Quantum Research News, High Performance Computing

Quandela’s research earns two ‘Best Paper’ awards at IEEE Quantum Week

Avatar of Ivy Delaney
October 13, 2026 by Ivy Delaney

Quandela earned two “Best Paper” distinctions at IEEE Quantum Week 2026, signaling growing peer recognition for its research as the field shifts focus from individual qubit performance to system-level challenges. Held September 13 to 18 in Toronto, the conference saw discussions move beyond metrics like qubit counts to questions of quantum module connection and hardware-aware compilation.

A joint NVIDIA–Quandela white paper detailed a practical path for integrating photonic quantum processing units into existing AI and high-performance computing workflows, suggesting a move toward real-world application, the company says. The team contributed to discussions on scalable photonic architectures, photonic quantum machine learning and hardware-aware compilation.

Photonic Architectures Enable Modular Quantum Systems

Modular quantum systems increasingly depend on overcoming interconnection challenges, as highlighted at IEEE Quantum Week 2026, where discussions centered on linking smaller processing units rather than solely maximizing individual qubit counts. Quandela’s research into hybrid matter-photon systems addresses this need by combining stationary qubits for local processing with photons for carrying quantum information between modules, creating a potential bridge to distributed quantum architectures and communication networks.

The company’s panel participation, with Nicolas Maring, focused on how interconnection losses and component performance become critical system-level hurdles as these modular systems scale. A key finding presented by Quandela concerns the optimization of photonic variational circuits, where gradients can diminish, hindering training, according to the company. Researchers detailed this issue in a paper, demonstrating the need for software that accounts for underlying hardware limitations to achieve useful performance.

This focus on co-design, where algorithms are tailored to specific hardware, represents a shift from simply improving processor metrics to addressing the complexities of integrated quantum systems. “Useful performance depends on software that understands the hardware,” reflecting the growing recognition that careful system design is paramount for realizing the potential of quantum computing.

Hardware-Aware Compilation Boosts Photonic QPU Performance

Quandela’s compilation method improved the probability of detecting all photons needed for computation by as much as 33.2% compared to existing approaches, a result presented at IEEE Quantum Week 2026. This optimization avoids treating every optical component as identical, instead selecting subcircuit placement and photon routing based on measured component performance characteristics like loss and phase noise. The team’s work directly addresses a critical challenge in scaling photonic quantum systems, where interconnection losses can quickly degrade signal quality and computational fidelity.

The success of this hardware-aware compilation was recognized with one of two “Best Paper” distinctions earned by Quandela at the September 13 to 18 conference in Toronto. The second award acknowledged the team’s research into quantum machine learning, further highlighting their contributions to the evolving field.

QUOPS and CUDA-Q Advance Logical Computation Benchmarking

The shift in evaluating quantum processors, from physical qubit counts to reliable computation, is now being formalized through new benchmarking systems like the Quantum Universal Operations Performance System, or QUOPS. QUOPS is designed to accommodate diverse hardware, extending its utility from current physical qubits to future fault-tolerant logical qubits as they emerge. Complementing QUOPS, NVIDIA’s CUDA-Q Logical tackles the software side of this evolution, providing a method to compile and analyze fault-tolerant algorithms across varied error-correction codes.

This approach moves beyond generalized resource estimations, allowing researchers to pinpoint how specific elements, routing, correction, decoding and classical communication, impact the actual cost of execution. The development of QUOPS and CUDA-Q, alongside the joint NVIDIA-Quandela white paper detailing integration pathways, suggests a move toward practical application and a maturation of the field beyond basic processor performance., the firm reports.

More like this
Quantum Research NewsOff-resonant tones boost single-qubit gate accuracy
Quantum Research NewsTeam demonstrates 2D topological insulator film
Quantum Computing NewsJin-Sung Kim, Wistron, and Western Digital map quantum’s path to AI supercomputers
High Performance ComputingClassiq links quantum code to Israel’s new HPC-quantum center
Stay current

See today’s quantum computing news on Quantum Zeitgeist for the latest breakthroughs in qubits, hardware, algorithms, and industry deals.

Tags:
CUDA-Q high-performance computing IEEE NVIDIA quandela Quantum Computing
Avatar of Ivy Delaney

Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

Latest Posts by Ivy Delaney: