Title: Is this quantum computer useful? Let’s solve optimization with it! Evaluating the performance of quantum processing units at large width and depth
Speaker: David Bernal Neira, Purdue University
Date: 3 April 2026
Time: 13:30-14:30
Place: EA409
Abstract: Quantum computers have surpassed classical simulation limits, but noise remains a key barrier to practical use. Benchmarking quantum processors is challenging because existing methods primarily measure gate fidelities or run shallow circuits, offering limited insight into algorithmic performance. We present a scalable benchmarking protocol for evaluating the performance of quantum computers by solving combinatorial optimization problems. We use the linear-ramp quantum approximate optimization algorithm (LR-QAOA), a deterministic variant of QAOA, to track a processor’s ability to preserve coherent signals as circuit depth increases. Using LR-QAOA, we benchmarked 29 quantum processors from six vendors on problems with up to 156 qubits and 10,000 layers, resulting in circuits with over a million two-qubit gates. Our results establish LR-QAOA as a unified, cross-platform benchmark that captures algorithmic performance at scale, providing a practical tool for tracking progress in quantum hardware. The results of this work are openly available in https://qpu-benchmarking.streamlit.app/ and https://arxiv.org/abs/2502.06471
Bio: David E. Bernal Neira is an Assistant Professor in the Davidson School of Chemical Engineering at Purdue University. His research centers on mathematical optimization, artificial intelligence, and computational methods for solving scientific and engineering problems, with applications in process systems, energy, and chemical engineering. His core expertise is in nonlinear discrete optimization, encompassing theory, algorithms, and software. He also leads research in quantum computing, with emphasis on quantum algorithms for optimization, computational chemistry, and machine learning. He has co-authored peer-reviewed publications, developed open-source tools, and delivered invited talks across academia, government, and industry. He has taught several courses, including one he co-designed on optimization, quantum computing, and machine learning. He collaborates broadly with researchers in academia, national laboratories, government agencies, and industry. At Purdue, he leads the SECQUOIA lab (Systems Engineering via Classical and QUantum Optimization for Industrial Applications).