AFRL/RITQ - Quantum Algorithms


Who We Are
The AFRL Quantum Algorithms group explores the design and application of quantum algorithms across research topics such as quantum optimization, algorithms, and quantum machine learning. The team also utilizes noisy, intermediate-scale quantum (NISQ) devices. 

Using quantum computing devices, AFRL aims to develop algorithms to maximize the number of missions performed given a finite set of qubit numbers and qubit types.

Credit: Max Cutugno

Research efforts in optimization

Our quantum random walks effort is yielding very exciting results. In a recent paper submitted for publication, the Quantum Algorithms group demonstrates their development of quantum circuit techniques along with some results run on IBM’s 32-qubit chip. Currently, we are refining and improving quantum walk circuit designs even further, pushing towards weighted graph walks for aiding in Markov and other weighted graph problems.

Another focus in optimization is scheduling problems. We explore ways to compare Adiabatic Systems (such as DWAVE) and Universal Machines (IBM’s gate model quantum computer) on constrained optimization problems. To find the cost and benefits of both models of computation, we collaborate with industry partners to study optimization algorithms for mission related applications.

Research efforts in quantum machine learning
The AFRL Quantum Algorithms group has two ongoing projects in quantum machine learning: Quantum Bayesian Networks (QBN) and Quantum Autoencoders (QAE). 

Using IBM’s quantum chips, we built a framework for constructing Quantum Bayesian Networks of arbitrary complexity, we streamlined the workflow to run the networks with different noise models, and we are developing a scheme to measure complexity in QBNs quantitatively. We are studying the relationship between QBN complexity and quantum chip noise on the output of QBNs.

Our quantum autoencoder project studies information loss in quantum data compression. We parameterize circuits using expressibility and entangling capability metrics to measure circuit performance in compressing quantum data and to compare our algorithms against theoretical lossless compression limits. 

We do research on AFRL's RI campus and in the Innovare Advancement Center.

CLICK HERE to visit our other quantum labs.

Dr. Paul Alsing, AFRL Team co-Lead - Principal Research Physicist
Laura Wessing, AFRL Team co-Lead - Research Mathematician
Saahil Patel, AFRL Research Computer Scientist
Max Cutugno, AFRL Research Computer Scientist
Renae Young, AFRL Mathematician

Dr. Daniel Koch, NRC Postdoc Contractor - Research Physicist



"Gate-Based Circuit Designs For Quantum Adder Inspired Quantum Random Walks on Superconducting Qubits" - D. Koch, M. Samodurov, A. Projansky, P.M. Alsing. arXiv:2012.10268 (2021) (Journal Pending)

"Demonstrating NISQ Era Challenges In Algorithm Design On IBM's 20 Qubit Quantum Computer," D. Koch, B. Martin, S. Patel, L. Wessing, and P.M. Alsing. AIP Advances 10, 095101 (2020). 
"Fundamentals in Quantum Algorithms: A Tutorial Series Using Qiskit Continued," D. Koch, S. Patel, L. Wessing, and P.M. Alsing. arXiv:2008.10647 (2020). 
"Simulating Quantum Algorithms using fidelity and coherence time as a principle models for error," D. Koch, A. Torrance, D. Kinghorn, S. Patel, L. Wessing, and P.M. Alsing. arXiv:1908.04229 (2020).
Quantum circuit optimization using quantum Karnaugh map,” J.-H. Bae, P. M. Alsing, D. Ahn and W. A. Miller, Sci. Rep. 10,  15651 (2020).  
Speed-up of Grover's search algorithm and closed timelike curves,” K.-H. Yee, J. H. Bang, P. M. Alsing, W. A. Miller and D. Ahn, Quantum Science and Technology 5, 045011 (2020)

Introduction to Coding Quantum Algorithms: A Tutorial Series Using Qiskit,” D. Koch, L. Wessing, and P.M. Alsing. arXiv:1903.04359 (2019).
"Scattering Quantum Random Walks on Square Grids and Randomly Generated Mazes,” D. Koch, Phys. Rev. A 99, 012330 – Published 18 January (2019)