Resource Challenges in Scaling Quantum Models

Nu Quantum has released a paper this week which significantly accelerates the quantum computing timeline by showing a viable path to commercial quantum computing via 'scale-out' 🦾 👀 These results are a significant haircut to Jensen's 15-year prediction for *very useful* computers 👀 We explore a modular architecture of quantum processing units (QPUs) of intermediate size, networked via a photonic fabric made of qubit-photon interfaces and switches. Flexible entanglement topologies are made possible by the network, enabling the use of error correcting codes (Floquet codes) which require significantly lower physical-to-logical qubit ratios than the surface code. We demonstrate that this error-corrected distributed system is feasible to build, since it tolerates realistic network fidelities and doesn't need all-to-all connectivity. The sort of quantum network we are trailblazing at Nu Quantum. Finally, we demonstrate it's efficient - i.e. you don't need more total qubits that in a monolithic approach in order to introduce networking. This is really significant. The results are timely - with the Willow announcement and others, in 2024 the industry demonstrated for the first time that matter qubits can be high-quality enough for computing. So we now have the building blocks. The only remaining orders-of-magnitude challenge is scaling, from ~100 qubits to 10k-1Ms of qubits... -> Modular scaling via networking together near-term available QPUs shortens the time-to-impact of quantum computing and makes the timeline more predictable, since it moves the problem from an R&D one to a scalable manufacturing engineering & capital resource one (stamp-and-repeat of modules that we already know how to make). So proud of the Nu Quantum Quantum Error Correction team for this fantastic work! Link in comments 🙂

Quantum Scaling Recipe: ARQUIN Provides Framework for Simulating Distributed Quantum Computing Systems Key Insights: • Researchers from 14 institutions collaborated under the Co-design Center for Quantum Advantage (C2QA) to develop ARQUIN, a framework for simulating large-scale distributed quantum computers across different layers. • The ARQUIN framework was created to address the “challenge of scale”—one of the biggest hurdles in building practical, large-scale quantum computers. • The results of this research were published in the ACM Transactions on Quantum Computing, marking a significant step forward in quantum computing scalability research. The Multi-Node Quantum System Approach: • The research, led by Michael DeMarco from Brookhaven National Laboratory and MIT, draws inspiration from classical computing strategies that combine multiple computing nodes into a single unified framework. • In theory, distributing quantum computations across multiple interconnected nodes can enable the scaling of quantum computers beyond the physical constraints of single-chip architectures. • However, superconducting quantum systems face a unique challenge: qubits must remain at extremely low temperatures, typically achieved using dilution refrigerators. The Cryogenic Scaling Challenge: • Dilution refrigerators are currently limited in size and capacity, making it difficult to scale a quantum chip beyond certain physical dimensions. • The ARQUIN framework introduces a strategy to simulate and optimize distributed quantum systems, allowing quantum processors located in separate cryogenic environments to interact effectively. • This simulation framework models how quantum information flows between nodes, ensuring coherence and minimizing errors during inter-node communication. Implications of ARQUIN: • Scalability: ARQUIN offers a roadmap for scaling quantum systems by distributing computations across multiple quantum nodes while preserving quantum coherence. • Optimized Resource Allocation: The framework helps determine the optimal allocation of qubits and operations across multiple interconnected systems. • Improved Error Management: Distributed systems modeled by ARQUIN can better manage and mitigate errors, a critical requirement for fault-tolerant quantum computing. Future Outlook: • ARQUIN provides a simulation-based foundation for designing and testing large-scale distributed quantum systems before they are physically built. • This framework lays the groundwork for next-generation modular quantum architectures, where interconnected nodes collaborate seamlessly to solve complex problems. • Future research will likely focus on enhancing inter-node quantum communication protocols and refining the ARQUIN models to handle larger and more complex quantum systems.

⚛️ Quantum Resource Management in the NISQ Era: Challenges, Vision, and a Runtime Framework 🧾 Quantum computers represent a radical technological advancement in the way information is processed by using the principles of quantum mechanics to solve very complex problems that exceed the capabilities of classical systems. However, in the current NISQ era (Noisy Intermediate-Scale Quantum devices), the available hardware presents several limitations, such as a limited number of qubits, high error rates, and reduced coherence times. Efficient management of quantum resources, both physical (qubits, error rates, connectivity) and logical (quantum gates, algorithms, error correction), becomes particularly relevant in the design and deployment of quantum algorithms. In this work, we analyze the role of resources in the various uses of NISQ devices today, identifying their relevance and implications for software engineering focused on the use of quantum computers. We propose a vision for runtime-aware quantum software development, identifying key challenges to its realization, such as limited introspection capabilities and temporal constraints in current platforms. As a proof of concept, we introduce Qonscious, a prototype framework that enables conditional execution of quantum programs based on dynamic resource evaluation. With this contribution, we aim to strengthen the field of Quantum Resource Estimation (QRE) and move towards the development of scalable, reliable, and resource-aware quantum software. ℹ️ Lammers et al - 2025

Last week, Jensen Huang from NVIDIA made waves with his comments on the state of quantum computing. Love it or hate it, one thing’s for sure: quantum is now on a much bigger stage. And with this attention comes an opportunity—to attract more minds to tackle the core challenges that stand in our way. Let’s start at the very bottom of the quantum stack: the 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗨𝗻𝗶𝘁 (QPU). 𝗧𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Building a QPU isn’t just about qubits—it’s about creating qubits that work together reliably at scale. That means: - 𝗘𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗶𝗻𝗴 𝗗𝗲𝗳𝗲𝗰𝘁𝘀: Reducing fabrication imperfections that degrade coherence and qubit performance. - 𝗥𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗖𝗿𝗼𝘀𝘀𝘁𝗮𝗹𝗸: Minimising unwanted interactions between qubits as systems grow. - 𝗘𝗻𝘀𝘂𝗿𝗶𝗻𝗴 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝘃𝗶𝘁𝘆: Reliable qubit interaction architectures are critical for executing complex algorithms and maintaining fault tolerance. - 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝘁𝗼 𝗠𝗶𝗹𝗹𝗶𝗼𝗻𝘀: Achieving precision and uniformity in wafer-scale manufacturing to support large-scale systems. 𝗧𝗵𝗲 𝗣𝗮𝘁𝗵 𝗙𝗼𝗿𝘄𝗮𝗿𝗱 To move quantum hardware beyond its current limits, we need a systems-level reinvention: - 𝗡𝗲𝘄 𝗙𝗮𝗯𝗿𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀: Transitioning to deposition-first techniques, in-situ vacuum environments, and annealing techniques reduces defects, ensures atomically sharp interfaces, and enhances qubit coherence and scalability. - 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗦𝗲𝗺𝗶𝗰𝗼𝗻𝗱𝘂𝗰𝘁𝗼𝗿 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: Main foundry players like Applied Materials, GlobalFoundries, and imec enable access to advanced techniques such as fabrication on 300-mm wafers and cutting-edge lithography for scalable, high-quality quantum hardware production. - 𝗠𝗼𝗱𝘂𝗹𝗮𝗿 𝗦𝗰𝗮𝗹𝗶𝗻𝗴: Designing QPUs with for instance 20,000 qubits per wafer, then tiling them together, creates a roadmap to millions while maintaining connectivity. Quantum hardware is no longer just an academic exercise—it’s a systems engineering challenge of the highest order. But the beauty of this moment? With the spotlight brighter than ever, we have the chance to bring more problem-solvers into the field. What’s your take on the biggest challenge at the QPU level? Is it defects, connectivity, crosstalk or something else entirely? Let’s hear your thoughts! Qolab Hewlett Packard Enterprise IQM Quantum Computers Rigetti Computing Oxford Quantum Circuits (OQC) ConScience QuantWare