IonQ to Present Nine Peer-Reviewed Papers and Take Part in Seven Events at 2026 IEEE Quantum Week

IonQ’s nine peer-reviewed papers and four Best Paper awards at QCE26 highlight externally validated progress in real-world quantum applications.

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• IonQ research spans quantum machine learning, computational fluid dynamics, protein folding, and quantum error-correction – across nine papers accepted for peer review
• Company research leaders will deliver a keynote address, lead a tutorial, and contribute to workshops and panels throughout the QCE26 program
• Work conducted with Synopsys, Einride, Kipu Quantum, Oak Ridge National Laboratory, qBraid, QuantumBasel, Quantum Signals, and the University of Tennessee.

COLLEGE PARK, Md.--(BUSINESS WIRE)-- IonQ (NYSE: IONQ), the world’s leading full-stack quantum platform and foundry, today announced it will take part in the 2026 IEEE International Conference on Quantum Computing and Engineering (QCE26) meeting. IEEE is the premier international flagship conference for quantum computing and engineering. IonQ will present nine peer-reviewed research papers and participate in six additional events throughout this week's IEEE Quantum program at the Metro Toronto Convention Centre in Toronto. In addition, IonQ’s quantum experts and researchers will participate in a keynote address, a tutorial, technical workshops, poster presentations, and panels.

IEEE, publisher of one of the most respected and cited journals for innovation, accepted IonQ’s papers. These publications span a wide range of high-impact research areas, from clinical data imputation and protein folding to industrial fluid dynamics and freight logistics. This body of work reflects IonQ’s leadership in advancing real-world quantum applications and impactful quantum research.

Four of IonQ's accepted papers were recognized with QCE26 Best Paper Awards, a rare distinction at the field's flagship peer-reviewed conference. Best Paper Awards at IEEE Quantum Week reflect the highest marks from independent peer reviewers across academia, industry, and government labs. Winning across multiple tracks in a single year underscores the breadth of IonQ's applications research and its focus on results that hold up under rigorous external scrutiny.

IonQ Peer-Reviewed Papers at QCE26

Protein Folding on a 64 Qubit Trapped-Ion Hardware via Counterdiabatic Quantum Optimization [won a Best Paper award]
Authors: Alejandro Gomez Cadavid, Pavle Nikačević, Pranav Chandarana, Sebastian Romero, Enrique Solano, Narendra Hegade, Miguel Angel Lopez-Ruiz, Claudio Girotto, Hanna Linn, Hakan Doga, Evgeny Epifanovsky, Panagiotis Kl. Barkoutsos, Ananth Kaushik, Martin Roetteler
In collaboration with Kipu Quantum, the research scaled quantum protein-folding optimization to 61-qubit instances on IonQ Tempo. The results reached classical reference energies in four of six sequences through a hybrid quantum-classical workflow.
Session: QECS::629::968::939 – Sept. 14, 10:00–11:30 a.m., Room 802A/B

Quantum Parity Representations: Learnable Basis Discovery, Encoders, and Shadow Deployment
Authors: Sang Hyub Kim, Oliver Knitter, Jonathan Mei, Claudio Girotto, Masako Yamada, Martin Roetteler, Chi Chen
The research used quantum resources during training to discover high-order parity structure. The accuracy lifted by up to 41.7 points while keeping inference fully classical in the strongest deployment mode.
Session: QML::1025::299::723 – Sept. 15, 10:00–11:30 a.m., Room 705

End-to-End Performance of Quantum-Accelerated Large-Scale Linear Algebra Workflows [won a Best Paper award]
Authors: Daiwei Zhu, Miguel Angel Lopez-Ruiz, François-Henry Rouet, Claudio Girotto, Willie Aboumrad, Robert Lucas, Ananth Kaushik, Martin Roetteler
In collaboration with Synopsys, the research demonstrated quantum-accelerated graph partitioning. This improved end-to-end finite-element simulation time by up to 14.6% across industrial models with meshes up to 35 million elements.
Session: QECS::412::308::814 – Sept. 15, 1:00–2:30 p.m., Room 713A

Hybrid Quantum-Classical Optimization Workflows for the Shipment Selection Problem
Authors: Miguel Angel Lopez-Ruiz, Daiwei Zhu, Jonas Hatzenbuehler, Shudian Zhao, Claudio Girotto, Willie Aboumrad, Jonas Alm, Julia Kompalla, Mena Issler, Ananth Kaushik, Martin Roetteler
In collaboration with Einride, the research found up to 12.1% more shipments with no meaningful cost increase on real-world logistics data. The data were scaled to instances of up to 130 qubits on IonQ Forte and Forte Enterprise.
Session: QECS::418::937 – Sept. 15, 3:00–4:30 p.m., Room 713A

Quantum Lattice Boltzmann Solutions for Transport Under 3D Spatially Varying Advection on Trapped-Ion Hardware
Authors: Sayonee Ray, Jezer Jojo, Jason Iaconis, Abeynaya Gnanasekaran, Apurva Tiwari, Martin Roetteler, Chris Hill, Jay Pathak
In collaboration with Synopsys, the findings present the first successful high-fidelity demonstration of the 3D advection-diffusion equation. This was seen without non-uniform velocities on quantum hardware
Session: QAPP::283::742::892 – Sept. 16, 3:00–4:30 p.m., Room 802A/B

Scalable On-Hardware Training of Quantum Neural Networks and Application to Clinical Data Imputation
Authors: Natansh Mathur, Panagiotis Barkoutsos, Masako Yamada, Iordanis Kerenidis, Martin Roetteler
In collaboration with Quantum Signals, researchers introduced a quantum neural-network method to reconstruct missing data from electronic health records. This training occurred directly on an IonQ Forte Enterprise system.
Session: QAPP::486::524::546 – Sept. 17, 10:00–11:30 a.m., Room 802A/B

Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Models [won a Best Paper award]
Authors: Oliver Knitter, Sang Hyub Kim, Maximilian Wurzer, Jonathan Mei, Claudio Girotto, Karen Horovitz, Chi Chen, Masako Yamada, Frederik Flöther, Martin Roetteler
In collaboration with QuantumBasel and the University of Basel, the findings showed up to 24% lower classification error than the best classical baseline. Additionally, a measurable energy-to-solution break-even was noted around 34 qubits.
Session: QAPP::486::524::546 – Sept. 17, 10:00–11:30 a.m., Room 802A/B

DQAOA-GPT: AI-Accelerated Distributed Quantum Optimization for Combinatorial Problems [won a Best Paper award]
Authors: Seongmin Kim, Abhinav Rijal, Yuri Alexeev, Nora Bauer, Martin Roetteler, Mina Yoon, George Siopsis, In-Saeng Suh
In collaboration with a leading chip manufacturer, Oak Ridge National Laboratory, and the University of Tennessee, Knoxville, applied AI-assisted methods to distributed quantum optimization for combinatorial problems.
Session: QGDD::265::1180::238 – Sept. 17, 1:00–2:30 p.m., Room 705

Mid-Circuit Measurements for Clifford Noise Reduction in Hamiltonian Simulations
Authors: James Brown, Jason Iaconis, Yuri Alexeev, Linta Joseph, Spencer Churchill, Kenneth Heitritter, William Aguilar-Calvo, Martin Roetteler, Martin Suchara
In collaboration with a leading chip manufacturer and qBraid, researchers used the mid-circuit measurement capability of IonQ Tempo to reduce errors in computational chemistry problems.
Session: QAPP::487::503::773 – Sept. 18, 3:00–4:30 p.m., Room 802A/B

Keynote, Tutorials, Workshops, and Panels

IonQ’s Lisa Lambert, Vice President, Global Strategy & Managing Director, Canada; John Gamble, Vice President, Architecture; and Martin Roetteler, Vice President, Quantum Applications R&D will deliver the QCE26 keynote address, Engineering Quantum for Real-World Impact, on Sept. 15 at 8:00 a.m. ET.

Additional IonQ contributions across the program include:

• Quantum Computing in Practice for Advanced Biotherapeutics: From Decision Problems to Deployable Workflows — a one-day workshop co-organized by IonQ’s Evgeny Epifanovsky with CCRM and Moderna. They will convene advanced-therapy practitioners and the quantum computing community to identify near- to medium-term, high-value optimization problems in biotherapeutics. Sept. 15, 10:00–11:30 a.m., Room 715A
• Bridging the Gap: Software, Compilation, and Hardware Interfaces for Quantum Error Correction — a panel featuring IonQ’s John Gamble alongside colleagues from AWS Quantum Technologies. Sept. 15, 1:00–2:30 p.m., Room 718A
• Distributed quantum architectures: more than the sum of their parts? – a panel exploring the benefits of quantum applications featuring IonQ’s Aharon Brodutch. Sept. 16, 1:00—2:30 p.m., Room 801A
• Quantum Software 2.6: Current Challenges and Headways in Quantum Software — a workshop session featuring IonQ’s Nate Stemen. Sept. 17, 10:00–11:30 a.m., Room 718B
• Linear Combination of Non-Unitaries: Theory, Computation and Applications — a tutorial led by IonQ’s Abeynaya Gnanasekaran with the U.S. Naval Research Laboratory and RTX Corporation. They will introduce a new framework for more efficient decomposition in quantum linear algebra applications. Sept. 18, 10:00–11:30 a.m.

For more information about IonQ’s participation at QCE26 and the company’s latest announcements, please visit www.ionq.com.

About IonQ

IonQ, Inc. [NYSE: IONQ] is the world’s leading quantum platform and foundry - delivering integrated quantum solutions across computing, networking, sensing, and security. IonQ’s newest generation of quantum computers, the Superion, is the latest in a line of cutting-edge systems. Earlier systems have helped customers and partners including Amazon Web Services, AstraZeneca, and NVIDIA achieve a 20x performance increase over previous quantum solutions and accelerate innovation in drug discovery, materials science, financial modeling, logistics, cybersecurity, and defense. In 2025, the company achieved 99.99% two-qubit gate fidelity, setting a world record in quantum computing performance.

Note to Investors Regarding Forward Looking Statements

This press release contains forward-looking statements. All statements contained in this press release other than statements of historical fact are forward-looking statements, including statements regarding IonQ’s expected participation in the 2026 IEEE International Conference on Quantum Computing and Engineering. In some cases, you can identify these statements by forward-looking words such as “pending,” “look forward,” “accelerate,” “anticipate,” “expect,” “suggest,” “plan,” “believe,” “intend,” “estimate,” “target,” “project,” “should,” “could,” “would,” “may,” “will,” “forecast,” “confident,” “position,” “become,” “on track,” “ensure,” “ongoing” and other similar expressions. These statements are only predictions based on our expectations and projections about future events as of the date of this press release and are subject to a number of risks, uncertainties and assumptions that may prove incorrect, any of which could cause actual results to differ materially from those expressed or implied by such statements, including, among others, those described under the heading “Risk Factors” in our Annual Report on Form 10-K for the year ended December 31, 2025 filed with the Securities and Exchange Commission, or SEC, and in our Quarterly Report on Form 10-Q for the quarter ended June 30, 2026 filed with the SEC. New risks emerge from time to time, and it is not possible for our management to predict all risks, nor can management assess the impact of all factors on our business or the extent to which any factor, or combination of factors, may cause actual results to differ materially from those contained in any forward-looking statement we make. Investors are cautioned not to place undue reliance on any such forward-looking statements, which speak only as of the date they are made. Except as otherwise required by law, we undertake no obligation to update any forward-looking statement, whether as a result of new information, future events or otherwise.

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IonQ Media Contacts:
Cheryl Krauss
cheryl.krauss@ionq.co

Tor Constantino
tor.constantino@ionq.co

IonQ Investor Contact:
investors@ionq.co

Source: IonQ

Key Terms

quantum machine learning technical

Quantum machine learning is the use of quantum computers or simulators to run algorithms that find patterns, make predictions, or learn from data, instead of using only ordinary computers. Think of it as trying a new, potentially faster type of engine for data tasks that may solve some problems more efficiently or in a different way. For investors, advances could lower costs, unlock new services, or create advantages for firms that successfully commercialize or apply the technology, affecting valuations and competitive positioning.

computational fluid dynamics technical

A computer-based method for creating virtual models of how liquids and gases move and interact with objects, like a digital wind tunnel that predicts airflow, water flow or blood flow around designs. Investors care because it lets companies test and improve products, cut costly physical prototypes, and spot problems earlier—which can speed development, reduce R&D costs and lower the risk that a product or design will fail in the real world.

quantum error-correction technical

A set of techniques used to detect and fix mistakes that naturally occur inside quantum computers, which are very sensitive to tiny disturbances. Think of it as a digital insurance policy and repair crew that keeps fragile quantum data usable over time. For investors, effective quantum error-correction is a key milestone because it determines whether quantum devices can perform reliable, large-scale tasks and thus whether companies in the field can deliver commercially valuable products.

mid-circuit measurement technical

A mid-circuit measurement is the act of reading the state of one or more qubits while a quantum computation is still running, without discarding the rest of the processed quantum information. It lets the machine use that measurement result immediately to change later operations, support error correction, or reuse qubits, similar to checking and adjusting a running engine rather than stopping it. For investors, the capability affects how powerful, efficient, and scalable a quantum processor can be and therefore influences hardware performance claims and development complexity.

qubits technical

Qubits are the basic units of information in quantum computing, similar to how traditional computers use bits. Unlike regular bits that are either 0 or 1, qubits can represent both at the same time, allowing quantum computers to process complex problems much faster. This potential for unprecedented speed and power could transform industries, making qubits a key focus for investors interested in cutting-edge technology.