Bert de Jong

EDBT 2026 Demo / reviewers in the wild / expert

dblp:131/7393 · also Wibe A. de Jong, Wibe Albert de Jong · DBLP profile ↗

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12ranked-venue papers

0as first author

10since 2021 · last 2026

0000-0002-7114-8315ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 8 · 6 since 2021Artificial intelligence and machine learning · 2 · 2 since 2021Theory of computation · 2 · 2 since 2021

Year	Publication	Venue	Position
2026	Structure-Aware Quantum Circuit Partitioning via Reinforcement Learning for Efficient Re-Synthesis abstract The advancement of quantum computing into the utility scale requires compilation frameworks that can effectively manage the discrepancy between high-level algorithmic intent and the low-level physical constraints of contemporary hardware. Quantum circuit partitioning is a pivotal stage in this compilation pipeline, particularly when leveraging high-performance synthesis tools that are computationally bounded by the number of qubits. Existing partitioning approaches, such as ScanPartitioner [19] and QuickPartitioner [21], while effective, do not leverage structural patterns in circuits, limiting their ability to make globally informed local partitioning decisions. To address this gap, we propose a novel structural-aware quantum circuit partitioning method using a reinforcement learning (RL) framework that harnesses global circuit knowledge to guide local partitioning decisions, enabling more optimization opportunities at the sub-circuit level. Experimental results on benchmark circuits transpiled to satisfy IBM quantum hardware constraints show that our approach reduces the native two-qubit gate count compared to existing quantum circuit partitioners (ScanPartitioner and QuickPartitioner) with an average two-qubit gate reduction of 18.55% over baselines. This research establishes a scalable and robust methodology for partitioning quantum circuits, bridging the gap between exact and approximate synthesis in the Noisy Intermediate-Scale Quantum (NISQ) era and beyond. Mohammad Walid Charrwi, Christian Rasmussen, Ed Younis, Bert de Jong, Samah Mohamed Saeed	ACM Great Lakes Symposium on VLSI	4
2026	Stability and Reproducibility in Heuristic Unitary Synthesis for Quantum Circuits abstract Quantum circuit optimization can unlock the full potential of quantum computers for scalable and practical applications. In particular, quantum circuit re-synthesis methods enable significant reductions in gate count by applying approximate unitary synthesis locally at the subcircuit level. Despite these improvements, such optimization techniques rely on random seeds, which can lead to variability in performance across different runs. This inherent randomness raises important questions about the stability and reproducibility of approximate re-synthesis methods. Christian Rasmussen, Jason Perez, Ed Younis, Bert de Jong, Samah Saeed	ACM Great Lakes Symposium on VLSI	4
2025	Artificial intelligence driven laser parameter search: Inverse design of photonic surfaces using greedy surrogate-based optimization abstract Photonic surfaces designed with specific optical characteristics are becoming increasingly crucial for novel energy harvesting and storage systems. The design of these surfaces can be achieved by texturing materials using lasers. The optimal adjustment of laser fabrication parameters to achieve target surface optical properties is an open challenge. Thus, we develop a surrogate-based optimization approach. Our framework employs the Random Forest algorithm to model the forward relationship between the laser fabrication parameters and the resulting optical characteristics. During the optimization process, we use a greedy, prediction-based exploration strategy that iteratively selects batches of laser parameters to be used in experimentation by minimizing the predicted discrepancy between the surrogate model’s outputs and the user-defined target optical characteristics. This strategy allows for efficient identification of optimal fabrication parameters without the need to model the error landscape directly. We demonstrate the efficiency and effectiveness of our approach on two synthetic benchmarks and two specific experimental applications of photonic surface inverse design targets. By calculating the average performance of our algorithm compared to other state of the art optimization methods, we show that our algorithm performs, on average, twice as well across all benchmarks. Additionally, a warm starting inverse design technique for changed target optical characteristics enhances the performance of the introduced approach. Luka Grbcic, Minok Park, Juliane Mueller 0002, Vassilia Zorba, Bert de Jong	Eng. Appl. Artif. Intell.	5
2024	Effective Quantum Resource Optimization via Circuit Resizing in BQSKit abstract In the noisy intermediate-scale quantum era, mid-circuit measurement and reset operations facilitate novel circuit optimization strategies by reducing a circuit's qubit count in a method called resizing. This paper introduces two such algorithms. The first one leverages gate-dependency rules to reduce qubit count by 61.6% or 45.3% when optimizing depth as well. Based on numerical instantiation and synthesis, the second algorithm finds resizing opportunities in previously unresizable circuits via dependency rules and other state-of-the-art tools. This resizing algorithm, implemented in BQSKit, reduces qubit count by 20.7% on average for these previously impossible-to-resize circuits. Siyuan Niu, Akel Hashim, Costin Iancu, Bert de Jong, Ed Younis	DAC	4
2024	Quantum-centric supercomputing for materials science: A perspective on challenges and future directions Yuri Alexeev, Maximilian Amsler, Marco Antonio Barroca, Sanzio Bassini, Torey Battelle, Daan Camps, David Casanova, Young Jay Choi, Fred Chong, Charles Chung, Christopher Codella, Antonio D. Córcoles, James Cruise, Alberto Di Meglio, Ivan Duran, Thomas Eckl, Sophia E. Economou, Stephan J. Eidenbenz, Bruce Elmegreen, Clyde Fare, Ismael Faro, Cristina Sanz Fernández, Rodrigo Neumann Barros Ferreira, Keisuke Fuji, Bryce Fuller, Laura Gagliardi, Giulia Galli, Jennifer R. Glick, Isacco Gobbi, Pranav Gokhale, Salvador de la Puente Gonzalez, Johannes Greiner, William Gropp, Michele Grossi, Emanuel Gull, Burns Healy, Matthew R. Hermes, Benchen Huang, Travis S. Humble, Nobuyasu Ito, Artur F. Izmaylov, Ali Javadi-Abhari, Douglas M. Jennewein, Shantenu Jha, Bert de Jong, Petar Jurcevic, William M. Kirby, Stefan Kister, Masahiro Kitagawa, Joel Klassen, Katherine Klymko, Kwangwon Koh, Masaaki Kondo, Doga Murat Kürkçüoglu, Krzysztof Kurowski, Teodoro Laino, Ryan Landfield, Matthew L. Leininger, Vicente Leyton-Ortega, Ang Li 0006, Meifeng Lin, Junyu Liu, Nicolás Lorente, André Luckow, Simon Martiel, Francisco Martín-Fernández, Margaret Martonosi, Claire Marvinney, Arcesio Castañeda Medina, Dirk Merten, Antonio Mezzacapo, Kristel Michielsen, Abhishek Mitra, Tushar Mittal, Kyungsun Moon, Joel Moore, Sarah Mostame, Mario Motta, Young-Hye Na, Yunseong Nam, Prineha Narang, Yu-ya Ohnishi, Daniele Ottaviani, Matthew Otten, Scott Pakin, Vincent R. Pascuzzi, Edwin Pednault, Tomasz Piontek, Jed W. Pitera, Patrick Rall, Gokul Subramanian Ravi, Niall Robertson, Matteo A. C. Rossi, Piotr Rydlichowski, Hoon Ryu, Georgy Samsonidze, Mitsuhisa Sato, Nishant Saurabh, Kunal Sharma, Soyoung Shin, George Slessman, Mathias Steiner, Iskandar Sitdikov, In-Saeng Suh, Eric D. Switzer, Joel Thompson, Synge Todo, Minh C. Tran, Dimitar Trenev, Christian Trott, Huan-Hsin Tseng, Norm M. Tubman, Esin Tureci, David García Valiñas, Sofia Vallecorsa, Christopher Wever, Konrad W. Wojciechowski, Xiaodi Wu 0001, Shinjae Yoo, Nobuyuki Yoshioka, Victor Wen-zhe Yu, Seiji Yunoki, Sergiy Zhuk, Dmitry Zubarev	Future Gener. Comput. Syst.	47
2022	Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery Nicholas Choma, Andrew Deru Chen, Mikaela Cashman, Érica T. Prates, Verónica G. Vergara Larrea, Manesh Shah, Austin Clyde, Thomas S. Brettin, Bert de Jong, Martha S. Head, Rick L. Stevens, Peter Nugent, Daniel A. Jacobson, James B. Brown	ICLR	10
2022	Test Points for Online Monitoring of Quantum Circuits abstract Noisy Intermediate-Scale Quantum (NISQ) computers consisting of tens of inherently noisy quantum bits (qubits) suffer from reliability problems. Qubits and their gates are susceptible to various types of errors. Due to limited numbers of qubits and high error rates, quantum error correction cannot be applied. Physical constraints of quantum hardware including the error rates are used to guide the design and the layout of quantum circuits. The error rates determine the selection of qubits and their operations. The resulting circuit is executed on the quantum computer. This study explores the risk of unexpected changes in the error rates of NISQ computers post-calibration. We show that unexpected changes in error rates can alter the output state of a quantum circuit. To detect these changes, we propose the insertion of test points into the quantum circuit to enable online monitoring of the physical qubit behavior. We utilize classical, superposition, and uncompute test points. Furthermore, we use a gate error coverage metric to assess the quality of the tests. We verify the effectiveness of the proposed scheme on different IBM quantum computers (IBM Q), in addition to a noisy simulation that shows the scalability of the proposed approach. Nikita Acharya, Miroslav Urbánek, Bert de Jong, Samah Mohamed Saeed	ACM J. Emerg. Technol. Comput. Syst.	3
2022	Introduction to the Special Issue on Software Tools for Quantum Computing: Part 1 abstract Quantum computing is emerging as a remarkable technology that offers the possibility of achieving major scientific breakthroughs in many areas.By leveraging the unique features of quantum mechanics, quantum computers may be instrumental in advancing many areas, including science, energy, defense, medicine, and finance.This includes solving complex problems whose solution lies well beyond the capacity of contemporary and even future supercomputers that are based on conventional computing technologies.As a foundation for future generations of computing and information processing, quantum computing represents an exciting area for developing new ideas in computer science and computational engineering. Yuri Alexeev, Alex McCaskey, Bert de Jong	ACM Trans. Quantum Comput.	3
2022	ArQTiC: A Full-stack Software Package for Simulating Materials on Quantum Computers abstract ArQTiC is an open-source, full-stack software package built for the simulations of materials on quantum computers. It currently can simulate materials that can be modeled by any Hamiltonian derived from a generic, one-dimensional, time-dependent Heisenberg Hamiltonian. ArQTiC includes modules for generating quantum programs for real- and imaginary-time evolution, quantum circuit optimization, connection to various quantum backends via the cloud, and post-processing of quantum results. By enabling users to seamlessly design, execute, and analyze materials simulations on quantum computers, ArQTiC opens this field to a broader community of scientists from a wider range of scientific domains. Lindsay Bassman, Connor Powers, Bert de Jong	ACM Trans. Quantum Comput.	3
2021	Achieving performance portability in Gaussian basis set density functional theory on accelerator based architectures in NWChemEx David B. Williams-Young, Abhishek Bagusetty, Bert de Jong, Douglas Doerfler, Huub J. J. Van Dam, Álvaro Vázquez-Mayagoitia, Theresa L. Windus, Chao Yang 0001	Parallel Comput.	3
2017	Towards Highly scalable Ab Initio Molecular Dynamics (AIMD) Simulations on the Intel Knights Landing Manycore Processor abstract The Ab Initio Molecular Dynamics (AIMD) method allows scientists to treat the dynamics of molecular and condensed phase systems while retaining a first-principles-based description of their interactions. This extremely important method has tremendous computational requirements, because the electronic Schrodinger equation, approximated using Kohn-Sham Density Functional Theory (DFT), is solved at every time step. With the advent of manycore architectures, application developers have a significant amount of processing power within each compute node that can only be exploited through massive parallelism. A compute intensive application such as AIMD forms a good candidate to leverage this processing power. In this paper, we focus on adding thread level parallelism to the plane wave DFT methodology implemented in NWChem. Through a careful optimization of tall-skinny matrix products, which are at the heart of the Lagrange Multiplier and non-local pseudopotential kernels, as well as 3D FFTs, our OpenMP implementation delivers excellent strong scaling on the latest Intel Knights Landing (KNL) processor. We assess the efficiency of our Lagrange multipliers kernels by building a Roofline model of the platform, and verify that our implementation is close to the roofline for various problem sizes. Finally, we present strong scaling results on the complete AIMD simulation for a 64 water molecules test case, that scales up to all 68 cores of the Knights Landing processor. Mathias Jacquelin, Bert de Jong, Eric J. Bylaska	IPDPS	2
2009	Liquid water: obtaining the right answer for the right reasons abstract Water is ubiquitous on our planet and plays an essential role in several key chemical and biological processes. Accurate models for water are crucial in understanding, controlling and predicting the physical and chemical properties of complex aqueous systems. Over the last few years we have been developing a molecular-level based approach for a macroscopic model for water that is based on the explicit description of the underlying intermolecular interactions between molecules in water clusters. In the absence of detailed experimental data for small water clusters, highly-accurate theoretical results are required to validate and parameterize model potentials. As an example of the benchmarks needed for the development of accurate models for the interaction between water molecules, for the most stable structure of (H2O)20 we ran a coupled-cluster calculation on the ORNL's Jaguar petaflop computer that used over 100 TB of memory for a sustained performance of 487 TFLOP/s (double precision) on 96,000 processors, lasting for 2 hours. By this summer we will have studied multiple structures of both (H2O)20 and (H2O)24 and completed basis set and other convergence studies and anticipate the sustained performance rising close to 1 PFLOP/s. Edoardo Aprà, Alistair P. Rendell, Robert J. Harrison, Vinod Tipparaju, Bert de Jong, Sotiris S. Xantheas	SC	5