151 citations · 200 across the 7 of their papers we have counts for
7 papers
Two Layers, No Swaps: Biplanar SPOQC Architecture Improves Runtime of Fermi-Hubbard Simulation
Boris Bourdoncle, Peter-Jan Derks, Théo Dessertaine +1
We estimate the cost of simulating the two-dimensional Fermi-Hubbard model on a biplanar spin-optical quantum computing (SPOQC) architecture. Qubits are encoded in the honeycomb Fl…
Dynamical codes for hardware with noisy readouts
Peter-Jan H. S. Derks, Alex Townsend-Teague, Jens Eisert +3
Dynamical stabilizer codes may offer a practical route to large-scale quantum computation. Such codes are defined by a schedule of error-detecting measurements, which allows for fl…
Designing fault-tolerant circuits using detector error models
Peter-Jan H. S. Derks, Alex Townsend-Teague, Ansgar G. Burchards +1
Quantum error-correcting codes, such as subspace, subsystem, and Floquet codes, are typically constructed within the stabilizer formalism, which does not fully capture the idea of…
The domain wall color code
Konstantin Tiurev, Arthur Pesah, Peter-Jan H. S. Derks +4
We introduce the domain wall color code, a new variant of the quantum error-correcting color code that exhibits exceptionally high code-capacity error thresholds for qubits subject…
Decoding quantum color codes with MaxSAT
Lucas Berent, Lukas Burgholzer, Peter-Jan H. S. Derks +2
In classical computing, error-correcting codes are well established and are ubiquitous both in theory and practical applications. For quantum computing, error-correction is essenti…
Correcting non-independent and non-identically distributed errors with surface codes
Konstantin Tiurev, Peter-Jan H. S. Derks, Joschka Roffe +2
A common approach to studying the performance of quantum error correcting codes is to assume independent and identically distributed single-qubit errors. However, the available exp…
Training Quantum Embedding Kernels on Near-Term Quantum Computers
Thomas Hubregtsen, David Wierichs, Elies Gil-Fuster +3
Kernel methods are a cornerstone of classical machine learning. The idea of using quantum computers to compute kernels has recently attracted attention. Quantum embedding kernels (…