We’ve awarded microgrants to over 72 teams across 23 countries, resulting in 17+ completed or planned publications, welcoming 13 people into the field, and helping to form 2 new startups and 1 new non-profit.

Could you be next? Apply here.

Grants Made

2023 Grants

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  To Alejandro Montanez-Barrera to simplify benchmarking of optimization problems in OpenQAOA. [arXiv]

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  To Shinichi Sunami and Masato Fukushima to develop Graphix, an open-source library to optimize and simulate measurement-based quantum computing.

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  To Harshit Gupta to fruther develop a timeline debugger for the Qiskit transpiler.

2022 Grants

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  To Lev Stambler to write a highly performant decoder in Rust for quantum error correction.

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  To Stefanie Muroya Lei and Christoph Kirsch, to develop develop QUARC within the Unicorn framework, a bounded model checking that verifies classical programs using the best classical and quantum algorithms.

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  To Omid Khosravani to develop adaptive quantum process tomography techniques through the use of reinforcement learning.

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  To Kaitlin Gili to conduct an outreach project called Iteration One, sparking curiosity among U.S. high school students with the most limited access to physics and computer science knowledge.

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  To Abdullah Khalid to write a methods focused guide to quantum error correction.

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  To Hong-Ye Hu, Yi-Zhuang You, and Susanne Yelin, to open-source PyClifford, a fast and flexible Python-based Clifford + few T gates simulator.

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  To Tim Weaving and Alexis Ralli, to develop Symmer into a fully scalable qubit reduction toolkit for the quantum computing community.

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  To Paria Naghavi to add code, visualizations, and conceptual content to the QIR Book, to build knowledge bridges for incoming users to the ecosystem.

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  To Maria Maryam and Karen Rezkalla, to improve the database and develop the Python client API of Metriq, as part of the Qubit x Qubit Early Quantum Career Immersion Program.

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  To Haoxiang Wang and Min Li, to develop a high-level API for variational quantum algorithm (VQA) training with quantum error mitigation.

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  To Matt Lourens, to develop Dynamic QCNN, a tool to generate quantum convolutional neural network models programmatically.

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  To Steven Thomson, to create Inside Quantum, a new quantum technology themed podcast focusing on the people behind the research, aimed at showcasing a diverse range of voices in order to promote inclusivity and inspire the next generation of quantum technologists.

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  To Ayush Tambde, to develop a Higher-Level Oracle Description Language (HODL), so that the compiler can interoperate with other frameworks/languages (OpenQASM, QIR). [arXiv]

2021 Grants

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  To Lingling Lao, to develop 2QAN, a compiler that optimizes quantum circuits for 2-local qubit Hamiltonian simulation problems. [arXiv]

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  To Ben Braham, to develop a framework to define and run variational quantum algorithms in QuTiP, leveraging quantum optimal control to parametrize control pulses.

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  To Hoang Van Do, Elie Gouzien, and Saesun Kim to develop a Heisenberg-picture simulator and universal gate set for high dimensional systems.

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  To Abuzer Yakaryilmaz and the QWorld team to organize QCourse511, a graduate-level online course on quantum computing and programming.

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  To George Watkins, Alex Nguyen, Varun Seshadri, and Keelan Watkins to further develop a lattice-surgery-based quantum error correction compiler. [Demo at latticesurgery.com]

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  To Brian Shi to build Qdot, a Scala 3 library (QASM-transpiler) that allows Scala/Java developers write native quantum/hybrid programs.

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  To Gerald E. Fux and Dominic Gribben and the TEMPO collaboration to develop an open source python package for simulating non-Markovian open quantum systems using tensor network techniques. In particular, the grant will enable adding two new features: studying the role of environment correlation functions through observables of the system and of multiple environments coupled to a single system.

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  To Utkarsh Azad and Animesh Sinha to build a visualization tool called qLEET for exploring loss landscapes, expressibility, entangling capacity and trainability for noisy, parameterized quantum circuits.

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  To Danny Samuel to improve and extend the performance of variational quantum algorithms with error mitigation, benchmarking them with mirror circuits.

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  To Lorenzo Buffoni, to develop SQWalk, A Stochastic Quantum Walk simulator based on QuTiP.

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  To Jonas Schwab and the ALF collaboration, to develop pyALF, the Python interface of the ALF project, a powerful and flexible package for Quantum Monte Carlo simulations of fermion systems.

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  To the teams at Orange Quantum Systems and Qblox, to develop Quantify Core, an open-source Python-based data acquisition and experiment control platform for quantum computing and solid-state physics experiments. It is built on QCoDeS and is a spiritual successor of PycQED.

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  To Diego Ariel Wisniacki, Martin Larocca, Emiliano Manuel Fortes, Julian Matias Ruffinelli to develop Krylov evolution algorithms integrated with QuTiP. [K-GRAPE, Krylov-Loschmidt]. It is hosted at GitHub, and explained on Medium.

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  To Reem Larabi to add more visualization improvements to the %debug tool in Q# by expanding its current set of controls.

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  To Alessandro Luongo and Armando Bellante to develop Quantumalgorithms.org an open-source web book collecting in an organized manner lectures notes around quantum algorithms for information processing, data analysis and machine learning.

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  To Goutam Tamvada and Douglas Stebila to develop VeriFrodo, an open-source package implementing a lattice-based quantum-resistant cryptographic algorithms in Jasmin within the Open Quantum Safe project.

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  To Owen Lockwood to develop a software package using classical deep reinforcement learning to improve quantum optimization, both for quantum simulations and hardware integration.

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  To Daniel Stilck França to develop a software package that will help benchmark the limitations of noisy quantum devices for solving optimization problems. [arXiv] [QIP talk on the technique].

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  To Rhea Parekh and Stephen DiAdamo to further develop Interlin-q, a distributed quantum-enabled simulator integrated with QuNetSim. [arXiv] [blog post]

2020 Grants

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  To Dariusz Lasecki to build an open-source Python library that delivers easy-to-use high-quality pre-trained machine learning models to predict good QAOA starting parameters for selected classes of problems.

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  To Oscar Higgott, to continue developing and maintaining PyMatching, a Python package for decoding quantum error correcting codes with minimum-weight perfect matching (MWPM). [arXiv]

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  To Nicola Mosco, to develop Marta CT, a software package in Julia for medical diagnostics that uses a quantum-tomography-inspired technique for state reconstruction in order to reduce the radiation dose patients receive. [arXiv]

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  To the QWorld, team, a follow-up grant to be incorporated as a non-profit organization and step up their activities. [ arXiv]

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  To the team at Qubit By Qubit, to develop courses and materials to educate a diverse ecosystem of open source quantum contributors.

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  To Pedro Rivero Ramírez for QRand, a multi-platform quantum random number generator library integrated with numpy.

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  To Jacob Miller for a PyTorch toolbox for matrix product state models.

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  To Rochisha Agarwal and Natansh Mathur to create a Machine Learning Textbook with integrated code and visualization.

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  To Mark Cunningham to explore applications of quantum computing to medical imaging.

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  To Spencer Churchill to write Quantum Tales, a series of fairy tales where quantum algorithms are applied to resolve their conflicts.

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  To Daniel Tan to develop and open source the Optimal Layout Synthesizer for Quantum Computing, OLSQ. This compiler beats other benchmarks on optimal layout of computational qubits onto physical qubits. [ arXiv] [ arXiv]

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  To Lia Yeh and the fullstackquantumcomputation.tech team to build community-driven open-source educational resources for quantum computing. [ site] [ discord]

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  To Olivia De Matteo and Sarah Kaiser to build and optimize an open source Q# library for quantum RAM. [ github]

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  To Roger Luo to continue the development of Yao.jl, a software for solving practical problems in quantum computation research. The grant will support Yao's new DSL compiler development, which includes an extensible DSL infrastructure, Julia-based frontend, Julia AST and QASM code generator, and a quantum circuit simplification infrastructure based on pattern matching. [ github]

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  To Mark Shui Hu to further develop Qsurface, a simulator package for surface codes. The grant will improve visualization methods and facilitate the collaboration of an open, modular platform for surface code simulations. [ github] [ Docs]

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  To Muhammad Usman Farooq for a research internship in the Yao group that lost funding due to the COVID crisis. They aim to construct a quantum channel with a Classical input Reverse Information Cost of zero.

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  To Dariusz Lasecki to build an open source QAOA library and examples using Q#.

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  To Stephen DiAdamo to develop QuNetSim, a quantum network Python simulation framework for investigating quantum network protocols. [ github] [ arXiv] [ video]

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  To Vincent Russo to support toqito, an open source Python toolkit for quantum information theory with extra functionality to study non-local games. [ software] [paper] [video]

2019 Grants

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  To Klementyna Jankiewicz and Piotr Migdal to develop widgets that embed visualizations of quantum states and ops into blog posts, interactive textbooks, and explorable explanations. These are extensions from their work on a new version of QuantumGame. [ QuantumGame - Github] [ Bra-Ket-Vue - Github]

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  To Abdul Karim Obeid and the QHyp project, developing research and software at the intersection of quantum contextuality and probabilistic programming. [ Publication]

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  To Qrack an open source, comprehensive, GPU-accelerated framework for simulating universal quantum processors. (Qrack has also passed the proof-of-concept stage for distributed simulation.) [ Docs] [ Benchmarks]

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  To Alexandru Paler and Daniel Herr to develop zxQentiana an open source, resource estimator for time and space tradeoffs for the surface code that uses pyZX compilation. [ github]

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  To QCousins to support their mission of welcoming a young and diverse group of programmers into quantum computing. The grant will fund more of their quantum programming workshops and educational materials. [ Workshops we funded]

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  To SciGym to build open source library for reinforcement learning environments in science. Examples include training a surface code decoder.

2018 Grants

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  To Gate42 to build an open source library for quantum error mitigation and dynamical decoupling: both techniques for compiling programs to reduce the effect of noise in quantum processors.

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  To the NISQAI project to build a library for machine learning with near-term quantum processors. [Proposal] [Video] [arXiv] [publ.]

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  To Petar Korponaić to support and extend the Quantum Programming Studio, an open source in-browser IDE for multi-platform quantum programming. This project became the startup Quantastica that makes cross-platform quantum software. [Twitter] [Website]

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  To Lucas Saldyt to prototype a probabilistic programming language for quantum computing. This library is called Qurry.

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  To Aleks Kissinger and John van de Wetering to support the development of pyZX, an optimizing quantum circuit compiler based on a diagrammatic semantics from monoidal categories. This work resulted in two publications: (i) an overview of the pyZX library and (ii) benchmarks showing that pyZX outperforms the state of the art in reducing T-Count. [arXiv1] [arXiv2] [Video] [Website]

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  To Adam Kelly to extend his work on the open source QCGPU high performance quantum circuit simulator [ github] [ libraries] [ website].

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  To Ntwali Bashige to develop the Avalon quantum programming language and quantum programming communities in India and D. R. Congo.

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  To Michał Stęchły to build a traveling salesman solver web application and tutorials for Forest based on the quantum approximate optimization algorithm. [github tutorials] [blogpost] [web app source code (it's not live anymore)]

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  To Carlos Bravo Prieto to implement the Adiabatically Assisted Variational Quantum Eigensolvers in Forest, a hybrid classical-quantum algorithm for solving optimization problems. [github tutorial] [arXiv] [publication]

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