Automatic oracle generation techniques can find optimized quantum circuits for classical components in quantum algorithms. However, most implementations of oracle generation techniques require that the classical component is expressed in terms of a conventional logic representation such as logic networks, truth tables, or decision diagrams. We implemented LLVM passes that can automatically generate QIR functions representing classical Q# functions into QIR code implementing such functions quantumly. We are using state-of-the-art logic optimization and oracle generation techniques based on XOR-AND graphs for this purpose. This enables not only a more natural description of the quantum algorithm on a higher level of abstraction, but also enables technology-dependent or application-specific generation of the oracles.

An important branch in quantum computing involves accurate resource estimation to assess the cost of running a quantum algorithm on future quantum hardware. A comprehensive and self-contained workflow with the quantum program in its center allows programmers to build comprehensible and reproducible resource estimation projects. We show how to systematically create such workflows using the quantum programming language Q#. Our approach uses simulators for verification, debugging, and resource estimation, as well as rewrite steps for optimization.

Quantum computing harnesses quantum laws of nature to enable new types of algorithms, not feasible on traditional computers, that may lead to breakthroughs in crucial areas like materials science and chemistry. There is a rapidly growing demand for a quantum workforce educated in the basics of quantum computing, supported by government programs like National Quantum Initiative and regional coalitions and hubs that bring together research institutions, universities, and companies and startups in quantum computing industry. Quantum programming education plays an important role in training the quantum workforce. Learning materials and graded assignments that focus on quantum software engineering are crucial for a modern quantum computing curriculum. This demo shows a variety of programming exercises and assignments that can be used in an introductory course on quantum computing. The materials use Microsoft Quantum Development Kit, an open-source cross-platform toolkit for developing quantum applications using its domain-specific programming language Q#. They cover different aspects of quantum software development, from learning the basic concepts of quantum computing and expressing them in a quantum programming language to implementing basic quantum algorithms to using specialized tools for evaluating and validating quantum programs to running them on quantum hardware using Azure Quantum. In this demo I will also summarize my experience using these assignments in university courses.

In just hours or days, a quantum computer can solve complex problems that would otherwise take billions of years for today's computers to solve. This has massive implications for research and workforce development for healthcare, energy, and environmental systems. Microsoft is focused on bringing the only scalable quantum system to the broadest set of customers and partnering with universities to ensure that we have the people ready to staff the quantum economy and ecosystem of tomorrow.

Quantum computing harnesses quantum laws of nature to enable new types of algorithms, not efficiently possible on traditional computers, that may lead to breakthroughs in crucial areas like materials science and chemistry. There is rapidly growing demand for a quantum workforce educated in the basics of quantum computing. However, there have been very few offerings that help the learner to bridge the gap between the theoretical quantum computing knowledge and applying it to solving real-world problems.

Quantum computing harnesses quantum laws of nature to enable new types of algorithms, not efficiently possible on traditional computers, that may lead to breakthroughs in crucial areas like materials science and chemistry. There is rapidly growing demand for a quantum workforce educated in the basics of quantum computing, in particular in quantum programming. However, there are few offerings for non-specialists and little information on best practices for training computer science and engineering students. In this report we describe our experience teaching an undergraduate course on quantum computing using a practical, software-driven approach. We centered our course around teaching quantum algorithms through hands-on programming, reducing the significance of traditional written assignments and relying instead on self-paced programming exercises ("Quantum Katas''), a variety of programming assignments, and a final project. We observed that the programming sections of the course helped students internalize theoretical material presented during the lectures. In the survey results, students indicated that the programming exercises and the final project contributed the most to their learning process. We describe the motivation for centering the course around quantum programming, discuss major artifacts used in this course, and present our lessons learned and best practices for a future improved course offering. We hope that our experience will help guide instructors who want to adopt a practical approach to teaching quantum computing and will enable more undergraduate programs to offer quantum programming as an elective.

Resource Managers like YARN and Mesos have emerged as a critical layer in the cloud computing system stack, but the developer abstractions for leasing cluster resources and instantiating application logic are very low level. This flexibility comes at a high cost in terms of developer effort, as each application must repeatedly tackle the same challenges (e.g., fault tolerance, task scheduling and coordination) and reimplement common mechanisms (e.g., caching, bulk-data transfers). This article presents REEF, a development framework that provides a control plane for scheduling and coordinating task-level (data-plane) work on cluster resources obtained from a Resource Manager. REEF provides mechanisms that facilitate resource reuse for data caching and state management abstractions that greatly ease the development of elastic data processing pipelines on cloud platforms that support a Resource Manager service. We illustrate the power of REEF by showing applications built atop: a distributed shell application, a machine-learning framework, a distributed in-memory caching system, and a port of the CORFU system. REEF is currently an Apache top-level project that has attracted contributors from several institutions and it is being used to develop several commercial offerings such as the Azure Stream Analytics service.