Quantum Applications

ORCA Computing Integrates Quantum Processors With $1 Trillion Industry

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May 7, 2026 by Ivy Delaney

ORCA Computing is integrating its photonic quantum processors with a novel artificial intelligence platform developed by SiC Systems, with the goal of improving efficiency in the $1 trillion Engineering, Procurement, and Construction industry over the next decade. The collaboration marks the first integration of quantum computing into industrial agentic AI systems designed for real-world process design and operation, potentially accelerating and refining complex chemical and biomanufacturing processes. SiC Systems’ platform utilizes what it terms “agentic ‘hives’,” model-based, autonomous AI workflows that enhance modeling, optimization, and continuous operation of industrial systems. “When combined with SiC Suite’s multi-agent AI, ORCA’s hybrid quantum–classical approach enables a different way to model and optimize complex chemical and biological systems,” said Per Nyberg, Chief Commercial Officer at ORCA Computing.

ORCA & SiC Systems Hybrid Quantum-Classical Computing Partnership

The combination of quantum computing and artificial intelligence could reshape industrial design and operation, as demonstrated by a new partnership between ORCA Computing and SiC Systems. Traditional EPC projects are often slowed by extensive modeling and simulation loops, but this partnership aims to accelerate those timelines and enhance responsiveness during live operations. This hybrid quantum, classical high-performance computing framework leverages the strengths of both technologies to improve the modeling, optimization, and continuous operation of complex systems. SiC Systems has already demonstrated significant time savings in plant design, with their platform capable of reducing engineering time by over 20,000 hours in a typical project. Dr.

SiC Suite Delivers 20,000-Hour Savings in Plant Design Projects

The projected $1 trillion scale of the Engineering, Procurement, and Construction industry may see disruption through the integration of quantum computing and advanced artificial intelligence; a collaboration between SiC Systems and ORCA Computing aims to alter how complex plants are designed and operated. Traditional EPC projects are characterized by iterative modeling and simulations, processes that can extend project timelines and hinder adaptability during live operations, but this partnership offers a way to accelerate these workflows. ORCA Computing’s photonic quantum processors are being integrated with SiC Systems’ SiC Suite, enhancing modeling, optimization, and continuous operation within global manufacturing environments. This impact is already demonstrable; in typical new chemical or biological plant design projects, SiC Suite has demonstrated the capability to save over 20,000 hours of engineering time. These savings stem from automating repetitive tasks, improving physics-informed simulations, and facilitating intelligent agent-driven decision-making, ultimately reducing design cycles and improving process robustness. Dr.

This collaboration shows how optimization can become both autonomous and explainable. By integrating quantum-accelerated computing with our agentic AI platform, we are empowering engineering teams to accelerate the design of new chemical and biological plants- adding to already proven savings of over 20,000 hours in a typical project while delivering higher accuracy and resilience.

Dr. Christopher Savoie, Co-Founder and CEO of SiC Systems
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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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