Quantum systems are rarely isolated from their surroundings. These hidden environments can retain information and return it later, producing correlated errors in quantum computers and complicating the interpretation of experiments. On a quantum processor, for example, one job can therefore leave an environmental trace...
Aaron Sander, J. F. Kam, S. Gauthameshwar et al.· 0 citations
Dynamical decoupling (DD) helps maintain high-fidelity quantum computations by suppressing dephasing noise through carefully timed refocusing pulses. In quantum charge-coupled device (QCCD) architectures, however, where ions are shuttled throughout the device, transport constrains when pulses can be applied and affects...
Linus Schulte, Aaron Sander, Robert Wille· 0 citations
Matrix product state algorithms must enlarge their bond spaces as entanglement grows and compress them to control cost. We formulate basis-update and Galerkin (BUG) time integration as a sequence of canonical MPS sweeps for Hamiltonians represented as matrix product operators. We show when two natural basis updates pro...
Maximilian Fröhlich, Richard M. Milbradt, Martin Eigel et al.· 0 citations
Quantum systems are rarely isolated from their surroundings. These hidden environments can retain information and return it later, producing correlated errors in quantum computers and complicating the interpretation of experiments.On a quantum processor, for example, one job can therefore leave an environmental trace t...
Aaron Sander, J. F. Kam, S. Gauthameshwar et al.· 0 citations
A scalable noise-learning framework for Lindblad dissipation rates that combines a stochastic simulation method, the Tensor Jump Method (TJM), with gradient-free optimization of a least-squares cost-function defined on time series of local-observable expectation values is proposed.
A. R. Ramos Ramos, Maximilian Fröhlich, Aaron Sander et al.· 1 citation
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