Maintaining open source quantum software for better UX

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.

Great read for #researchers #faculty #quantumresearcher looking to enable seamless integrations of quantum and classical high-performance computing resources. Open-Source tools that IBM® and partners like Rensselaer Polytechnic Institute (RPI), STFC Hartree Centre, Cleveland Clinic, and more are developing can be of great help in #quantum #quantumcomputing research. Use #opensource quantum #plugin Slurm, most popular #HPC workload manager in the world for HPC workflows, to take care of resource management and job scheduling, using the “Slurm Plug-in Architecture for Node and job Kontrol” or spank architecture, giving full operational control to administrators while maintaining maximum flexibility for users. Use #spank plugins to submit jobs incorporating quantum compute resources alongside QRMI, the quantum resource management interface which functions as middleware layer that abstracts away the complexities of controlling the quantum resources of specific hardware backends, allowing the plugin to easily acquire or release hardware, run tasks, and monitor the state of quantum computational resources. For more details read the blog by Iskandar Sitdikov and Robert Davis. https://lnkd.in/gCSunXjr

🚀 Day 18 is LIVE: Scaling MCP Systems for High Concurrency & Low Latency! We’ve built secure, distributed, cached, async, resilient, and well-tested MCP systems—but what happens when you need to handle 10,000+ concurrent users with sub-500ms response times? Today’s article solves the ultimate scaling challenge for AI applications. Here’s the enterprise-grade scaling architecture I’m sharing to make your MCP apps handle massive load: ✅ Intelligent load balancing with session affinity for conversation continuity ✅ Multi-layer caching (L1/L2/L3) with semantic similarity matching ✅ Distributed state management with Redis and context compression ✅ Auto-scaling with predictive ML-based load forecasting ✅ Advanced backpressure management for traffic spikes ✅ JVM optimization and memory management for AI workloads 💡 Why does this matter? Most MCP applications work great with 10 users but crumble under 1,000. The difference isn’t just more servers—it’s intelligent architecture that scales efficiently while maintaining performance. 👉 Whether you’re building consumer AI apps or enterprise systems, proper scaling isn’t optional—it’s what separates proof-of-concepts from production-ready applications that can handle real-world demand. Real transformation: One client’s MCP system went from 50 requests/second (with frequent timeouts) to 2,000+ requests/second with P95 latency under 500ms—a 40x improvement in throughput! 📖 Read the full article: https://lnkd.in/g3N2t2aA 🎯 Quick question for the community: What’s your biggest scaling bottleneck right now? CPU, memory, database, or something else? Drop your current concurrent user targets in the comments—I’ll share specific recommendations for your situation! 🔗 Save this if you’re building scalable AI systems 💬 Share your scaling war stories—we all learn from each other’s challenges! #MCP #GenerativeAI #Java #Scaling #Performance #LoadBalancing #HighConcurrency #MediumSeries #WomenWhoCode #DeveloperJourney

One of my biggest learnings after 2 decades in tech learning and working with distributed systems across Google, Amazon, and many other great systems is that: ► If you can avoid having a distributed system, go for it and let me explain why… 1. Distributed ≠ Scalable by default Everyone assumes distributing something will make it scale. But often, the bottleneck just moves. You now have multiple services, maybe in multiple regions, but one cache miss, one lock contention, or one overloaded queue can bring everything down. A well-optimized monolith can outperform a badly designed distributed system any day. 2. The fallacies of networked systems are real Latency is not zero. Bandwidth is not infinite. The network is not reliable. Once you go distributed, every interaction becomes a conversation over the network. Timeouts, retries, backpressure, duplicate messages, now you have to care about all of it. 3. Debugging becomes detective work In a monolith, you get a stack trace. In a distributed system, you get a partial trace, if you’re lucky. Logs scattered across services, missing spans, unknown retry logic, now you're piecing together puzzles, not fixing bugs. 4. You now have to solve consensus Clock skew. Leader election. Paxos/Raft. Things that don’t exist in simple systems now become foundational requirements. And unless you're building a database or running a control plane, you probably didn’t sign up for that level of complexity. 5. Eventually consistent = eventually confusing Building on CAP? Choosing availability over consistency? Great, now you have stale reads, replays, race conditions, and maybe some weird bugs that show up once a month on prod at 2AM. Designing for consistency without making the system unusable is one of the hardest engineering challenges. So what do you do? → Start simple. If you don’t need 99.999% uptime or global failover, don’t overengineer. Use a single-node system. Write a well-tested monolith. Add async where needed. Keep your state local. → Earn your way into distributed systems. Only break it apart when: – Your data won’t fit on one node – Your team/org is big enough to own the complexity Or your business needs it At scale, distributed systems are a beautiful thing. But unless you need it, don’t start there. Simplicity scales longer than you think. And complexity never comes free.

𝗜𝘁’𝘀 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝗹𝘆 𝗲𝘅𝗰𝗶𝘁𝗶𝗻𝗴—𝗮𝗻𝗱 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹—𝘁𝗼 𝘀𝘆𝗺𝗯𝗼𝗹𝗶𝗰𝗮𝗹𝗹𝘆 𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁 𝗹𝗮𝗿𝗴𝗲 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. Being able to precisely write down and manipulate complex spin, fermionic, bosonic, or mixed-system Hamiltonians unlocks new possibilities for quantum research. It’s not just cool; it’s a critical step toward building reliable, scalable quantum solutions. This capability is also key to unlocking quantum advantage. Many promising early wins in quantum computing, especially for problems involving 50+ spin sites, will depend on teams working with consistent, well-defined Hamiltonians across labs, codebases, and hardware. That’s where struqture comes in. Our open-source Rust/Python library provides a shared, portable format for defining and exchanging quantum operators and Hamiltonians, helping teams move from ideas to benchmarks to pilots more efficiently. While algorithms are still evolving, struqture ensures you’re not slowed down by mismatched models, letting you focus on refining those algorithms and running them on simulators or real hardware. 𝗪𝗵𝘆 𝘁𝗲𝗮𝗺𝘀 𝘂𝘀𝗲 𝘀𝘁𝗿𝘂𝗾𝘁𝘂𝗿𝗲: 🔸 Unified model format: Define your Hamiltonian once and use it seamlessly across teams, tools, simulators, and hardware. 🔸 Fewer mistakes: struqture validates your models, preventing errors from “almost the same” Hamiltonians. 🔸 Simulator-ready: Use struqture’s output as direct input for popular quantum simulators or HQS Spectrum Tools or HQS Quantum Solver. 🔸 Scales effortlessly: Transition from simple Ising models to complex, real-world NMR systems without reworking your pipeline. 🔸 Easy to adopt: Install via pip or Rust crate, and contribute to our Apache-2.0 open-source project on GitHub. In our short video, we explain struqture’s role for quantum projects in under a minute. If you’re looking for a reliable way to define, share, and validate quantum models—or to feed them into simulators—struqture is the place to start. Follow us to learn more about struqture. More videos will follow. We’d love your input: What operator types (spin, fermion, boson, or mixed) are you working with? What challenges do you face in keeping classical and quantum workflows aligned? Check out the GitHub repo and docs linked below. Feedback and contributions are warmly welcomed! #OpenSource #QuantumComputing #Software #Hamiltonians #SpinModels

To achieve an industrial relevant quantum advantage over powerful classical computers we need to run quantum algorithms on more than 50 qubits and to this end it is important to store the inputs to these algorithms symbolically. That is what we created struqture for.

Anthropic's post-mortem is a critical analysis of the challenges in productionizing large-scale models. The Triton JIT compiler bug causing cancellation in log-sum-exp is particularly telling. It signals that our compiler toolchains, often built on MLIR dialects, lack the robust formal verification needed for low-precision formats like FP8 (E4M3/E5M2). Ensuring gradient fidelity post-quantization isn't just a software problem; it necessitates rethinking ALU microarchitectures and using SAT/SMT solvers to provably guarantee numerical stability in fused kernels. The tokenizer and canary issues highlight a failure in managing IID assumptions. The subtle padding difference created a covariate shift between training and inference, a classic data-centric failure mode. Furthermore, canarying stochastic models with high Kolmogorov complexity using simple A/B tests is becomes pointless. It fails to detect latent issues like representational collapse. True observability requires more sophisticated techniques, such as monitoring the Mahalanobis distance in the embedding space to detect semantic drift in real-time. These incidents prove that a siloed approach is obsolete. We need a new discipline of full-stack AI engineering, where optimizations are co-designed from the gate-level of the silicon and its on-chip network (NoC) topology all the way up to the distributed serving stack. https://lnkd.in/gmh22yRV

Here’s your quick roundup of the top Kubernetes and platform engineering stories making waves this week: Azure Kubernetes Service on VMware Retirement (2025-09-16) ⚠️ Azure Kubernetes Service on VMware will be retired on March 16, 2026. Users are advised to replace it with Azure Kubernetes Service on Azure Local. https://lnkd.in/ga8cUdAY Azure Container Storage v2.0.0 GA (2025-09-16) Azure Container Storage v2.0.0 is now generally available, featuring NVMe performance boosts, open-source availability, and no service fees. https://lnkd.in/gzVfZMUc Grafana 12 Released at GrafanaCON 2025 (2025-09-16) Grafana 12 has been released, with key announcements around observability as code, OpenTelemetry investments, UI/UX changes with Drilldown, and new AI capabilities. A notable feature is SQL expressions within dashboards, allowing data transformation and manipulation without ETL pipelines. https://lnkd.in/gh_zVMeA NVIDIA Enhances GPU Support for Containers and Kubernetes (2025-09-14) NVIDIA continues to invest in making GPUs more accessible for AI workloads on Kubernetes. This includes improved integration with container runtimes and a dedicated container registry for deep learning frameworks, simplifying the deployment and scaling of AI/ML applications. https://lnkd.in/gBi4bmXe Kubernetes Security Disaster Due to Maintainer Burnout (2025-09-11) A concerning report highlights how maintainer burnout is contributing to a Kubernetes security disaster. This emphasizes the critical need for sustainable open-source project maintenance and community involvement to prevent security vulnerabilities. https://lnkd.in/gae8nu4q Under-the-Radar Tool KubeDiagrams (Last update: Sep 2, 2025) This open-source tool generates Kubernetes architecture diagrams from manifest files, Kustomization files, Helm charts, helmfiles, and even actual cluster states. It supports most Kubernetes built-in and custom resources, customizable clustering, and various output formats, making it invaluable for visualizing complex Kubernetes environments. https://lnkd.in/g3b-6xzv What are your thoughts on the increasing focus on AI/ML integration within Kubernetes platforms? Are you seeing these trends impact your infrastructure strategies?

🚀 Amazon Braket now supports local device emulation for verbatim circuits. This lets developers test their quantum circuits against the most recent calibration data from real quantum hardware - catching compatibility issues and understanding noise effects - before submitting their work to a QPU. In the blog’s example, a verbatim circuit is run against a noise model of Rigetti Computing’s Ankaa-3 device, showing that the local emulator’s noisy simulation (based on calibration data) matches hardware results more closely than a noiseless simulator, making it a practical tool for developing noise-aware algorithms. This will help shorten development cycles and allow users to build more noise-aware algorithms. 👍 Great blog by Mao LIN Ammar Ali Cody Wang Altanali N. https://lnkd.in/gr78Xbsc

Concurrency ≠ Parallelism. And mistaking them could tank your system’s performance. Let’s clear it up once and for all 👇 - Concurrency It’s about managing multiple tasks, not necessarily doing them at the same time. Your system switches between tasks fast enough to feel responsive. But as the task count grows? Performance suffers because tasks are still waiting for their turn. - Parallelism Now we’re talking about simultaneous execution of multiple tasks running at the same time on different processors. Great for CPU-heavy workloads. But there's a catch: Without careful coordination, parallelism alone won’t scale either. So what’s the real secret to building high-performance systems? - Combine both. Concurrency for responsiveness. Parallelism for raw throughput. Here’s how that actually works in the real world: 1. Synchronization Techniques Coordinate task access to shared resources: • Mutex: Only one thread at a time. • Semaphore: Limit how many threads access a resource concurrently. 2. Lock-Free Mechanisms (e.g., in Go) Avoid blocking altogether using goroutines and channels: • Goroutines: Lightweight, scalable concurrent functions. • Channels: Let goroutines talk to each other safely - no shared memory needed. 3. Architectural Design Your system design must support both models: • Load balancers use thread pools or non-blocking I/O to avoid bottlenecks. • Event-driven or multithreaded servers efficiently handle thousands of requests. follow Sandeep Bonagiri for more insights