DARPA: Quantum Computing Applications with State of the Art Capabilities Request for Information (RFI)
DARPA invites participation from all those engaged in related research activities and appreciates responses from all capable and qualified sources including, but not limited to, universities, university-affiliated research centers (UARCs), Federally-Funded Research and Development Centers (FFRDCs), private or public companies and Government research laboratories.
Ale Lukaszew, Program Manager, DARPA-SNfirstname.lastname@example.org
4:00 PM (Eastern) on 08/10/2018
The Defense Advanced Research Projects Agency (DARPA) Defense Sciences Office (DSO) is seeking information on new capabilities that could be enabled by current and next generation quantum computers for understanding complex physical systems, improving artificial intelligence (AI) and machine learning (ML), and enhancing distributed sensing.
DARPA Defense Sciences Office (DSO) is seeking information on new capabilities that could be enabled by current and next generation quantum computers for understanding complex physical systems, improving artificial intelligence (AI) and machine learning (ML), and enhancing distributed sensing. DARPA seeks to challenge the community to address the fundamental limits of quantum computing and to identify where quantum computing can relevantly address hard science and technology problems.
RFI responses may address one or multiple of the following challenge areas:
- Challenge 1: Fundamental limits of quantum computing.
- Challenge 2: Hybrid approaches to machine learning. We are interested in approaches that dramatically improve the total time taken to construct a high-performing ML/DL solution by leveraging a hybrid quantum/classical computing approach. For example, a hybrid approach may incorporate a small scale quantum computer to efficiently implement specific subroutines that require limited resources in a ML/DL task that is being handled by a classical computer. The challenge here is to identify the best approaches for achieving significant speed up as compared to the capabilities of the best known algorithms that run solely on classical computers.
- Challenge 3: Interfacing quantum sensors with quantum computing resources.
- Challenge 4: QC inspired algorithms and processes that are applicable to classical computers.
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