90 citations · 178 across the 6 of their papers we have counts for
6 papers
Reduction of finite sampling noise in quantum neural networks
David A. Kreplin, Marco Roth
Quantum neural networks (QNNs) use parameterized quantum circuits with data-dependent inputs and generate outputs through the evaluation of expectation values. Calculating these ex…
Quantum Gaussian Process Regression for Bayesian Optimization
Frederic Rapp, Marco Roth
Gaussian process regression is a well-established Bayesian machine learning method. We propose a new approach to Gaussian process regression using quantum kernels based on paramete…
Time-resolved tomography of a driven adiabatic quantum simulation
Gian Salis, Nikolaj Moll, Marco Roth +2
A typical goal of a quantum simulation is to find the energy levels and eigenstates of a given Hamiltonian. This can be realized by adiabatically varying the system control paramet…
Optimal gauge for the multimode Rabi model in circuit QED
Marco Roth, Fabian Hassler, David P. DiVincenzo
In circuit QED, a Rabi model can be derived by truncating the Hilbert space of the anharmonic qubit coupled to a linear, reactive environment. This truncation breaks the gauge inva…
Adiabatic quantum simulations with driven superconducting qubits
Marco Roth, Nikolaj Moll, Gian Salis +4
We propose a quantum simulator based on driven superconducting qubits where the interactions are generated parametrically by a polychromatic magnetic flux modulation of a tunable b…
Analysis of parametrically driven exchange-type (iSWAP) and two-photon (bSWAP) interactions between superconducting qubits
Marco Roth, Marc Ganzhorn, Nikolaj Moll +3
A current bottleneck for quantum computation is the realization of high-fidelity two-qubit quantum operations between two and more quantum bits in arrays of coupled qubits. Gates b…