Q# Blog

We are pleased to recognize the winners and finalists of Microsoft’s latest round of the Quantum Pioneers Program. Below are the winners of the Hardware Track and the winners and finalists of the Software Track from the June 2026 competition.  

Alongside the University of Maryland (UMD) and our industry partners, Microsoft Quantum has opened a new quantum research center, which includes a hands-on quantum hardware makerspace. In conjunction, we are launching an annual research workshop series and funding quantum postdoctoral positions at UMD. Within this center, Microsoft Quantum is providing topological qubits built on our Majorana 2 quantum chip for independent testing and evaluation by the Defense Advanced Research Projects Agency (DARPA).   

Utility-scale quantum computing will require executing long, complex algorithms with end-to-end error rates far below what physical qubits can support directly. Error-corrected logical qubits are needed to achieve this goal. To make the progress on logical qubits measurable and comparable we introduce the definition of scalable logical qubits: logical qubits preserved for long computations by repeated quantum error correction, capable of fault-tolerant universal operations with low-latency real-time decoding and feedback, and replicable to the hundreds or thousands, as required by applications. We characterize these scalable logical qubits along four coupled dimensions: reliability, scale, capability, and performance, and discuss trade-offs among these dimensions.  

  by Sasha Schmidt, Principal Program ManagerWhat if some of the most important signals in your data are hidden in relationships that conventional analytics cannot reveal?   As a potential solution to that problem, we are announcing the private preview of the Microsoft Quantum Development Kit (QDK) for analytics, a new library of quantum algorithms and tools designed for advanced data analysis. Its first capabilities focus on Quantum Tensor Principal Component Analysis (Quantum Tensor PCA), an approach that can help researchers identify weak signals in noisy, high-dimensional datasets.   Across financial services, life sciences, infrastructure, security, professional networks, materials science, and other data-intensive fields, important patterns can be buried within complex data. Traditional analytical techniques can struggle to capture relationships that span many variables, leaving valuable insights undiscovered.   QDK for analytics gives researchers tools to investigate these challenging datasets, uncover hidden patterns and risks, and evaluate how future fault-tolerant quantum computers could open new possibilities for data analysis and decision making.    We are also announcing an early-stage collaboration with LSEG to evaluate the application of these methods to practical challenges and explore their potential in real-world financial applications.   Uncovering signals in higher-order data Many real-world datasets involve relationships that extend beyond simple pairwise interactions. For example, financial markets, biological systems, and supply chains can exhibit patterns that emerge only when multiple variables are considered together.   Quantum Tensor PCA helps analyze these higher-order relationships. By capturing interactions across multiple dimensions simultaneously, it can surface correlations and structure that traditional matrix-based techniques may overlook. This makes it a promising approach for investigating complex datasets where important signals are weak or obscured by noise.   For select problem classes, these algorithms may enable substantially faster analysis on future fault-tolerant quantum computers, creating new opportunities for data analysis.    

Microsoft is focused on building a utility-scale quantum computer, and that requires fault-tolerant logical qubits. The effort has been underway for more than a decade and approaches the problem holistically by designing hardware, software, and system architecture together as a single engineered stack.   Recently, Nature published Improved quantum processor logical error rates via correction and detection reporting that Microsoft-designed error-correction protocols delivered order-of-magnitude improvements in logical error rates on Quantinuum’s state-of-the-art trapped-ion processors—the product of a rewarding technical collaboration between the two teams. Those results were a first step toward a more ambitious goal: a fully productized logical-qubit machine called Magne, built and deployed in partnership with QuNorth in Copenhagen, using Atom Computing’s neutral-atom hardware and Microsoft’s full-stack quantum software. Magne is expected to be the first general-purpose commercial machine to make logical qubits available to users on demand.   In parallel, we continue to innovate on the error-correcting codes and system architectures needed to scale fault-tolerant computation, as well as the toolchain that makes that work possible. We are also announcing deq, the newest open-source package in the Microsoft Quantum Development Kit (QDK) for error correction—presented at QEC 2026 and now on GitHub under an MIT license.   The Nature paper: foundational error-correction results The Nature paper reports on two new quantum error-correction codes that Microsoft designed and optimized for an ion-trap architecture—a ⟦12, 2, 4⟧ carbon code (a substantially streamlined construction derived from Knill’s C₄/C₆ concatenation) and a ⟦16, 6, 4⟧ tesseract color code on the 4-D hypercube (Figure 1)—together with the fault-tolerant protocols that run them.   

We are pleased to announce the winners and finalists from April’s 2026 Quantum Pioneers Program – Hardware Track  

Quantum computing is entering a new phase. Across the industry, we are seeing meaningful progress on multiple fronts at once: higher-fidelity hardware, better control systems, more practical error correction, richer software tooling, and increasingly sophisticated hybrid workflows that combine quantum, AI, and high-performance computing (HPC). Useful quantum computing will not arrive from a single breakthrough in isolation; it will come from advances that work together across the full stack.   Logical qubits made from trapped ions Microsoft continues to make progress on the path to utility-scale quantum computing, as reflected in today’s publication in Nature on logical qubits. The paper, titled “Improved quantum processor logical error rates via correction and detection,” captures a set of results that marked a significant step forward for the field. By applying the Microsoft Quantum platform to Quantinuum’s trapped-ion hardware, the teams demonstrated logical error-rate improvements ranging from 11x to 800x over corresponding physical circuit baselines. In a Bell-state preparation, the logical circuit error rate was reduced from roughly 0.8% for the physical baseline to 0.001%, yielding the now well-known 800x improvement. The work also demonstrated repeated error correction with an error rate per round 51x lower than a physical baseline, along with a 22x improvement for preparing a 12-qubit cat state.  

Majorana 2 contains qubits that are 1,000x more reliable than those in our previous quantum processing unit. The new material stack, which swaps aluminum for lead, creates highly reliable topological qubits with operations on the microsecond scale and lifetimes with a mean of 20 seconds, occasionally exceeding one minute. This rapid progress, enabled by AI, has cut our timeline in half for delivering a scalable quantum computer—now anticipated by 2029.  

As quantum algorithms grow more sophisticated, teams need powerful tools that support practical, scalable quantum development. With this release, Microsoft’s Quantum Development Kit takes a major step forward. QDK v1.27.0 delivers stronger insight into program behavior, richer developer tooling, and first-class program composability across Q#, Python, OpenQASM, and leading frameworks such as Qiskit and Cirq. QDK v1.27.0 improves how developers build, inspect, and assemble quantum programs, making it easier to iterate on complex algorithms using reusable components.   AI‑assisted quantum development is now a first‑class experience in the QDK, elevating quantum development across the entire workflow. Deep integration with Visual Studio Code and GitHub Copilot brings intelligent assistance to every stage, from code generation and circuit reasoning to debugging, visualization, hardware submission, and resource estimation. Enhanced circuit diagrams, quantum state inspection, and a streamlined VS Code experience make iteration and exploration faster, more reliable, and more interactive across Python, Jupyter, and Q#.   Alongside these core advances, QDK for chemistry v1.1.0 expands what can be explored on today’s early fault‑tolerant hardware. In response to strong user demand, the release adds support for a broad family of model Hamiltonians, introduced through a systematic workflow that connects real molecular systems to reduced models such as the Fermi‑Hubbard Hamiltonian. Combined with stronger core primitives, deeper ecosystem integration, and a significantly faster classical pipeline, QDK for chemistry enables practical chemistry and materials science experimentation today while laying the groundwork for larger‑scale quantum capabilities ahead.   Together, these updates advance quantum computing from basic research toward practical, hardware-aware quantum engineering.   Download Microsoft’s QDK and QDK for chemistry   In the video below, hear directly from David and Stefan as they discuss the key updates in this release and what they unlock for practical quantum development.    

The field of quantum computing has advanced beyond the level of error-prone physical qubits to an era of reliable quantum computation with logical qubits. To continue making progress toward fault-tolerant quantum machines, advances in both quantum hardware and software are required. Microsoft is committed to providing the tools that developers and scientists need to enable applications on the quantum machines that exist today, as well as the fault-tolerant machines of the future.   New tools that simplify quantum development The Microsoft Quantum Development Kit (QDK) is an open-source developer toolkit for building quantum applications. It provides everything needed—from simulators to a modern programming experience—for developers to build and execute quantum code, both locally and on quantum hardware. The QDK is integrated with VS Code and GitHub Copilot to help you write, test, and execute quantum code. Microsoft’s QDK installs and runs on laptops, and GitHub Copilot makes it quick and easy to get started.    Learn more about Microsoft’s QDK   Domain libraries and workflows available with Microsoft’s QDK include those for error correction and chemistry, with other applications on the horizon. The included tools reduce the effort and expertise needed for researchers to design, test, and implement quantum chemistry applications or the error correction codes needed to create logical qubits. Both these tools and the QDK are open source and interoperable with the most popular quantum programming languages and frameworks, including Q#, OpenQASM, Qiskit, and Cirq. The quantum chemistry toolkit supports WSL and Docker support for reproducibility and portability across systems.