Classiq links quantum code to Israel’s new HPC-quantum center

Quantum Machines opened the Israeli Quantum Computing Center (IQCC) at Tel Aviv University with support from the Israel Innovation Authority, and Classiq is now directly linking its quantum software to the center’s high-performance computing resources, the company says. Classiq provides a software package that connects to the IQCC’s HPC scheduler, creating an integrated workflow for hybrid quantum-classical computing. This allows researchers to develop and run quantum applications across different quantum processing units. “Classiq helps researchers and developers rapidly turn algorithmic ideas into runnable circuits across different backends,” accelerating experimentation with diverse quantum technologies.

Classiq Enables Unified Quantum Workflows at the IQCC

Classiq delivers three core components to the Israeli Quantum Computing Center, establishing a comprehensive software foundation for its operations. These include a quantum software development platform directly connected to IQCC’s quantum backends, a software package integrating quantum job submission’s high-performance computing scheduler. Management portals for tracking and operating quantum resource usage across the organization. This layered approach aims to streamline the entire quantum workflow, from initial code development to resource allocation and monitoring.

Classiq’s software package functions as a user-side stack for integrated HPC-QC, allowing researchers to submit quantum jobs directly to the IQCC’s HPC scheduler. This connection is critical, as it moves beyond simply providing access to quantum processing units and instead focuses on making those QPUs usable within existing classical computing environments.

The system allows for centralized resource management, enabling administrators to control credit allocation, track usage and monitor key performance indicators across users, projects, and quantum backends, according to Classiq. Classiq’s software separates the application model from hardware-specific implementation, which is important for the IQCC’s heterogeneous environment, the firm reports. When a user selects a different quantum backend, the synthesis process accounts for characteristics like connectivity and native gate sets, generating an implementation tailored to that specific target.

This capability allows researchers to experiment across multiple hardware technologies, compare implementations and adapt to new systems without rewriting application logic. “When a different backend is selected, the synthesis process can account for characteristics such as connectivity and native gate sets and generate an implementation suited to that target,” explains the company.

The User Portal within the Classiq Manager provides developers and researchers with a clear view of their activity and resources, enabling them to monitor job submissions, review execution status and track available hardware credits. This granular level of control is intended to facilitate efficient workload planning and management. The IQCC represents a model for future quantum computing environments, integrating multiple quantum technologies alongside classical HPC and GPU resources.

Classiq and Quantum Machines are actively implementing this model, connecting high-level quantum development, hardware-aware synthesis, HPC job orchestration, heterogeneous quantum hardware and centralized resource management into a single, integrated stack. This integration makes those resources readily usable as part of a broader computing infrastructure.

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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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