11 citations · 27 across the 17 of their papers we have counts for
18 papers
Support Vector Machine with a Scalable Quantum Kernel
Anant Agnihotri, Michael Krebsbach, Florentin Reiter +1
Quantum support vector machines are classification algorithms that rely on quantum-generated kernels. The fidelity quantum kernel commonly used in quantum support vector machines s…
Quantum Fanout Gates in Constant Depth via Resonance Engineering
Johannes Alexander Jaeger, Elias Zapusek, Florentin Reiter
We present a novel implementation of an n-qubit fanout gate using resonance engineering. Our proposed mechanism uses Jaynes-Cummings interactions between multiple qubits and a comm…
Quantum theory for phonon lasing and non-classical state generation in mixed-species and single trapped ions
David Baur, Tanja Behrle, Ivan Rojkov +4
In this article we present a comprehensive theoretical investigation of phonon lasing with mixed-species trapped ions, as demonstrated in [T. Behrle, Phys. Rev. Lett. 131 (2023)],…
Random layers for quantum optimal control with exponential expressivity
Marco Dall'Ara, Martin Koppenhöfer, Florentin Reiter +3
A long-standing problem in quantum optimal control is finding an optimal pulse structure that leads to an efficient exploration of the unitary space with a minimal number of optimi…
Encoding Numerical Data for Generative Quantum Machine Learning
Michael Krebsbach, Florentin Reiter, Thomas Wellens +2
Generative quantum machine learning models are trained to deduce the probability distribution underlying a given dataset, and to produce new, synthetic samples from it. The majorit…
Learning Reduced Representations for Quantum Classifiers
Patrick Odagiu, Vasilis Belis, Lennart Schulze +6
Data sets that are specified by a large number of features are currently outside the area of applicability for quantum machine learning algorithms. An immediate solution to this im…
Laser-enhanced quantum sensing boosts sensitivity and dynamic range
Florian Schall, Lukas Lindner, Yves Rottstaedt +8
Magnetometers based on nitrogen-vacancy (NV) centers in diamond have emerged as the most important solid-state quantum sensors. However, ensembles are limited in optical contrast t…
Scaling Quantum Algorithms via Dissipation: Avoiding Barren Plateaus
Elias Zapusek, Ivan Rojkov, Florentin Reiter
Variational quantum algorithms (VQAs) have enabled a wide range of applications on near-term quantum devices. However, their scalability is fundamentally limited by barren plateaus…
Scalable dissipative quantum error correction for qubit codes
Ivan Rojkov, Elias Zapusek, Florentin Reiter
Dissipative quantum error correction (QEC) autonomously protects quantum information using engineered dissipation and offers a promising alternative to error correction via measure…
Fieldoscopy at the Quantum Limit
Dmitry A. Zimin, Arjun Ashoka, Florentin Reiter +1
We demonstrate a novel concept for measuring time-varying electric field transients of petahertz-scale photons down to a single-photon regime. We observe a clear transition from cl…
Extrapolation method to optimize linear-ramp QAOA parameters: Evaluation of QAOA runtime scaling
Vanessa Dehn, Martin Zaefferer, Gerhard Hellstern +3
The Quantum Approximate Optimization Algorithm (QAOA) has been suggested as a promising candidate for the solution of combinatorial optimization problems. Yet, whether - or under w…
Two-media laser threshold magnetometry: A magnetic-field-dependent laser threshold
Yves Rottstaedt, Lukas Lindner, Florian Schall +9
Nitrogen-vacancy (NV) centers in diamond are a promising platform for high-precision magnetometry. In contrast to the use of spontaneous emission in a number of NV-magnetometers, l…
Variational quantum thermalizers based on weakly-symmetric nonunitary multi-qubit operations
Elias Zapusek, Kristina Kirova, Walter Hahn +2
We propose incorporating multi-qubit nonunitary operations in Variational Quantum Thermalizers (VQTs). VQTs are hybrid quantum-classical algorithms that generate the thermal (Gibbs…
Learning agent-based approach to the characterization of open quantum systems
Lorenzo Fioroni, Ivan Rojkov, Florentin Reiter
Characterizing quantum processes is crucial for the execution of quantum algorithms on available quantum devices. A powerful framework for this purpose is the Quantum Model Learnin…
Stabilization of cat-state manifolds using nonlinear reservoir engineering
Ivan Rojkov, Matteo Simoni, Elias Zapusek +2
We introduce a novel reservoir engineering approach for stabilizing multi-component Schrödinger's cat manifolds. The fundamental principle of the method lies in the destructive int…
Guided Quantum Compression for High Dimensional Data Classification
Vasilis Belis, Patrick Odagiu, Michele Grossi +3
Quantum machine learning provides a fundamentally different approach to analyzing data. However, many interesting datasets are too complex for currently available quantum computers…
Spin coherence in strongly coupled spin baths in quasi-two-dimensional layers
Philip Schätzle, Reyhaneh Ghassemizadeh, Daniel F. Urban +5
We investigate the spin-coherence decay of NV-spins interacting with the strongly-coupled bath of nitrogen defects in diamond layers. For thin diamond layers, we demonstrate th…
Provable advantages of kernel-based quantum learners and quantum preprocessing based on Grover's algorithm
Till Muser, Elias Zapusek, Vasilis Belis +1
There is an ongoing effort to find quantum speedups for learning problems. Recently, [Y. Liu et al., Nat. Phys. , 1013--1017 (2021)] have proven an exponential speedup…