We show how a quantum computer can be employed to elucidate reaction mechanisms in complex chemical systems, using the open problem of biological nitrogen fixation in nitrogenase as an example. We discuss how quantum computers can augment classical-computer simulations for such problems, to significantly increase their accuracy and enable hitherto intractable simulations. Detailed Read more
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 Read more
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 the bizarre behavior of quantum particles. For example, after the artificial intelligence is trained on the elementary rules of Read more
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 Read more
Generation of reversible circuits from high-level code is an important problem in the compilation flow of quantum algorithms to lower-level hardware. The instantiation of quantum oracles in particular will require mapping classical circuits to a reversible implementation. Existing tools compile and optimize reversible circuits for various metrics, such as the overall circuit size or the Read more
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 Read more
