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memQ Explores Path to Quantum Scale-Out

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July 1, 2026 by Ivy Delaney

Shifting the focus from maximizing qubits on a single chip, memQ will host the webinar “Quantum Scale-Out: The Power of Networked Quantum Computing” on July 21, from 1:00 to 1:30 PM ET, potentially marking a turning point for the field. As quantum systems approach practical applications, the challenge is no longer solely about qubit count but about effectively connecting enough qubits, even if distributed across diverse systems and vendors. Sean Sullivan, memQ’s Founder and CTO, and Skip Sanzeri, Strategic Advisor to memQ, will lead the discussion on building and using processing units, memory, and optical links. This approach aligns with investment in interconnects and compilers demonstrated by DARPA’s HARQ initiative, suggesting a move toward modular, heterogeneous quantum systems as a solution to the limitations of monolithic scaling.

Networked Quantum Computing for Scalable Systems

Rather than solely concentrating on monolithic scaling, the discussion will center on interconnecting qubits distributed across diverse systems and modalities, addressing the challenge of achieving sufficient qubit numbers for practical applications. This move toward modularity is gaining traction with government investment; DARPA’s HARQ initiative is now specifically geared towards combining disparate qubit types via interconnects and compilers. This represents a departure from previous strategies focused exclusively on scaling a single qubit technology, acknowledging the potential of heterogeneous systems. The webinar will explore how distributed compilers are essential for managing quantum programs across this interconnected architecture, enabling more complex computations. The potential of networked quantum computing hinges on the seamless integration of various components, and memQ positions itself as a key architect of this emerging landscape, suggesting that this approach may circumvent the limitations of monolithic scaling. This strategy acknowledges that practical quantum computation may require a fabric of interconnected resources rather than a single, massive processor.

But as quantum systems move closer to useful workloads, a different question is becoming more important: how do we connect enough qubits to do meaningful work, even when those qubits are spread across chips, machines, modalities, or vendors?

The pursuit of scalable quantum computing is increasingly diverging from monolithic approaches, with government investment now mirroring a shift toward interconnected, modular systems. The initiative’s current direction suggests a recognition that achieving fault-tolerant, useful quantum computation will likely depend on integrating, rather than simply scaling, existing qubit modalities.

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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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