From the blog

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

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

March 27th, 2017|Comments Off on 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 [...]

  • Learning quantum physics

Solving the quantum many-body problem with artificial neural networks

February 15th, 2017|Comments Off on Solving the quantum many-body problem with artificial neural networks

Working together, ETH Zurich and Microsoft QuArC researchers have provided the first application of machine-learning techniques to solve outstanding problems in quantum physics. The neural networks used in their study developed a genuine intuition of [...]

  • Design automation and design space exploration for quantum computers

Design automation and design space exploration for quantum computers

January 25th, 2017|Comments Off on 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 [...]

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