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| Novi Research | Jonakee Reynolds | Vice President |
Small Business
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Other Energy Technologies
| Novi Research specializes in assisting with commercialization, by connecting the research to business goals so that the data can be used to inform actionable strategies that drive growth, spark innovation, and strengthen the long-term success of a business. Novi brings a powerful combination of market expertise, strategic insight, and data-driven methodologies to support comprehensive market needs assessments and commercialization strategies. With a long track record of proven experience, we help clients identify, segment, and effectively target key markets to unlock new opportunities and optimize growth. Our structured process ensures we find precise product market fit across the sectors with the greatest opportunities, and push new technologies into the marketplace.
Our approach integrates stakeholder interviews, precision data analysis, segmentation, and ethnographic research, followed by strategy workshops. This systematic process provides a comprehensive understanding of market dynamics, competitive landscapes, and strategic positioning, as well as streamlining the process through the RDD&D continuum. By leveraging our extensive experience, we deliver insights that highlight market size, growth potential, and optimal pathways for product-market fit. We take our work a step beyond traditional research, by facilitating connections with key stakeholders or first customers, ensuring market entry.
Novi Research is uniquely positioned to translate complex technical data into actionable market insights across a wide range of industries, including cleantech, consumer technology, healthcare, and AI. Our experience with both deep tech startups and established corporations enhances our ability to identify early market trends and develop strategies that are responsive to regulatory challenges and market shifts. Our team’s commitment to research and strategic follow-through empowers clients to move from data to action seamlessly, whether they are aiming for market entry, strategic partnerships, or long-term growth plans. We ensure each project’s success by aligning data-driven strategies with clear, actionable recommendations that maximize commercialization impact and outcomes.
We are looking to partner with technologies/companies to assist primarily with commercialization, or market needs assessment, although we are also well equipped to conduct primary research in the marketplace and gather necessary data. |
| CA |
| Los Alamos | Ping Yang | Deputy Director, Seaborg Institute |
Federal Government
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Other Energy Technologies
| My research delves into unveiling electronic structures, spectroscopic properties, reactivity, and dynamical behaviors of d/f-element systems, including transition metals, lanthanides, and actinides. I am broadly interested in applying advanced high-performance computing frameworks and developing new computational methods for long time-scale simulations of complex chemical systems. Passionate about harnessing autonomous discovery in chelation chemistry—which can be applied to separation and energy storage—I aim to unlock the vast chemical space using data science to develop innovations for a clean energy future. |
| NM |
| Quar-Tech | Brian Lovejoy | CEO |
Individual
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Other Energy Technologies
| My expertise lies in developing business strategies to accelerate the deployment and commercialization of advanced technologies in Negative Carbon Emissions and Quantum Technologies.
History has demonstrated that market demand and product-market fit are the key drivers of technological innovation that affects our daily lives. Disruptive innovations, from Netflix to personal computers, exemplify how they are transforming business and mission models.
I have conducted research on specific use cases and developed a preliminary go-to-market strategy. I will validate the product-market fit before developing solutions, ensuring that the demand and pain points drive the technical innovation needed to achieve our negative carbon goals in clean energy. |
| NY |
| National Renewable Energy Laboratory | Wesley Jones | Principal Scientist |
Federally Funded Research and Development Center (FFRDC)
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Other Energy Technologies
| The National Renewable Energy Lab hosts the largest high performance, scalable, accelerated computing systems dedicated to a clean energy future. Our deep understanding of the research opportunities in renewable energy, synergistic end use, and wholistic energy system integration including the electric grid and storage technologies provides a foundation of knowledge and expertise, computational, scientific, engineering and analysis. Our understanding of the technical challenges and opportunities in the utilization of computational chemistry and material science extend to the potential use of quantum computing. Our Quantum Readiness activity leverages that foundation to identify classes of applications, algorithms and challenges that might be fruitfully investigated to have impact and bring the renewable, clean energy horizon closer to full realization. |
| CO |
| University of Tennessee | Arpan Biswas | Research Assistant Professor |
Academic
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Other Energy Technologies
| Biswas's research expertise broadly covers in • ML algorithm design, development and domain specific applications for accelerating scientific discoveries. • Bayesian optimization/ Multi-objective/ Multi-fidelity/ Cost aware/ Robust Bayesian Optimization. • Machine Learning/Deep Learning. • Automated and Autonomous Experiments. • Uncertainty Quantification, Propagation and Robust Optimization. • Human-AI collaboration. • Physics driven ML. • Advancement in Data Analysis.
He is currently serving as co-PI of the project at Center of Advanced Materials and Manufacturing (CAMM, UTK), funded by MERSEC program "Taming the Complexity of Quantum Materials with Artificial Intelligence" where his lab collaborate with theorist and experimentalist to develop novel autonomous workflows and digital twins to accelerate quantum material research via data, physics and human co-operative ML methods. In the MERSEC project, he has supported another co-PIs lab to developing a BO framework to optimize the Critical Point of phase transition of the super expensive Bose-Hubbard Model in minimal qMC and DMRG simulation (each simulation takes 2 weeks to 1 month). His lab also focus to develop specialized optimization techniques to solve for complex design space (eg. non-smooth, noisy, discontinuous etc.), where standard methods will fail. The global objective of our lab is to build tools with appropriate trade-offs between accuracy, cost, reproducibility and usability. His work at Oak Ridge National Lab in developing advanced autonomous material characterization workflow was highlighted in the Story tip for the ORNL news site, “New system combines human, artificial intelligence to improve experimentation”. Also, he work in supporting another research at ORNL in developing autonomous material synthesis workflow was highlighted in Story tip for the ORNL news site“, “Researchers harness AI for autonomous discovery and optimization of materials”.
Dr. Biswas is interested in teaming for this FOA as this suits direct to his current research interest and aim to broad the applications in the domain of quantum material research. |
| TN |
| Q-CTRL | Yuval Baum | Head of QC research |
Small Business
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Other Energy Technologies
| Q-CTRL is a global leader in quantum control and error handling in quantum computers, with validated technology, commercial software products, and global deployments in major cloud platforms including IBM Quantum. We specialize in quantum control infrastructure software tools for R&D professionals, platform vendors, and end-users seeking maximum performance from quantum hardware and continually develop innovative techniques to accelerate the path to useful quantum computers. Our core product offering in error suppression is Fire Opal, an out-of-the-box solution for minimizing error and boosting algorithmic success. The fully automated performance management pipeline applies best-in-class, AI-driven error-suppression techniques which reduce errors at the gate and circuit level and enable efficient use of available quantum hardware. This technology is validated to deliver enormous performance enhancements on commercial and research prototype hardware - orders of magnitude beyond any competing approaches. It is validated across a variety of QC architectures and qubit modalities, including superconducting circuits, trapped ions, neutral atoms, NV diamond, and semiconductor spins. In developing Fire Opal we have driven new industry-leading techniques in autonomous AI-driven quantum logic design, high-efficiency compilation, automated circuit-level crosstalk cancellation, and automated layout selection – insights published in the peer-reviewed literature. These techniques carry no overhead, and are validated to be fully compatible with quantum error correction encoding and error identification. Fire Opal includes hardware-optimized application solvers, allowing researchers to run entire algorithms without any hardware expertise or exposure. Q-CTRL Optimization Solver enables solutions to utility-scale optimization problems on quantum hardware. The entire workflow is noise-aware and leverages Q-CTRL Performance Management under the hood, allowing the execution of problems at full QPU scale and achieving results that are otherwise impossible on comparable hardware. Q-CTRL has assembled the world’s foremost team of expert quantum-control researchers. The Q-CTRL team has expertise in algorithm development, compilation, error suppression, and hardware-level device physics. Our team has repeatedly demonstrated novel methods and works with industry, academia, and national labs and has a track record of providing valuable solutions to government and industry problem. |
| CA |
| University of Victoria | Thomas E. Baker | Canada Research Chair |
Academic
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Other Energy Technologies
| Thomas E. Baker holds a Tier 2 Canada Research Chair in Quantum Computing for Modelling of Molecules and Materials in the Department of Physics & Astronomy and also the Department of Chemistry at the University of Victoria. He is also an affiliate member of the Centre for Advanced Materials and Related Technologies (CAMTEC) at the University of Victoria. He has a broad background in density functional theory, quantum algorithms, quantum information, and entanglement renormalization methods. He has active research projects on new methods for quantum computers that relate to quantum chemistry and computer science. He is the lead-developer of the DMRjulia entanglement renormalization library and has written introductory materials for it.
In 2021, he was a Fulbright U.S. Scholar at the University of York in the United Kingdom. From 2017-2020, Prof. Baker was the Prized Postdoctoral Scholar in Quantum Sciences and Technology at Institut quantique à l'Université de Sherbrooke.
Prof. Baker is a member of the education committee for the NSERC CREATE program in Quantum Computing affiliated with Quantum BC. He is the Principal Investigator of the quantum photonics, algorithms, light-matter interactions for technology (QuALITy) collaboration at the University of Victoria. He remains committed to building a diverse research group capable of handling the multitude of challenges related to his wide research interests. |
| British Columbia |
| Polaris Quantum Biotech Inc | William Shipman | CTO |
Small Business
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Other Energy Technologies
| Polaris Quantum Biotech (PQB) developed the first quantum computing (QC)-based drug discovery platform, supported by a team of computational chemists and quantum software engineers. We translate complex chemistry into algorithms for QC, achieving faster, more effective results than traditional methods, with proven wet-lab success that sets us apart. Starting from protein-binding pockets associated with disease, PQB identifies preclinical drug leads. Traditional methods take 3-10 years using classical computing and wet-lab efforts, but PQB accomplishes this in under three months using QC. Over four years, we’ve identified novel molecules, with a 30% success rate in wet-lab assays—significantly higher than the industry standard of 2-5%. PQB’s QC system efficiently searches massive chemical spaces for top molecules that may be developed into future medicines. It is faster than classical drug discovery processes, which are slow, iterative, and have a failure rate exceeding 80%. PQB’s methods and hardware escape the limitations of classical methods that depend on slow calculations and require training datasets. QC is 500x faster, uses less energy, and is more financially practical than other technologies. PQB’s platform treats drug discovery as a multi-objective optimization problem, solved using a quantum annealer. We explore vast chemical spaces (10^30 possibilities) to identify optimal molecular candidates. Our Quadratic Unconstrained Binary Optimization (QUBO) and Constrained Quadratic Model (CQM) algorithms, deployed on D-Wave’s quantum annealer, have successfully identified drug leads for difficult protein targets. PQB also employs advanced molecular modeling and molecular dynamics simulations using tools like MOE and NAMD3 to deeply characterize protein structures. We’ve expanded from small-molecule design to peptides, using databases like the Protein Data Bank and chemistry tools like RDKit to generate critical ligand and pocket descriptors. Our methods handle complex protein-protein interactions (PPIs), a very challenging drug target. PQB’s engineering team ensures platform scalability and performance with a test-driven development process and continuous integration (CI/CD) pipelines. By utilizing cloud tools and quantum resources like Google Cloud and D-Wave, PQB efficiently manages large-scale computations, positioning us as a leader in quantum-powered drug discovery. |
| NC |
| Pasqal | Michelle Lampa | US Government Sales Executive |
Small Business
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Other Energy Technologies
| We, at Pasqal, are a globally recognized neutral atom-based, analog quantum computing company creating enterprise-grade devices. As data sets become more complex, we strongly believe that quantum computing is needed to accelerate the groundbreaking work in the industries that affect our everyday lives, e.g. life sciences, energy, finance, defense, among others. Just this year, Pasqal delivered two devices to the High Performance Computer-Quantum Simulator (HPCQS) in France and we will deliver another device to the Jülich Supercomputing Centre (JSC) at Forschungszentrum Jülich by the end of this year. We also just sold an on-prem device to the largest oil and gas company in the world. In order to enhance the usability of various qubit modalities, we also announced a joint collaboration with IBM to define classical-quantum integration for quantum-centric supercomputers. As a full-stack quantum computing company, we have also worked closely with commercial partners from the Energy, Life Sciences, Aerospace, Defense and Aerospace, Transportation and Logistics, and Finance industries and are actively working with US National Labs to advance their research.
We are looking for those interested in having a quantum computing device on-premise, those interested in accessing our quantum computing device via the cloud, and partners to collaborate on via Proof of Concept or ARPA-E projects to advance research in materials discovery and development (e.g. hydrogen fuel cell simulation, new battery discovery), optimization problems (e.g. energy management systems), among others. |
| MA |
| Rensselaer Polytechnic Institute | Zhiding Liang | Assistant Professor |
Academic
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Other Energy Technologies
| Zhiding Liang is an assistant professor at Rensselaer Polytechnic Institute (RPI) CS department. He received PhD degree from the Department of Computer Science and Engineering at the University of Notre Dame. His current research focus on hardware software codesign for quantum computing, including calibration optimization, quantum control, quantum compiler and quantum architecture design. The results of his research have been published in prestigious conferences and journals, including DAC, ICCAD, QCE, TCAD, TQE, and TVCG. He has been selected as a DAC Young Fellow in both 2021 and 2022. He has also been nominated as the recipient of the Edison Innovation Fellowship by the IDEA Center at the University of Notre Dame. He is devoted to quantum education and outreach; he is the co-founder of the Quantum Computer System (QuCS) Lecture Series, an impactful public online lecture series in the quantum computing community. He also led the organization of the first ACM/IEEE Quantum Computing for Drug Discovery Challenge at ICCAD, a top-tier computer science conference. |
| NY |
| Carnegie Mellon University | Reeja Jayan | Professor of Mechanical Engineering |
Academic
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Other Energy Technologies
| Our research lies at the intersection of electromagnetics and materials science. We pioneered instrumentation to watch chemical reactions taking place under microwave stimulation. We mapped out dynamic structural transformations and measured kinetic parameters as reactions progress from precursor molecules towards various structural polymorphs. We apply this knowledge of field-matter coupling mechanisms to lower temperatures and to control reaction pathways, thus enabling the sustainable synthesis, processing, and manufacturing of ceramic materials. Ceramics (e.g., metal oxides) are key to realizing a renewables-based energy future because they form the core of critical technologies necessary for energy harvesting, conversion, and storage. Nevertheless, ceramic synthesis and crystallization remains energy intensive (requires high temperatures 500-2500 °C) and highly polluting.
Using high-resolution in-situ synchrotron x-ray total scattering measurements that monitor atomic displacements in real-time, we demonstrated the first experimental evidence that microwaves have the unique ability to transfer energy directly to molecules via polarization effects. This preferential absorption of radiation by materials with high dielectric loss can cause rapid and selective heating of precursor molecules in solution; leading to the formation of metastable polymorphs with unexpected properties that are not achievable by traditional thermal heating. For example, we showed that microwave fields can introduce disorder in the oxygen sub-lattice of ceramic oxides like ZrO2, leading to coatings that modulate the diffusion of cations like Li+ and enhance the cycle-life of lithium-ion batteries. We further leveraged results from these fundamental studies to advance technology related to additive manufacturing of ceramic oxides and carbides, realizing energy savings by lowering sintering temperatures and durations.
My lab has access to multiple 2–4 GHz (S-band), 4–8 GHz (C-band) microwave waveguide reactors for solid and liquid phase materials synthesis and processing as well as for large area ceramic sintering and densification including a system for feedthrough processing. We collaborate with AFRL for access to systems operating at terahertz frequencies/millimeter waves. |
| PA |
| Los Alamos National Laboratory | Yu Zhang | Scientist |
Federally Funded Research and Development Center (FFRDC)
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Other Energy Technologies
| Yu's research is at the intersection of chemical physics, quantum physics, and quantum chemistry. He focuses on solving critical challenges in sustainable energy and quantum technologies. He is particularly adept at developing and applying cutting-edge theoretical models and computational methods (including classical and quantum algorithms) to investigate materials' electronic, chemical, and optical properties.
In the particular field of quantum computation, Yu's work centers on developing efficient quantum algorithms to compute ground and excited states and quantum dynamics. These algorithms are crucial for understanding chemical reactions in areas such as catalysis, energy conversion, photosynthesis, combustion, and beyond. By applying state-of-the-art theoretical models and computational techniques, Yu aims to unravel materials' electronic, chemical, and optical properties for practical energy applications.
In addition to his expertise in quantum algorithms, Yu's research includes light-matter interactions, cavity quantum materials, non-adiabatic molecular dynamics, and the theory of open quantum systems. These play essential roles in simulating complex quantum behaviors relevant to energy processes. |
| NM |
| Los Alamos National Laboratory | Main POC: Rachel Atencio | Technical Project Manager |
Federally Funded Research and Development Center (FFRDC)
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Other Energy Technologies
| Los Alamos National Laboratory (LANL) is interested in quantum information science and technology (QIST) broadly, including sensing, materials, networking, and computing. Our R&D capabilities in materials science, chemical engineering, chemistry, nanotechnology, and catalysis are highly relevant to this ARPA-E call, as is our depth of knowledge in experiment, classical computing, and quantum. LANL’s newest high performance computing asset, Venado, is a powerful new platform for AI-based discovery science. These capabilities converge to enable rapid scientific progress on energy related problems.
The recent report “Potential Applications of Quantum Computing at Los Alamos National Laboratory,” (https://arxiv.org/abs/2406.06625) features investigative use cases that tackle fundamental questions in quantum magnetic materials, high-temperature superconductivity, and nuclear astrophysics simulations. The report demonstrates LANL’s rigorous approach to assessing and selecting energy-related problems to tackle with quantum computing.
LANL performs fundamental algorithmic research for quantum computing, is advancing integrated development environments, and is integrating quantum machine learning. LANL’s materials science and chemistry strengths are applied in projects on magnetic quantum materials, understanding quantum defects in materials, and integrating data science with quantum chemistry for stimuli controlled reactive chemistry. The LANL branch of the National High Magnetic Field Laboratory (MagLab) is a powerful experimental capability leveraged to advance our understand next-generation quantum materials. This work ultimately enables “quantum engineering” and can be leveraged to develop next-generation devices for QIS applications.
LANL has expertise in the diverse fields of quantum optics, classical network engineering, cryptography, and electric power systems. We develop hard/software solutions to secure quantum communication nodes and have implemented quantum systems on the links between these nodes. LANL supports the DOE Advanced Grid Modeling effort, focusing on novel methods in mathematics and computation, as well as DOE’s emerging interest in applying QC technology to challenges in electric grid operations, planning, and resilience. LANL is a main partner in the Quantum Science Center and leads the thrust on algorithms and simulations, as well as co-design for topological materials. |
| NM |
| University of North Dakota | Ayush Asthana | Assistant Professor |
Academic
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Other Energy Technologies
| We work on developing new algorithms and methods for quantum computing applications in simulation of molecular ground and excited electronic states. In the past, we have developed state-of-the-art methods for using quantum computers to predict molecular excited states using near-term quantum computers with potential advantages compared to classical computers, along with making fundamental advances in pulse level variational algorithms for near-term quantum computers. |
| ND |
| Strangeworks | Idalia Friedson | Chief Strategy Officer |
Small Business
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Other Energy Technologies
| Description of Strangeworks technical capabilities:
The Strangeworks advanced compute platform provides users access to quantum gate-based, quantum annealing, quantum-inspired, classical computing resources and tools and applications that leverage these resources. The platform abstracts hardware-specific details, creating a standard interface that allows researchers to easily select their preferred hardware when configuring their jobs. Strangeworks business tools enable users to control their quantum resources, spending, usage, and teams.
Users primarily interact with the Strangeworks platform through a multi-tenant cloud web portal, client SDK, or product API:
Web portal: This unified platform is designed to manage user access and resource procurement across multiple computing vendors. Users can access hardware resources, application and algorithm solutions, and manage administrative functions, such as permissions, billing, and job management.
SDK: provides users access to computing resources via the Python programming language and third-party quantum programming frameworks.
Product API: Backend that enables application developers to leverage platform functionality directly. Additionally, the platform allows developers to deploy hosted applications and provide other users access to these applications. Due to the close integration with external compute resources, such applications can leverage the Strangeworks platform for hardware access, job scheduling and observability.
Furthermore, Strangeworks has expertise in classical HPC, quantum embedding, and quantum hardware across several modalities. |
| TX |
| Quantinuum | Anand Shah | Strategic Partnerships |
Large Business
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Other Energy Technologies
| Quantinuum builds the world's highest-performing and highest-fidelity quantum computers built on a Quantum Charge-Coupled Device (QCCD) trapped-ion architecture – which has enabled it to be an excellent platform for high accuracy quantum chemistry simulations as well as the leading and most flexible testbed for quantum error correction, enabling the exploration of novel high-rate, efficient codes with novel topologies. Please see below details of our current performance metrics and future projected metrics as per our public hardware roadmap which promises a universal, fully fault-tolerant quantum computer by 2030.
Hardware Specs:
System Model H1 (available today) Physical Qubits: 20 Two-Qubit Gate Error: 1E-3 Logical Qubits: 0 Logical Error Rate: N/A
System Model H2 (available today) Physical Qubits: 56 Two-Qubit Gate Error: 1E-3 Logical Qubits: 10+ Logical Error Rate: 1E-3
Helios System (coming in 2025) Physical Qubits: 96 Two-Qubit Gate Error: < 5E-4 Logical Qubits: ~50 Logical Error Rate: < 1E-4
Sol System (coming in 2027) Physical Qubits: 192 Two-Qubit Gate Error: < 1E-4 Logical Qubits: ~100 Logical Error Rate: 1E-5
Apollo System (coming in 2029) Physical Qubits: 1000s Two-Qubit Gate Error: 1E-4 Logical Qubits: 100+ Logical Error Rate: 1E-5 to 1E-10
With our target specifications for the Apollo System, and potential to achieve logical error rates of 1E-10 based on newly published QEC codes, we believe this system is a great target for industrial chemical problems for the energy industry.
Separately, we have engaged with many industrial and energy companies (BMW, Airbus, Equinor, Honeywell, TotalEnergies, JSR) on real-world chemistry use cases are charting a path towards fault-tolerant quantum chemistry simulations. Last year, we implemented a Bayesian Phase Estimation workflow on our H1 device using a [[k+2, k, 2]] Quantum Error Detection code. With Microsoft, we have demonstrated an end-to-end chemistry simulation workflow leveraging our logical qubits on System Model H2 and combining HPC, AI, and Quantum for the first time.
We welcome collaborators to make use of our hardware with each new generation of system coming online throughout the course of the program. We would also be happy to provide access to our InQuanto quantum chemistry platform which has show leading performance in active-space selection, circuit generation and compilation, and experimental results across hardware modalities. |
| CO |
| Womanium | Vardaan Sahgal | Quantum Software & Solutions head |
Non-Profit
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Other Energy Technologies
| The Womanium Foundation’s flagship Quantum Program [https://womanium.org/Quantum], trains over 4,500 individuals annually across 120+ countries, with 45% participation from women. Our training spans quantum fundamentals, advanced quantum computing, quantum sensing, materials, quantum communication, and AI, attracting early-career researchers as well as industry professionals.
One of Womanium's core initiatives, the Quantum Solutions Launchpad connects participants with various government entities, national labs and industry stakeholders, enabling collaborative problem-solving. Recent research project successes include quantum algorithms for solving partial differential equations (PDEs) in computational fluid dynamics (CFD), quantum logistics optimization, miniaturization of quantum sensors, photonic chip development for free-space QKD, energy grid time-series prediction using QML, and the discovery of new materials using hybrid Quantum+AI approaches.
Our interest is in providing diverse top quantum talent to applicants and prospective grantees. These scholars will be at a PhD or postdoctoral level, can work remotely in part-time or full-time roles and will have strong quantum and material science expertise and be able to work with the grantee team to deliver on successful milestones and outcomes. |
| DC |
| Lawrence Berkeley National Laboratory | Wibe de Jong | Senior Scientist |
Federally Funded Research and Development Center (FFRDC)
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Other Energy Technologies
| At Lawrence Berkeley National Laboratory (LBNL), we are forging solutions to harness quantum information science and technology for discoveries that will improve our lives, from new materials to secure communications. LBNL's research is driven by discovery science and solutions for clean energy, with a focus on bioenergy, biofuels, biomanufacturing, carbon management, photovoltaics and photelelectrics, next-generation catalysts, energy storage (for example batteries), hydrogen based energy, manufacturing decarbonization, electric grid, and others. The laboratory is interested in partnering with institutions to utilize quantum computing to further its missing in these clean energy research areas.
LBNL is pioneering work across the quantum research ecosystem – from theory to application – partnering with industry and academia to fabricate atom-by-atom and test quantum-based devices, develop software and algorithms, build a prototype computer and network, and apply these innovations for breakthroughs in physics and chemistry. LBNL has research and capabilities for the full quantum computing stack.
LBNL has demonstrated expertise in algorithm and application development for chemistry, condensed-matter physics, high-energy physics, linear algebra, machine learning, application domains that are relevant to the advancement of the Department of Energy’s science mission. They have developed novel tools to rapidly build quantum application simulations. The team has delivered early, and limited scale, demonstrations of simulations in chemical and materials science, and high-energy physics on superconducting trapped ion and neutral atom quantum computing platforms. In addition, the team has demonstrated early applications in numerical linear algebra, and machine learning. The team has expertise with setting up (scalable) workflows, experimental design, error mitigation, compilation, data post-processing for multiple commercial quantum platforms, i.e. IBM, Quantinuum, Rigetti, IonQ, and QuEra. |
| CA |
| Purdue University | Qiyu Liang | Assistant Professor of Physics and Astronomy |
Academic
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Other Energy Technologies
| I am a cold atom experimentalist specializing in Rydberg quantum optics. My expertise lies in leveraging Rydberg-Rydberg interactions and electromagnetically induced transparency (EIT) to achieve nonlinearity at the single-photon level. Currently, I operate a Rydberg EIT apparatus, aiming at fast, nondestructive detection of individual Rydberg atoms and deterministic distributed quantum computing. Additionally, I am developing a novel optical tweezer system based on a digital micromirror device (DMD) to study many-body physics resulting from Rydberg anti-blockade and nonlinear collective optical effects on subwavelength-spaced atomic arrays. |
| IN |
| NC State University | Sabre Kais | Goodnight Distinguished Chair in Quantum Computing |
Academic
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Other Energy Technologies
| Developing quantum computing algorithms and implementation for complex many-body systems. Quantum machine learning for structure and dynamics of quantum and topological materials. Open quantum dynamics and quantum phase transitions. |
| NC |
| RTX Technology Research Center | Amit Surana | Principal Technical Fellow |
Large Business
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Other Energy Technologies
| RTRC, the innovation hub of RTX, develops cutting-edge technology for aerospace and defense. For more than 90 years RTRC has operated as a multidisciplinary group of experts collaborating on groundbreaking innovations. RTRC expertise spans a wide range of disciplines including artificial intelligence, optimization and controls, quantum computing and sensing, electric and electromagnetic systems, systems engineering, cyber physical systems, aerodynamics and acoustics, combustion and propulsion technology, thermofluid sciences, chemical sciences, material science, mechanics and manufacturing and measurement sciences. RTRC partners with RTX’s businesses (Pratt and Whitney, Raytheon, and Collins Aerospace) to transform innovative research into practical applications in sustainable aviation, cybersecurity, energy conservation and advanced defense systems.
RTRC has state-of-the art modeling, simulation, high performance computing and lab-scale experimental and characterization capabilities in the areas of advanced/additive manufacturing and material science/chemistry domains with applications spanning new material development for harsh environments, electrochemical power and energy systems including fuel cells and batteries, technologies for reducing carbon emissions/capture, functional materials for high energy/power technology, corrosion science, and energetic fuels.
RTRC works at algorithm/application level in the quantum stack and is exploring novel ways to utilize quantum computing in aerospace and defense applications. The research focuses on development of novel quantum algorithms for NISQ and fault tolerant quantum devices for applications including simulation of physical systems/CFD, optimization, and machine learning.
RTRC is looking to team with partners with expertise in the areas of classical high-performance computing, quantum algorithms and software engineering/compilation/error correction, and/or are (full stack) quantum hardware providers. |
| CT |
| MIT Lincoln Laboratory | Kevin M. Obenland, Ph.D. | Senior Staff |
Federally Funded Research and Development Center (FFRDC)
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Other Energy Technologies
| Our team at Lincoln Laboratory has extensive experience in developing optimized quantum circuit implementations of application instances in quantum chemistry, electronic structure, and differential equations. Our pyLIQTR tool chain spans the entire quantum application workflow, from problem specification, to quantum implementation, circuit generation, and complete logical resource analysis. We are looking for opportunities to leverage our expertise, and tools, to develop application implementations optimized to run on quantum platforms that are being developed by industry in the next 3-5 years. We are interested in leading a team or providing support to other teams. |
| MA |
| Aalto University | Alexandru Paler | prof. dr. |
Academic
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Other Energy Technologies
| === Quantum Software: Compilers, Optimizers and Decoders
We are developing a realistic software stack that is already available and can be used for automating the search and co-design of quantum algorithms. Additionally, in order to answer novel research questions, new models, methods and (software) tools are currently being researched and implemented.
QEC and FT-protocols, on the one hand, and algorithms/circuits, on the other hand, influence each other through the architecture of the hardware. Co-design is a complex process and can be both theoretically and practically, investigated by analyzing the software stack that translates an algorithm to an executable circuit. In order to co-design algorithms with hundreds of qubits and millions of gates, one should start from the following research questions related to the execution of simpler protocols: a) how are injection protocols reflected in the decoding of correlated errors?; b) do logical qubits suffer from novel/unexpected types of errors, and if so, what is the effect of these errors on the structure of the fault-tolerance compilation primitives? c) what logical cycle times are to be expected based on the underlying architecture, and how much of an improvement is necessary for lowering the resource counts?
Our results were developed partially within projects (where the speaker is a PI) funded by the DARPA Quantum Benchmarking program, QuantERA and Google. |
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| Citrine Informatics | James Saal | Director-External Research Programs |
Small Business
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Other Energy Technologies
| Citrine Informatics develops software for materials-focused machine learning and artificial intelligence to accelerate materials development, manufacturing optimization, and commercialization. Citrine can provide AI/ML support for teams to develop new materials, improve manufacturing processes, develop robust property and performance models, and accelerate qualification of materials. Our core technology is sequential learning, using ML-derived uncertainties to guide design of experiments. We have extensive experience in metal alloy, ceramic, and composite materials development, including the construction of property databases, atomistic simulations, and data infrastructure development. We are interested in joining teams as a subcontractor co-PI.
Specific ideas for this FOA include the use of sequential learning to identify which quantum computing calculations are most likely to achieve materials design goal and/or offer the most value for building materials property databases. Additionally, the generation of machine learning-based corrections to lower fidelity simulations from quantum computing results would be extremely valuable. |
| CA |
| Texas Tech University | Lu WEI | Associate Professor |
Academic
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Other Energy Technologies
| Lu Wei's group at Department of Computer Science, Texas Tech University, focuses on theoretic studies of quantum entanglement and its applications to quantum algorithms. The research group has been supported by different quantum programs in DOE and NSF.
We are looking for team partners with expertise on quantum algorithms relevant to energy applications. |
| TX |
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