is designed to be lightweight and mathematically grounded, with theoretical guarantees on certain instances.
	Nature	Boston	United States
Many climate and energy challenges require predicting material properties that depend on quantum interactions, which are hard to model classically. We introduce a framework for simulating such properties that computes closer to the underlying physics and analyzes resource savings relative to qubit based approaches.
	ParityQC	Innsbruck	Austria	https://parityqc.com/
We introduce an algorithm that layers quantum speedups over a broad class of classical branch and bound style methods. Formal analysis informs practical heuristics and circuit constructions, and we estimate resources with current error correction considerations to assess paths toward practical advantage.
	Pasqal Alliance in Quebec for Quantum Applications	Sherbrooke	Canada	https://www.physique.usherbrooke.ca/kourtis/
We use principles from many body quantum physics to create algorithmic primitives for model counting, a core component of probabilistic inference, knowledge representation, and planning. The aim is to improve efficiency for artificial intelligence workloads at scale by deriving algorithms from physical insights and information flow behaviors.
	Phasecraft - Materials Team	London	United Kingdom	https://www.phasecraft.io/
Clean energy adoption needs better materials and catalysts, yet classical modeling can miss quantum accuracy at scale. We introduce two complementary quantum algorithm approaches for electronic structure. One augments established classical pipelines, and one addresses core materials modeling on quantum hardware.
	Q-CTRL & UoE	Edinburgh	United Kingdom	https://q-ctrl.com/
We study quantum algorithms for molecular dynamics where classical approaches become costly as systems grow. Building on recent theory for coupled oscillators, we outline a more practical pathway and examine a representative materials case, then sketch how the framework could extend to less structured systems by using problem structure and symmetries.
	Q4Proteins	Zurich	Switzerland
We are developing a quantum driven simulation workflow for biochemical problems linked to human health, focusing on large, weakly correlated systems such as biomolecular condensates. Accurate quantum sub calculations feed a machine learning model to capture dynamics and connect to rare event sampling. The aim is a reliable approach that can extend across biomolecules.
	QC assisted biological electron microscopy	Yurihonjo	Japan	https://sites.google.com/site/okamotohiroshisan/home/okamotohiroshisan-2
We frame high resolution biological imaging as a quantum computation problem where measurements damage samples. Quantum measurement theory suggests ways to reduce the number of required interactions while extracting needed information, which is directly relevant when specimens are fragile.
	QuantumForGraphproblem	State College	United States
We develop a structure aware quantum linear systems method that adapts to properties of the solution, enabling speedups beyond prior Quantum Linear Systems (QLS) approaches under stated conditions. This positions QLS as a foundation for end to end applications, including certain polynomial systems and combinatorial optimization tasks.
	Riverlane	Cambridge	United Kingdom
Accurate chemistry requires handling electron correlation efficiently. Active space methods often recover only part of it. We propose a first quantized formulation that supports larger active spaces for a more complete treatment, relevant to industrial areas such as capture and cleaner aviation fuels.
	The QuMIT	Cambridge	United States
We propose a technically rigorous super quadratic speedup for a real world application. The method combines two recent algorithmic developments. The goal is direct: real world quantum impact.
	Xanadu	Toronto	Canada	https://xanadu.ai/
We propose a low cost quantum algorithm for simulating the joint dynamics of electrons and nuclei, and discuss how it could apply to solar energy materials. The focus is on dynamics rather than only static properties, aiming for fewer qubits and lower runtimes than many alternatives.

Quantum for real-world impact

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