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QuAIL Team Collaborates with Rigetti Computing and Publishes in the Physical Review Applied Journal
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QuAIL Team Collaborates with Rigetti Computing and Publishes in the Physical Review Applied Journal

Members of the Quantum Artificial Intelligence Laboratory (QuAIL) team and Rigetti Computing collaborated through the DARPA ONISQ SAAM program to investigate how entanglement and other quantum properties may be practically verified on modern quantum processors upon execution of an algorithm of interest. Findings were published in the Physical Review Applied journal.

The Quantum Approximate Optimization Algorithm (QAOA), introduced by Farhi et al., 2014, is a widely studied variational quantum algorithm designed for combinatorial optimization problems. On the one hand, it is important to study when, if at all, this algorithm provides an advantage over the best available classical algorithms. However, it is equally important to benchmark the extent to which physical executions of such an algorithm maintain crucial quantum properties of the algorithm that may provide a quantum advantage. To this end, we identified entanglement witnesses that certify the presence of entanglement in the output state of the quantum processor upon executing a QAOA circuit. Our analysis showed that such entanglement witnesses can be measured with only a handful of measurements, avoiding exponential cost, and that they are also sufficient to verify the presence of coherence in the final state. Moreover, we showed that such witnesses are especially well tailored for detecting entanglement in QAOA. While several standard benchmarks exist that capture the performance of a physical quantum processor in the form of, for example, gate and readout fidelities, our work takes an important step in creating algorithmic benchmarks of quantum processors.

Paper reference: MS Alam, FA Wudarski, MJ Reagor, J Sud, S Grabbe, Z Wang, M Hodson, P Aaron Lott, Eleanor G Rieffel, Davide Venturelli, Practical Verification of Quantum Properties in Quantum-Approximate-Optimization Runs, Physical Review Applied 17 (2), 024026, 2022 .

BACKGROUND: QuAIL conducts theoretical and empirical analyses of quantum computing and annealing algorithms, and investigates their applications with challenging computational problems in areas such as Earth-science data understanding, air-traffic flow control, and autonomous decision-making.

NASA PROGRAM FUNDING: This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under agreement numbers HR00112090058 and IAA 8839.

TEAM: M. Sohaib Alam, Filip A. Wudarski, Matthew J. Reagor, James Sud, Shon Grabbe, Zhihui Wang, Mark Hodson, P. Aaron Lott, Eleanor G. Rieffel, and Davide Venturelli

POINT OF CONTACT: Eleanor Rieffel, eleanor.rieffel@nasa.gov

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