High Performance Computing, Quantum Software

Classiq to present quantum software at SC26 conference

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November 11, 2026 by Ivy Delaney

Classiq will demonstrate its quantum software platform at SC26, beginning November 15, with a focus on connecting high performance computing environments to quantum hardware. The company intends to showcase how its modality-agnostic software benefits hybrid quantum and HPC workloads at Booth #5442 during the Chicago conference. Classiq welcomes the opportunity to engage with innovators and leaders at SC26 and will host live presentations with customers and partners. Attendees can also join the AWS & Classiq Quantummania Reception on Wednesday, November 18, from 4:00-6:00PM at AWS Booth #4819.

Classiq’s SC26 Showcase: Application-Layer HPC and Quantum Synthesis

Classiq will demonstrate a software platform at SC26, targeting hybrid quantum and high-performance computing environments, from November 15-20 within the McCormick Place Convention Center in Chicago. The company’s platform aims to connect HPC infrastructure to quantum hardware using a modality-agnostic approach, enabling applications to benefit from quantum processing power where applicable. Experts will be available at Booth #5442 to discuss the latest innovations and provide live demonstrations of the technology.

The focus at SC26 is on an application layer for quantum in HPC, allowing for software engineering agility with a build once, deploy anywhere strategy. Classiq intends to show how its software benefits hybrid workloads by complementing quantum performance with existing HPC systems. Live presentations featuring customers and partners will highlight real-world applications of this approach.

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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.

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