Opportunity Information: Apply for FOA AFRL AFOSR 2023 0010
The Materials Characterization and Quantum Performance: Correlation and Causation (MQC) FY23 opportunity is a research funding call from the Air Force Office of Scientific Research (AFOSR), run in collaboration with the Laboratory for Physical Sciences (LPS). It targets a very specific bottleneck in solid-state, gate-based quantum computing: even when qubit designs look similar on paper, real devices often vary widely in stability and performance from wafer to wafer, chip to chip, or cooldown to cooldown. MQC frames this as a materials problem first, meaning the program is less interested in purely architectural or control-level fixes and more focused on understanding how underlying materials and fabrication realities translate into the qubit behavior people measure in the lab.
The program scope centers on solid-state qubits made from semiconductor and superconductor material systems, specifically for gate-based quantum computing. It highlights two platform families as especially relevant: silicon (Si) gate-defined quantum dots and Josephson junction-based superconducting quantum circuits. In both cases, the qubit is not just an abstract two-level system; it is a device whose coherence, noise, and reproducibility are strongly shaped by interfaces, defects, disorder, contamination, film quality, junction properties, and other microstructural features that can vary subtly across fabrication runs. MQC is essentially asking teams to connect those microscopic and materials-level details to the macroscopic qubit metrics used to judge progress in the field.
The core technical goal is to identify clear correlations between qubit performance metrics and specific material properties. Importantly, the solicitation emphasizes material properties that can be measured precisely and at high throughput. That detail signals a practical intention: the Air Force is not only looking for one-off deep dives with exotic characterization on a small number of devices, but also for measurement approaches and material indicators that could realistically be used as screening, process monitoring, or feedback tools during development and manufacturing. In other words, they want material descriptors that can be gathered efficiently and consistently enough to guide iterative improvement and reduce variability.
A second goal, stated as equally important, is moving beyond correlation to causation by identifying the underlying physical mechanisms that produce those correlations. The program is looking for mechanistic explanations that hold up under scrutiny, not just statistical relationships. To support that, proposed work is expected to include detailed materials characterization measurements paired with modeling. The modeling component is described broadly and can include material microstructure modeling as well as device modeling, which implies multi-scale thinking: how microstructural features and material disorder affect electronic states, interfaces, noise sources, loss channels, and ultimately observable qubit behavior. The best-fit efforts for this program are likely those that can close the loop from fabrication or materials variation to measured qubit performance and then back to a physically grounded explanation that suggests how to improve stability and reproducibility.
From an eligibility and administrative standpoint, the opportunity is a discretionary research and development funding call (CFDA 12.800) under AFOSR, labeled as an "Other" funding instrument type in the source listing. It is open to "all responsible sources," explicitly including academia, nonprofit organizations, and industry, including for-profit entities. The funding opportunity number is FOA AFRL AFOSR 2023 0010. The posting was created on March 20, 2023, and the original closing date listed is June 20, 2023. The award ceiling shown is $6,750,000, which indicates the maximum potential amount for an award under this call as presented in the listing, though the number of expected awards is not specified in the provided source data.
In practical terms, MQC is designed for teams that can bridge quantum device performance testing with rigorous materials science. Strong proposals under this theme would typically bring together quantum engineering (to define and measure meaningful qubit performance metrics), advanced characterization (to quantify materials and interface properties with precision and throughput), and theory/modeling (to establish plausible causal mechanisms). The overall intent is to turn qubit performance variability into something measurable, explainable, and ultimately controllable by identifying which material factors matter most, how to measure them efficiently, and why they influence the quantum device outcomes.Apply for FOA AFRL AFOSR 2023 0010
- The Air Force Office of Scientific Research in the science and technology and other research and development sector is offering a public funding opportunity titled "Materials Characterization and Quantum Performance: Correlation and Causation (MQC) FY 23" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 12.800.
- This funding opportunity was created on 2023-03-20.
- Applicants must submit their applications by 2023-06-20. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- Each selected applicant is eligible to receive up to $6,750,000.00 in funding.
- Eligible applicants include: Others.
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Materials Characterization and Quantum Performance: Correlation and Causation (MQC) FY23 - FAQs
What is the MQC FY23 funding opportunity?
MQC FY23 (Materials Characterization and Quantum Performance: Correlation and Causation) is a research funding call from the Air Force Office of Scientific Research (AFOSR), run in collaboration with the Laboratory for Physical Sciences (LPS). The focus is on understanding why solid-state, gate-based quantum computing devices that look similar in design can still show large differences in stability and performance across wafers, chips, or cooldowns.
Who is sponsoring and running the program?
The opportunity is sponsored by AFOSR (Air Force Office of Scientific Research) and is run in collaboration with LPS (Laboratory for Physical Sciences).
What is the central problem MQC is trying to solve?
The program targets a specific bottleneck in solid-state, gate-based quantum computing: real devices often vary widely in stability and performance even when their designs appear similar. MQC frames this variability primarily as a materials and fabrication reality problem, rather than something that can be solved only through qubit architecture or control techniques.
What types of quantum computing platforms are in scope?
The scope centers on solid-state qubits made from semiconductor and superconductor material systems for gate-based quantum computing. Two platform families are highlighted as especially relevant: silicon (Si) gate-defined quantum dots and Josephson junction-based superconducting quantum circuits.
Is this opportunity focused on gate-based quantum computing specifically?
Yes. The program is explicitly aimed at solid-state qubits for gate-based quantum computing.
What is the main technical objective of MQC?
The core technical objective is to identify clear correlations between qubit performance metrics and specific material properties. The solicitation emphasizes material properties that can be measured precisely and at high throughput, signaling interest in practical, repeatable measurement approaches that can support screening and process monitoring.
Why does MQC emphasize high-throughput, precise measurements?
The emphasis suggests the program is not only interested in one-off deep characterization studies on a small number of devices. Instead, it is looking for material indicators and measurement approaches that can be gathered efficiently and consistently enough to guide iterative improvement, reduce variability, and potentially support development and manufacturing feedback loops.
Does the program require moving beyond correlation to causation?
Yes. A stated goal that is described as equally important is moving from correlation to causation by identifying the underlying physical mechanisms responsible for the observed correlations. The program is looking for mechanistic explanations, not just statistical relationships.
What kinds of research approaches fit MQC best?
Efforts that bridge quantum device testing with rigorous materials science are a strong fit. Based on the description, proposals are expected to combine detailed materials characterization with modeling, aiming to connect microstructural or fabrication variations to measurable qubit outcomes and then to a physically grounded explanation that informs improvement.
What role does modeling play in the expected work?
Modeling is expected to be paired with characterization. The modeling is described broadly and may include material microstructure modeling and device modeling, implying multi-scale work that links microstructural features and disorder to electronic states, interfaces, noise sources, loss channels, and the qubit behavior measured in the lab.
What kinds of material or device factors is MQC concerned with?
The opportunity highlights that coherence, noise, and reproducibility can be strongly shaped by interfaces, defects, disorder, contamination, film quality, junction properties, and other microstructural features that may vary subtly across fabrication runs.
Is MQC mainly interested in architectural or control-level solutions?
No. The program is framed as a materials problem first. While quantum engineering and performance testing are important to define and measure relevant qubit metrics, the emphasis is on understanding how materials and fabrication realities translate into measured qubit behavior.
What outcomes is MQC ultimately aiming for?
The intent is to make qubit performance variability measurable, explainable, and controllable by determining which material factors matter most, how to measure them efficiently, and why they influence quantum device performance and stability.
What is the funding opportunity number?
The funding opportunity number is FOA AFRL AFOSR 2023 0010.
What is the CFDA number listed for this opportunity?
The listing identifies the opportunity under CFDA 12.800.
What type of funding instrument is indicated?
The source listing labels the funding instrument type as "Other" and describes the call as a discretionary research and development funding opportunity.
Who is eligible to apply?
The opportunity is open to "all responsible sources," explicitly including academia, nonprofit organizations, and industry, including for-profit entities.
When was the opportunity posted, and what is the closing date shown?
The posting was created on March 20, 2023. The original closing date listed is June 20, 2023.
What is the maximum award amount shown in the listing?
The award ceiling shown is $6,750,000, indicating the maximum potential amount for an award under this call as presented in the listing.
How many awards are expected?
The number of expected awards is not specified in the provided source data.
What kinds of teams are a strong match for MQC?
Teams that can integrate quantum engineering (to define and measure qubit performance metrics), advanced materials characterization (to quantify materials and interface properties with precision and throughput), and theory/modeling (to establish causal mechanisms) are positioned as strong candidates under the stated intent of the program.
Does MQC focus on variability across wafers, chips, and cooldowns?
Yes. A central motivation is that performance can vary from wafer to wafer, chip to chip, or cooldown to cooldown, even for similar qubit designs.
What does MQC mean by linking microscopic properties to macroscopic metrics?
In this context, it means connecting measurable material and microstructural properties (such as interface quality or defect/disorder characteristics) to the qubit-level performance metrics used by researchers (such as stability, coherence-related behavior, noise characteristics, and reproducibility).
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