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Microsoft Quantum
Dave Wecker in lab

Achieving practical quantum computing

To gain a broader understanding of quantum computing, check out this comprehensive overview from Microsoft Quantum Architect Dave Wecker. You’ll learn how quantum computing is different from classical computing, the types of problems a quantum computer can solve, and what makes topological qubits so unique....

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Magic state distillation with low space overhead and optimal asymptotic input count

  In our quest for topological quantum computing with Majorana zero modes, one missing piece is the efficient, high-quality creation of magic states to perform the π/8 (or “T” gate). Our new paper, Magic State Distillation with Low Space Overhead and Optimal Asymptotic Input Count, provides a family of solutions to this need, allowing for a wide range...

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Design automation and design space exploration for quantum computers

  A major hurdle for quantum algorithms for linear systems of equations, and for quantum simulation algorithms, is the difficulty to find simple circuits for arithmetic. Prior approaches typically led to a large overhead in terms of quantum memory, required operations, or implementation error. By leveraging recent advances in reversible logic synthesis, Martin Roetteler and...

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Common framework for scientific experiments: QCoDeS

  QCoDeS is an open source data acquisition framework that was created by distilling the homegrown solutions used in Station Q’s experimental labs, and infused with all the best practices from the open source software world. It includes a simple syntax to define complex sweeps over n-dimensional parameter space, all the machinery required to visualize...

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Training a quantum optimizer

  In this paper, published in Physical Review A, we show how to greatly improve success at solving Constraint Satisfaction Problems on a quantum computer by using a learned schedule, instead of the standard linear ramps. The technique actually improves as the problem gets larger and more difficult, allowing classical machines to learn optimizations that...

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