Zobrazeno 1 - 10
of 80
pro vyhledávání: '"Roth, Marco"'
Autor:
Schnabel, Jan, Roth, Marco
Since the entry of kernel theory in the field of quantum machine learning, quantum kernel methods (QKMs) have gained increasing attention with regard to both probing promising applications and delivering intriguing research insights. Two common appro
Externí odkaz:
http://arxiv.org/abs/2409.04406
Quantum machine learning models use encoding circuits to map data into a quantum Hilbert space. While it is well known that the architecture of these circuits significantly influences core properties of the resulting model, they are often chosen heur
Externí odkaz:
http://arxiv.org/abs/2406.02717
Autor:
Matt, Paul-Amaury, Roth, Marco
We introduce a novel quantum computing heuristic for solving the irregular strip packing problem, a significant challenge in optimizing material usage across various industries. This problem involves arranging a set of irregular polygonal pieces with
Externí odkaz:
http://arxiv.org/abs/2402.17542
Autor:
Kreplin, David A., Willmann, Moritz, Schnabel, Jan, Rapp, Frederic, Hagelüken, Manuel, Roth, Marco
sQUlearn introduces a user-friendly, NISQ-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine learning tools like scikit-learn. The library's dual-layer architecture serves both QML resear
Externí odkaz:
http://arxiv.org/abs/2311.08990
Autor:
Kreplin, David A., Roth, Marco
Publikováno v:
Quantum 8, 1385 (2024)
Quantum neural networks (QNNs) use parameterized quantum circuits with data-dependent inputs and generate outputs through the evaluation of expectation values. Calculating these expectation values necessitates repeated circuit evaluations, thus intro
Externí odkaz:
http://arxiv.org/abs/2306.01639
Autor:
Rapp, Frederic, Roth, Marco
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 parameterized quantum circuits. By employing a hardware-efficient feature map
Externí odkaz:
http://arxiv.org/abs/2304.12923
Our goal in this paper is to automatically extract a set of decision rules (rule set) that best explains a classification data set. First, a large set of decision rules is extracted from a set of decision trees trained on the data set. The rule set s
Externí odkaz:
http://arxiv.org/abs/2209.07575
Publikováno v:
Phys. Rev. A 102, 062611 (2020)
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 parameters to steer an initial eigenstate into the eigenstate of the target H
Externí odkaz:
http://arxiv.org/abs/2001.05243
Publikováno v:
Phys. Rev. Research 1, 033128 (2019)
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 invariance present in the full Hamiltonian. We analyze the determination o
Externí odkaz:
http://arxiv.org/abs/1904.08735
Autor:
Roth, Marco
Publikováno v:
The Baffler, 2021 Nov 01(60), 126-132.
Externí odkaz:
https://www.jstor.org/stable/27087397
