IonQ had four papers recognized with Best Paper Awards at the 2026 IEEE International Conference on Quantum Computing and Engineering, a rare achievement at the field’s flagship peer-reviewed event, the company says. The company will present a total of nine accepted research papers this week, ranging from advances in protein folding to optimizations for freight logistics, reflecting IonQ’s focus on real-world quantum applications.
These publications, accepted by IEEE, publisher of highly-cited journals, demonstrate IonQ’s breadth of research and its ability to withstand rigorous external scrutiny. “Winning across multiple tracks in a single year underscores the breadth of IonQ’s applications research,” said a company representative.
IonQ Wins Four Best Paper Awards at 2026 IEEE Quantum Week
The awards acknowledge the quality and impact of IonQ’s contributions to quantum computing research, judged by independent peer reviewers from academia, industry, and government laboratories. This level of recognition signals a growing maturity in IonQ’s research program, extending beyond foundational science into applied quantum solutions, according to the company. IonQ’s research portfolio, as presented at the conference, extends beyond theoretical advancements to address challenges in diverse fields.
The accepted papers detail work ranging from optimizing protein folding, a critical step in drug discovery, to improving the efficiency of freight logistics. This breadth of application is underscored by collaborations with organizations like Kipu Quantum, Synopsys, and Einride, demonstrating a commitment to translating quantum potential into tangible results. The company’s focus on real-world problems is reflected in the selection of topics, moving beyond abstract algorithms toward practical implementations.
The award-winning paper detailing quantum protein folding utilized 61 qubits on IonQ’s Tempo system, achieving results comparable to classical reference energies in four of six test sequences. Researchers, led by Alejandro Gomez Cadavid, employed a hybrid quantum-classical workflow to scale the optimization process, a technique increasingly favored for near-term quantum devices. Another winning paper focused on quantum-accelerated linear algebra, demonstrating a 14.6 percent improvement in finite-element simulation time using industrial models with meshes up to 35 million elements, a result achieved in collaboration with Synopsys.
IonQ’s research also addresses the challenges of scaling quantum machine learning. A paper on quantum parity representations showed accuracy improvements of up to 41.7 percent while maintaining classical inference, a key step toward practical quantum machine learning algorithms.
This work, led by Sang Hyub Kim, highlights the potential of quantum resources to enhance classical machine learning models. The team’s research into AI-assisted distributed quantum optimization, conducted with Oak Ridge National Laboratory and a leading chip manufacturer, explored methods to tackle complex combinatorial problems using a combination of quantum and classical computing, the company says. The company’s commitment to hardware development is also evident in the research presented.
A paper detailing mid-circuit measurements for Clifford noise reduction on IonQ’s Tempo system demonstrated a method for using the unique capabilities of IonQ’s hardware to improve the fidelity of quantum computations. IonQ is developing 256-qubit trapped-ion systems for delivery in 2026, with Horizon Quantum as the first customer for a 256-qubit machine. IonQ’s Lisa Lambert, John Gamble, and Martin Roetteler will deliver the QCE26 keynote address, “Engineering Quantum for Real-World Impact,” on September 15, further solidifying the company’s position as a leader in the field.
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