How to integrate quantum and classical computing resources using open-source tools

Today, I’d like to highlight some of our recent work developing software for quantum-centric supercomputing (QCSC) with our partners at Rensselaer Polytechnic Institute (RPI) (https://lnkd.in/eJYkcy6Q) —also detailed in this week’s IBM Quantum blog: https://lnkd.in/ezfTu3nW Last November, we shared our vision for hybrid computing architectures that seamlessly integrate quantum and classical HPC resources to run workloads beyond the capabilities of classical HPC alone. Since then, we’ve worked closely with RPI's Future of Computing Institute to put these architectures to the test with real-world users of the Institute’s AiMOS supercomputer and the on-prem IBM Quantum System One. This collaboration has yielded a set of open-source quantum plugins for Slurm workload manager (check out the Slurm plugins GitHub repo: https://lnkd.in/ecn_nhrU), and QRMI, the quantum resource management interface (QRMI GitHub repo: https://lnkd.in/eBa39cX2). You can learn more about these QCSC software tools in the blog linked above or in the overview paper on arXiv: https://lnkd.in/ePRrR-Tf The leaders, researchers, and students at RPI have made invaluable contributions to this work—helping us optimize UX, administer storage and data flow between quantum and classical resources, manage dependencies, and optimize system partitioning and access policies. This work will enable quantum researchers at RPI and their partner institutions to explore QCSC workflows for applications in Hamiltonian simulation, PDEs, and more. If you’re at Quantum World Congress (#QWC2025) this week, I encourage you to attend the quantum-centric supercomputing workshop RPI will be hosting with IBM quantum SWE Iskandar Sitdikov (https://lnkd.in/eX-Zm9V9) from 1-3 p.m. on Tuesday, as well as the fireside chat featuring RPI President, Dr. Martin Schmidt (https://lnkd.in/eSmG-Ra8) on Thursday at 8:35 a.m. And be sure to check the full QWC 2025 agenda (https://lnkd.in/e29Eddpi) to catch all of this year’s IBMer sessions, including the keynote presentation I’ll be delivering Thursday morning.

As with any new technology, early quantum systems introduce unique complexities such as specialized calibration, workload optimization, and hardware-specific programming. For data center and HPC providers, this creates a new challenge in integrating quantum computing into infrastructure in a way that is efficient, scalable, and usable by end users. The key is software abstraction. With the right infrastructure software, operators can automate calibration, suppress errors, and turn raw quantum processing units into dependable, usable resources. This enables providers to: → Perform system calibration and tune-up with push-button simplicity → Maintain consistent performance without specialist intervention → Follow a practical path to integrating quantum with less complexity and risk At Q-CTRL, our Boulder Opal and Fire Opal platforms deliver the abstraction layers needed to make quantum computing viable in production environments. Read the full article here:

⚛️ Architecting Distributed Quantum Computers: Design Insights from Resource Estimation 🧾 To enable practically useful quantum computing, we require hundreds to thousands of logical qubits (collections of physical qubits with error correction). Current monolithic device architectures have scaling limits beyond few tens of logical qubits. To scale up, we require architectures that orchestrate several monolithic devices into a distributed quantum computing system. Currently, resource estimation, which is crucial for determining hardware needs and bottlenecks, focuses exclusively on monolithic systems. Our work fills this gap and answers key architectural design questions about distributed systems, including the impact of distribution on application resource needs, the organization of qubits across nodes and the requirements of entanglement distillation (quantum network). To answer these questions, we develop a novel resource estimation framework that models the key components of the distributed execution stack. We analyse the performance of practical quantum algorithms on various hardware configurations, spanning different qubit speeds, entanglement generation rates and distillation protocols. We show that distributed architectures have practically feasible resource requirements; for a node size of 45K qubits, distributed systems need on average 1.4× higher number of physical qubits and 4× higher execution time compared to monolithic architectures, but with more favourable hardware implementation prospects. Our insights on entanglement generation rates, node sizes and architecture have the potential to inform system designs in the coming years. ℹ️ Filipov et al - Department of Computer Science and Technology, University of Cambridge, United Kingdom - 2025

🚀 Quantum + supercomputing are coming together in exciting new ways. IBM just shared how quantum-centric supercomputing (QCSC) is becoming real — with open-source tools, new middleware, and even the first working setup at RPI’s AiMOS supercomputer. It’s a significant step toward integrating hybrid quantum-classical workflows into everyday research and innovation. 👉 Check out the full blog here: https://lnkd.in/e8z_iTry #QuantumComputing #Supercomputing #HPC #IBMQuantum #QuantumCentric #HybridCloud #OpenSource #Innovation #Qiskit #FutureOfComputing

High-performance Quantum Data Center Network Architecture Enables Scalable Computing for Academic and Commercial Applications Researchers present a novel network architecture for quantum data centres, utilising an innovative three-layer design and advanced queue scheduling to maintain high performance and scalability when clustering smaller computers for large-scale quantum processing #quantum #quantumcomputing #technology https://lnkd.in/eJFN6PGx

⚛️ A Framework for Quantum Data Center Emulation Using Digital Quantum Computers 📜 As quantum computing hardware advances, the limitations of single-chip architectures—particularly in terms of small qubit count —have sparked growing interest in modular quantum computing systems and Quantum Data Centers (QDCs). These architectures interconnect multiple quantum processor units (QPUs) to overcome physical constraints and support complex quantum algorithms. However, the implementation of distributed quantum computing (DQC) faces significant technical challenges, especially in the execution of remote gates. More-over, no practical emulation tool currently exists to evaluate theoretical proposals of various DQC systems. In this work, we propose a framework that emulates a DQC system using a single quantum processor. We partition the physical qubit coupling map of an existing QPU into multiple logical QPUs, and introduce an experimentally grounded noise model based on quantum collision dynamics to quantify the interconnect- induced noise, representing fiber-connected QPUs. The framework is validated using IBM’s quantum hardware, demonstrating the successful execution of remote gates under noisy conditions. Furthermore, we implement distributed versions of Grover’s search and the Quantum Fourier Transform (QFT), showing that complex circuits can be executed within the proposed framework with reasonable fidelity across interconnected QPUs. The emulation result of Grover’s algorithm aligns with the real-world experimental implementations between two Ion-trapped QPUs interconnected by optical fiber, which demonstrate the feasibility and accuracy of our framework. Overall, this work provides a versatile emulation tool for investigating QDC behavior while accounting for interconnect-induced communication noise and offers a practical method for validating distributed quantum protocols without requiring specialized interconnect hardware. ℹ️ Elyasi et al - 2025

German Breakthrough: Hybrid Software Unites Quantum and Supercomputers Introduction A team of German researchers has unveiled Sys-Sage, a pioneering hybrid software tool designed to bridge the gap between quantum computers and traditional supercomputers. Developed by scientists at the Technical University of Munich (TUM) and the Leibniz Supercomputing Centre (LRZ), the system is currently in experimental testing. This innovation represents a critical step toward practical integration of quantum computing into high-performance computing (HPC) environments. Key Details • The Challenge • Quantum and classical supercomputers operate on fundamentally different architectures. • Seamless integration has remained a major barrier to unlocking quantum’s potential. • The Solution: Sys-Sage • Developed by a joint TUM–LRZ research team led by Professor Martin Schulz, expert in computer architecture and parallel systems. • Provides a hybrid interface that allows quantum processors and HPC systems to interact seamlessly. • Designed to reduce inefficiencies and enable smooth execution of hybrid workloads. • Quantum vs. Classical • Classical supercomputers: Process data using binary bits (0 or 1). • Quantum computers: Use quantum bits (“qubits”) that can exist in multiple states simultaneously, enabling vastly greater complexity. • Sys-Sage enables researchers to harness the best of both worlds—supercomputers for large-scale numerical tasks, quantum processors for specialized problem-solving. • Applications and Potential • Accelerating breakthroughs in drug discovery, material science, financial modeling, and climate simulations. • Serves as a model for how emerging quantum technologies can be embedded into existing HPC infrastructure. • Positions Germany as a leader in the global race to operationalize quantum computing. Why This Matters Quantum computing’s promise will only be realized if it can work alongside today’s HPC systems. Sys-Sage represents a breakthrough in making that coexistence possible, creating a practical pathway to hybrid quantum-classical workflows. This advance not only accelerates research and innovation but also strengthens Europe’s position in shaping the next era of computing. The work signals that the future of computation lies not in replacing existing systems, but in strategically merging them. I’ve had the privilege of reaching over 18 million views in the past year, sharing daily insights with a network of 27,000+ followers and 10,000+ professional contacts across defense, technology, and policy. If this topic resonates, I welcome you to connect and continue the conversation. Keith King https://lnkd.in/gHPvUttw

Evolutionary Circuit Optimization Reduces Quantum Communication Costs by 89% for Distributed Computing An evolutionary algorithm successfully optimises quantum circuits, reducing the number of communication steps required for execution by up to 19% and decreasing the total number of necessary operations by over 89% while maintaining high accuracy #quantum #quantumcomputing #technology https://lnkd.in/ebDtKDcW

⚛️ Interfacing Quantum Computing Systems with High-Performance Computing Systems: An overview 📜 The connection and eventual integration of High-Performance Computing (HPC) with Quantum Computing (QC) represents a transformative advancement in computational technology, promising significant enhancements in solving complex, previously intractable problems. This manuscript provides a comprehensive overview of the current state of HPC-QC interfacing, detailing architectural methodologies, software stack developments, middleware functionalities, and hardware integration strategies. It critically assesses existing hardware-level integration models, ranging from standalone and loosely-coupled architectures to tightly-integrated and on-node systems. The software ecosystem is analyzed, highlighting prominent frameworks such as Qiskit, PennyLane, CUDA-Q, and middleware solutions like Pilot-Quantum, essential for seamless hybrid computing environments. Furthermore, the manuscript discusses practical applications in optimization, machine learning, and many-body dynamics, where hybrid HPC-QC systems can offer substantial advantages. It also describes existing challenges, including hardware limitations (coherence, scalability, connectivity), software maturity, communication overhead, resource management complexities, and cost factors. Finally, future directions towards tighter hardware and software integration are discussed, emphasizing ongoing research developments and emerging trends that promise to expand the capabilities and accessibility of hybrid HPC-QC systems. ℹ️ Rallis et al - 2025

Oak Ridge National Laboratory (ORNL) has released a new study outlining a blueprint software architecture to bridge emerging quantum computers with the world’s fastest supercomputers, such as Frontier. Key Innovations Include: - Unified resource management, coordinating both quantum and classical compute resources. - Hardware‑agnostic Quantum Programming Interface (QPI), abstracting away vendor-specific complexities. - Quantum Platform Manager (QPM), enabling streamlined integration across diverse quantum hardware. - End‑to‑end toolchain for circuit optimization, scheduling, and execution—even in hybrid workflows   . This complements ORNL’s previous strategic framework published in Future Generation Computer Systems and arXiv, providing the first practical design for operationalizing quantum-HPC fusion