768 citations · 855 across the 16 of their papers we have counts for
23 papers
Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits
Onur Danaci, Yash J. Patel, Riccardo Molteni +3
Learning problems involving quantum data are natural candidates for demonstrating an advantage in quantum machine learning. Recent results indicate that, for certain tasks and unde…
Multiple-time Quantum Imaginary Time Evolution
Julio Del Castillo, Mats Granath, Evert van Nieuwenburg
Quantum Imaginary-Time Evolution (QITE) is a powerful method for preparing ground states on quantum hardware. However, executing QITE has costly measurement budgets for general Ham…
Discovering emergent connections in quantum physics research via dynamic word embeddings
Felix Frohnert, Xuemei Gu, Mario Krenn +1
As the field of quantum physics evolves, researchers naturally form subgroups focusing on specialized problems. While this encourages in-depth exploration, it can limit the exchang…
A Monte Carlo Tree Search approach to QAOA: finding a needle in the haystack
Andoni Agirre, Evert Van Nieuwenburg, Matteo M. Wauters
The search for quantum algorithms to tackle classical combinatorial optimization problems has long been one of the most attractive yet challenging research topics in quantum comput…
Machine-learned tuning of artificial Kitaev chains from tunneling-spectroscopy measurements
Jacob Benestad, Athanasios Tsintzis, Rubén Seoane Souto +3
We demonstrate reliable machine-learned tuning of quantum-dot-based artificial Kitaev chains to Majorana sweet spots, using the covariance matrix adaptation algorithm. We show that…
Physics-informed tracking of qubit fluctuations
Fabrizio Berritta, Jan A. Krzywda, Jacob Benestad +9
Environmental fluctuations degrade the performance of solid-state qubits but can in principle be mitigated by real-time Hamiltonian estimation down to time scales set by the estima…