Quantum computing is the utilization of newly emerging quantum technologies for the purpose of solving challenging computational problems. While many of these technologies are still in their infancy, their potential as computational platforms is widely recognized by mathematicians, physicists, and computer scientists alike. Quantum algorithms research here at AFRL is an intersection of these three fields, with a focus on understanding and developing new algorithms, testing them on near-term devices (IBM & IonQ), as well as simulating them for future devices.
Interested applicants should have strong familiarity with:
• Python coding as well as quantum circuit coding, such as Qiskit
• Linear Algebra as pertaining to discrete quantum systems
• Prominent quantum algorithms, such as Shor's, Grover's, QAOA, VQE, Phase Estimation, etc.
Quantum algorithms research here at AFRL has two ongoing focuses: Amplitude Amplification & Quantum Machine Learning, with recent publications provided below. AFRL is seeking postdocs with research interests including, but not limited to, the above topics and prominent algorithms, e.g. quantum neural networks, combinatorial optimization, logistics & supply chains, decision making, and problem complexity.
Recent Publications:
• "Gaussian Amplitude Amplification for Quantum Pathfinding" - D. Koch
• "Variational Amplitude Amplification for Solving QUBO Problems" - D. Koch
• "Information Loss and Run Time from Practical Application of Quantum Data Compression" - S. Patel
Quantum Computing; Quantum Algorithms; Quantum Circuit Design; Quantum Information Science; Quantum Computer Science