ROME, N.Y. – US Air Force researchers are asking industry to develop new quantum computer algorithm software for machine automation and machine learning in future command, control, communications and intelligence systems.
Officials at the Air Force Research Laboratory Information Directorate in Rome, NY, released a broad agency announcement (FA8750 AFRL RIK ROME NY 13441-4514 USA) on Thursday for the Quantum Information Services project.
Researchers want companies to submit white papers for research, design, development, concept testing, experimentation, integration, evaluation and delivery of technologies to support Air Force research in command and control.
Quantum computing tries to take advantage of quantum mechanics to make a huge leap in processor performance to solve particularly difficult problems.
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The project has five focus areas: quantum algorithm and computation; quantum information processing; memory node-based quantum networks; superconducting hybrid quantum platforms; and quantum information sciences.
Quantum algorithms and computations aim to develop quantum software algorithms for today’s computers, including noisy intermediate-scale quantum computers (NISQ) and quantum glow and adiabatic quantum computers.
Researchers are interested in quantum software algorithms for machine learning, neural networks, optimization, quantum walks, unstructured searches, decision and risk analysis, hybrid classical and quantum algorithms, efficient quantum gate and circuit decomposition and characterization, protocols and algorithms for quantum photonic integrated waveguide chips, superconducting qubits and platforms with trapped ions.
Quantum information processing involves entanglement distribution, quantum information processing, and local and distributed quantum computation. The project will focus on photon-based qubits, including quantum integrated photonic circuits, interactions between photon-based qubits, and other qubit technologies.
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Other interests include quantum repeaters, high-dimensional entanglement, efficient generation and measurement of quantum states, quantum channel characterization and discrimination, and measurement-based quantum computation.
Researchers focus on photon-based qubits, single and entangled photons on demand, quantum algorithms using cluster and graph states, ion-trapped qubits, superconducting qubits, quantum annealing or adiabatic quantum computation, and blind quantum computation.
Memory node-based quantum networks include quantum networking, quantum communication, and quantum information processing with an emphasis on ion trapped qubits, superconducting qubits, integrated circuit qubits, and entanglement distribution.
Areas of focus include multi-node network connections, quantum transduction across frequency bands, interfacing with heterogeneous qubit technologies, quantum information mapping between homogeneous and heterogeneous qubit technologies, entanglement distribution, entanglement verification and validation, ultra-high vacuum technology, dilution refrigerator technology, laser development and laser control, and interfaces.
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Superconducting hybrid quantum platforms focus on developing new quantum devices, new functionalities and exploring fundamental quantum network physics, with an emphasis on hybrid superconducting systems.
The focus includes cross-quantum technologies for coupling superconducting qubits and circuits with ion trap systems, integrated photonic circuits and electromechanical and optomechanical systems; quantum and classical microwave-optical interfaces; developing 3D integrated heterogeneous quantum architectures; chip-scale cooling; and quantum interfaces over large temperature gradients.
Quantum Information Sciences focuses on quantum communications, quantum networks, and quantum computing, with an emphasis on quantum bit technologies, quantum protocols for networking and computation, and assistive technologies.
Funding for this project will be approximately $20 million over the next two years.
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More information can be found online at https://sam.gov/opp/afdb23099e5b4275b211e72f6cef0861/view.