Quantum Software Platforms

As quantum computers enter the utility era, with users executing circuits on 100 or more qubits, the performance of quantum computing software begins to play a prominent role. With this in mind, starting in 2020 Qiskit began the move from a mainly Python-based package to one utilizing the Rust programming language. What began with creating a highly optimized graph library in Rust (https://lnkd.in/eUdwqiMU), has now culminated in most of the circuit creation, manipulation, and transpilation code being fully ported over in the upcoming Qiskit 1.3. The fruits of this labor are easy to verify, with Qiskit outperforming competing SDKs in terms of runtime by an order of magnitude or more, as measured by rigorous benchmarks (https://lnkd.in/e98wniXY). However, algorithmic improvements also play a critical role in Qiskit's continued success. The team recently released a paper highlighting 18-months of effort optimizing the routing of circuits to match the topology of a target quantum device. This new LightSABRE method (https://lnkd.in/eMgm3TMG) is 200x faster than previous implementations, and reduces the number of two-qubit gates by nearly 20% compared to the original SABRE algorithm. In addition, LightSABRE, supports complex quantum architectures, disjoint connectivity graphs, and classical flow-control. The work the team puts into optimizing and enhancing Qiskit is one of the primary reasons why nearly 70% of quantum developers select Qiskit as their go-to quantum computing SDK.

NVIDIA’s launch of "Ising" marks the introduction of the world’s first open-source #AI model family purpose-built for #quantum #computing workflows. The platform targets two of the most critical bottlenecks in quantum systems—processor calibration and real-time error correction—by embedding AI directly into quantum control loops. Released across developer ecosystems (GitHub, Hugging Face) and integrated with CUDA-Q, Ising positions AI as the #orchestration layer for hybrid quantum-classical computing. Early adoption by institutions such as Fermilab and Harvard University signals immediate traction in #research. Strategically, this launch reframes AI not just as an application layer, but as foundational infrastructure for scalable, fault-tolerant quantum systems. Ising is fundamentally differentiated by its dual-model architecture: a 35B-parameter vision-language model for automated quantum calibration and a #3D CNN-based decoder for real-time quantum error correction. This architecture replaces manual calibration workflows with agentic AI pipelines, achieving up to 2.5× faster and 3× more accurate decoding while requiring significantly less training #data. Technically, it integrates tightly with NVIDIA’s CUDA-Q stack and NVQLink interconnect, enabling low-latency coupling between GPUs and quantum processing units (QPUs). Unlike generative AI models, Ising operates as a physics-aware control system, optimized for noisy qubit environments and scalable to millions of qubits, effectively acting as an AI control plane for quantum hardware. The Ising launch materially reshapes the quantum ecosystem by positioning NVIDIA as the control-plane leader in quantum computing, despite not manufacturing quantum hardware. It accelerates commercialization timelines by addressing error correction—widely seen as the primary barrier to the development of useful quantum systems. Market response was immediate, with quantum stocks (IonQ, Rigetti Computing, D-Wave) surging on expectations of faster industry maturation. Strategically, Ising challenges incumbents by shifting value from hardware-centric differentiation to AI-driven orchestration, thereby reinforcing a hybrid architecture in which GPUs and QPUs co-evolve. This positions NVIDIA as a central enabler across competing quantum vendors, potentially standardizing its ecosystem as the de facto operating layer for quantum-AI #convergence. These architectures intensify system autonomy and complexity, requiring dynamic governance models and adaptive #cyber-#ethics to continuously monitor, audit, and recalibrate #risks across hybrid quantum-AI control planes. #strategy #governance #business #investments #technology #future #digital

Cloud Quantum Computing: Strategic Shift From Experiment to Enterprise Preparation Introduction Quantum computing is moving beyond research labs into cloud platforms, enabling enterprises to experiment without owning specialized hardware. This shift is reframing quantum technology as a strategic readiness investment rather than a distant scientific curiosity. Democratization Through Cloud Access Lowering Capital Barriers • Traditional quantum systems require extreme cooling, shielding, and multimillion-dollar infrastructure. • Cloud access allows pay-as-you-go experimentation. • Enterprises can validate use cases before committing to large-scale investment. Hybrid Reality • Current devices are Noisy Intermediate-Scale Quantum systems with limited qubits and high error rates. • Hybrid models combine classical preprocessing with quantum computation. • Cloud platforms integrate quantum workflows into existing enterprise systems. Competitive Provider Landscape Platform Approaches • IBM emphasizes hybrid enterprise integration and broad network access. • Amazon Braket offers hardware-agnostic access across multiple architectures. • Microsoft focuses on long-term qubit stability while enabling partner hardware access. • Vendors are building ecosystems of SDKs, programming tools, and developer communities. Emerging Enterprise Use Cases • Financial firms are testing quantum algorithms for pricing and portfolio optimization. • Pharmaceutical and materials companies are exploring molecular simulation. • Logistics operators are evaluating optimization gains in supply chains. • Organizations are preparing for post-quantum cybersecurity threats. Strategic Implications • Venture and government investment in quantum technologies is accelerating. • Talent shortages are driving education and training initiatives. • Timelines for fault-tolerant quantum systems remain uncertain. • Early engagement builds institutional knowledge and competitive positioning. Conclusion: Readiness Over Hype Cloud-based quantum computing allows companies to prepare today for tomorrow’s computational breakthroughs. While practical advantages remain limited, strategic experimentation positions organizations to capitalize when scalable, fault-tolerant systems emerge. The competitive edge may belong not to the first to deploy quantum at scale—but to those who build quantum literacy early. I share daily insights with tens of thousands of followers across defense, tech, and policy. If this topic resonates, I invite you to connect and continue the conversation. Keith King https://lnkd.in/gHPvUttw

I'm really happy with the rapid development of CUDA-Q QEC, our toolkit for quantum error correction. QEC is an incredibly rich and fast-moving field, and in CUDA-Q QEC we aim to provide a platform with a diverse set of accelerated decoders, AI infrastructure, tools to enable researchers to develop and test their own codes, decoders, and architectures, hopefully even better than our own! As we dig deeper into the problem of scalable QEC, the benefits of GPUs and AI have become much clearer. We started with research tools, for simulation and offline decoding, which is still an important capability. Now with the 0.5.0 release we also provide the infrastructure for real-time decoding, where syndrome processing occurs concurrently with quantum operations. This release also introduces GPU-accelerated algorithmic decoders like RelayBP, a promising approach developed in the past year that aims to overcome the convergence limitations of traditional belief propagation. For scenarios demanding maximum throughput, we have integrated a TensorRT-based inference engine that allows researchers to deploy custom AI decoders trained in frameworks like PyTorch and exported to ONNX directly into the quantum control loop. To address the complexities of continuous system operation, we added sliding window decoders that handle circuit-level noise across multiple rounds without assuming temporal periodicity. These tools are designed to be hardware-agnostic and scalable, supporting our partners across the ecosystem who are building the first generation of reliable logical qubits. Check out the full technical breakdown in our latest developer blog by Kevin Mato, Scott Thornton, Ph.D., Melody Ren, Ben Howe, and Tom L. https://lnkd.in/gvC__zRd

Most people hear "quantum computing" and think: not for me. Too theoretical. Too far away. Maybe someday. These past two weeks have been a fire hose of learning. I've gotten to see what different teams are building and some of it genuinely stopped me in my tracks. Some things are still on the horizon. But others are here, right now, and they're remarkable. The team behind the QDK (Quantum Development Kit) demoed their January release in a meeting, which also includes contributions from the Error Correction and Chemistry teams and maybe some others. Count me as impressed. It's fully open source and here's what's in it: A Chemistry extension that optimizes molecular modeling for near-term quantum hardware, reducing circuit complexity by orders of magnitude in some cases. If you work in pharma, materials science, or computational chemistry, this was built for you. An Error Correction toolkit with open source modules for designing and testing fault-tolerant quantum programs. If you're a researcher pushing the boundaries of reliable quantum systems, this was built for you. Full GitHub Copilot integration for AI-assisted quantum programming, from code generation to hardware submission. If you're a developer who knows Python but not quantum, this was built for you too. What I keep coming back to is this: the people who built these tools spent countless hours making something that works so simply that we might never fully appreciate how hard it was to get here. That's the kind of work that quietly moves an entire field forward. If you've been waiting for a sign that quantum is ready for curious people, here it is. https://lnkd.in/g4YrE9Xm #QuantumComputing #Python #OpenSource #QDK #Microsoft

Which quantum computing framework should you learn in 2025? The quantum programming landscape can feel crowded with options. Here’s a balanced comparison of key open-source frameworks: Qiskit (IBM) • Widely used in the quantum community - one study identified it as the most-widely adopted framework.  • Version 2.2 brings a 10-20% faster circuit transpilation on average.  • Suitable for: general quantum development (e.g., chemistry, finance) • Best for: developers wanting broad ecosystem support Cirq (Google) • Built for noisy intermediate-scale quantum (NISQ) devices; focuses on hardware-aware circuit design.  • Version 1.6.0 adds support for Python 3.11+ and introduces the “willow_pink” QVM.  • Suitable for: error-correction research, benchmarking hardware • Best for: researchers focused on near-term quantum devices PennyLane (Xanadu) • Designed for quantum machine learning & hybrid classical-quantum workflows; integrates with PyTorch, TensorFlow and JAX.  • Suitable for: quantum neural networks, hybrid algorithms • Best for: AI/ML researchers exploring quantum-classical workflows Ocean SDK (D-Wave) • Open-source suite focused on quantum annealing and hybrid optimisation workflows.  • Version updates show support added for Python 3.14.  • Suitable for: optimisation problems, logistics, supply-chain modelling • Best for: industry practitioners solving real-world optimisation tasks How to decide: ✓ Your background (industry vs research) ✓ Problem type (optimisation vs general computing) ✓ Hardware access / deployment preferences Which one are you using or planning to try? Drop a comment with your experience or questions about getting started. ♻ Repost to help others in your network. And follow me for more grounded posts like this.

In early 2022 I wrote about cloud-based quantum computing access and noted that Microsoft provided access to three providers. I took another look more recently and my how much has changed! In this post I cover their robust Azure Quantum platform, some important and exciting collaborations (including today's announcement with Atom Computing of their record-breaking 24 logical qubits), and their work to create topological qubits. It's a long post but expect it'll be worth the read (or you can skip to the TL;DR summary at the end). Mentions in the post: Microsoft Azure , Atom Computing, Quantinuum, Chetan Nayak, Lindsay Bayne, IonQ, Pasqal, Quantum Computing, Inc., 1QBit, Rigetti Computing, Toshiba #topological, #Q# https://lnkd.in/gDMer8AY

A Chinese company just gave away the heart of its quantum computing stack. No Western vendor offers anything comparable for free. On Feb 26, Origin Quantum released its quantum operating system, Origin Pilot, for free download. The coverage mostly framed it as Chinese SDK versus Western SDK -  QPanda versus Qiskit. That comparison misses what matters. I published two pieces on PostQuantum.com: → News summary: "China Releases World's First Freely Downloadable Quantum Operating System" https://lnkd.in/dtsa429J → Full analysis: "China's Quantum OS Play: Origin Pilot and the Battle for the Integration Layer" https://lnkd.in/djjTbhd7 The core argument, which I haven't seen any other analyst make yet: Origin Pilot isn't competing with Qiskit. Qiskit is a programming framework. Origin Pilot targets the systems-integration layer - and there is no downloadable Western equivalent at that layer. The Quantum Open Architecture movement has produced world-class modular components - QuantWare QPUs, Bluefors cryostats, Qblox control electronics, Q-CTRL firmware. But there is no freely available, downloadable software layer that ties them all together. China just offered a solution. Top-down, free, and available to everyone. For a university in Indonesia, Saudi Arabia, or Brazil assembling its first quantum computer from modular components, the choice is now: hire a systems integrator to build custom middleware at significant cost, or download Origin Pilot for free. The available solution beats the theoretically superior one every time. This is bigger than one company releasing an OS. It's another move in a pattern we've now seen repeatedly - DeepSeek with AI models, Qwen overtaking Llama on Hugging Face, and now Origin Pilot for quantum. China is systematically using open releases to reshape technology ecosystems from the outside in. While the West debates export controls and forms commissions to study how to maintain primacy, China ships free software and lets adoption do the rest. The stakes with a quantum OS are arguably higher than with AI models. An AI model is a trained artifact you can swap out. A quantum OS, if it becomes the default integration layer, shapes the hardware interfaces, calibration protocols, and programming abstractions that entire generations of quantum infrastructure are built upon. That's not easily replaced. Important caveats in the full analysis: this is not a breakthrough, performance claims are unaudited, and the individual user experience is more modest than headlines suggest. But the strategic signal is unmistakable. The Western QOA community should be asking urgently whether a freely available, open-source quantum integration layer is something it needs to build - before China's version becomes the default. #QuantumComputing #QuantumSecurity #QOA #QuantumSystemsIntegration #OriginQuantum #China #QuantumSovereignty

NVIDIA’s #quantum strategy isn’t about building a quantum computer, it’s about owning the layer that makes quantum computing usable. At NVIDIA GTC 2026, the company positioned itself as the platform provider for the quantum ecosystem, focusing on how quantum systems integrate with AI and high-performance computing rather than competing at the hardware level. The strategy is clear: • #NVQLink connects quantum processors into GPU-powered systems • #CUDAQ provides a familiar development environment for hybrid workflows • AI is used to accelerate error correction, calibration, and algorithm development This approach plays directly to NVIDIA’s strengths. Many of the organizations that will be early adopters of quantum (national labs, research institutions, HPC environments) are already deeply invested in NVIDIA platforms. Extending those environments to support quantum lowers the barrier to entry and speeds up experimentation. It also creates a challenge for quantum hardware vendors trying to build full-stack platforms. Developing leading hardware is already difficult. Building the software, tooling, and developer ecosystem around it is an entirely different scale of effort. The result is a potential shift in where value accrues in the quantum market, from the hardware itself to the infrastructure and workflows that make it usable. Today, I published an IDC Link featuring an overview of NVIDIA’s announcements at GTC 2026, with a more in-depth Market Note coming soon. The question now is which ecosystem players shape the quantum computing market of the future. https://lnkd.in/epYXy5Ve cc: Kristine Neufeld Sam Stanwyck Krysta Svore Ester De Nicolás Ashish Nadkarni Peter Rutten Dave Pearson Lorenzo Larini Rick Villars Matt Eastwood Brandon Hoff

𝗤𝗶𝘀𝗸𝗶𝘁 𝗶𝘀 𝘁𝗼 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝘄𝗵𝗮𝘁 𝗖𝗨𝗗𝗔 𝗶𝘀 𝘁𝗼 𝗚𝗣𝗨. Qiskit, IBM's open-source quantum computing framework, is becoming an indispensable tool in the quantum ecosystem. Much like how CUDA revolutionized GPU computing by giving developers the AI software, Qiskit simplifies and accelerates quantum development, making what was inaccessible (quantum resources) far more programmable and practical for real-world applications. This week, IBM announced advancements to its Heron quantum processor and Qiskit’s capabilities – hitting a landmark of running circuits with up to 5,000 two-qubit gate operations. The integration of Qiskit with classical computing environments enssure a quantum-centric computing future. Exactly like CUDA unlocked possibility for GPUs in AI. 𝗔𝘀 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗺𝗮𝘁𝘂𝗿𝗲𝘀, Qiskit’s advancements aren't just academic, they're enabling new research in chemistry, life sciences, and beyond by weaving quantum and classical computation into cohesive workflows. IBM’s partnerships, from Cleveland Clinic to RIKEN, illustrate how quantum algorithms are being applied to critical, complex problems. 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻: Will Qiskit become a standard in the field as CUDA is for AI?