768 citations · 1.5k across the 28 of their papers we have counts for
30 papers
An exact semidefinite characterization of the stoquasticity cone of permutationally invariant Bell operators
Jan Li, Owidiusz Makuta, Evert van Nieuwenburg +1
Bell correlations have been detected in atomic ensembles ranging from hundreds to hundreds of thousands of particles, witnessed by the permutationally invariant (PI) Bell operators…
How fast can a parent estimate the value of their children? A quantum algorithm for stochastic games
Marien Raat, Merlin Incerti-Medici, James R. Wootton +2
We give a quantum algorithm for stochastic -player games. Their game trees are the expectiminimax trees of classical game search: adversarial layers alternate with chanc…
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…
The Quantum Cartpole: A benchmark environment for non-linear reinforcement learning
Kai Meinerz, Simon Trebst, Mark Rudner +1
Feedback-based control is the de-facto standard when it comes to controlling classical stochastic systems and processes. However, standard feedback-based control methods are challe…
Efficient adaptive Bayesian estimation of a slowly fluctuating Overhauser field gradient
Jacob Benestad, Jan A. Krzywda, Evert van Nieuwenburg +1
Slow fluctuations of Overhauser fields are an important source for decoherence in spin qubits hosted in III-V semiconductor quantum dots. Focusing on the effect of the field gradie…
Real-time two-axis control of a spin qubit
Fabrizio Berritta, Torbjørn Rasmussen, Jan A. Krzywda +9
Optimal control of qubits requires the ability to adapt continuously to their ever-changing environment. We demonstrate a real-time control protocol for a two-electron singlet-trip…
Data-driven decoding of quantum error correcting codes using graph neural networks
Moritz Lange, Pontus Havström, Basudha Srivastava +6
To leverage the full potential of quantum error-correcting stabilizer codes it is crucial to have an efficient and accurate decoder. Accurate, maximum likelihood, decoders are comp…
Explainable Representation Learning of Small Quantum States
Felix Frohnert, Evert van Nieuwenburg
Unsupervised machine learning models build an internal representation of their training data without the need for explicit human guidance or feature engineering. This learned repre…
Deep learning of spatial densities in inhomogeneous correlated quantum systems
Alex Blania, Sandro Herbig, Fabian Dechent +2
Machine learning has made important headway in helping to improve the treatment of quantum many-body systems. A domain of particular relevance are correlated inhomogeneous systems.…
Learning Coulomb Diamonds in Large Quantum Dot Arrays
Oswin Krause, Anasua Chatterjee, Ferdinand Kuemmeth +1
We introduce an algorithm that is able to find the facets of Coulomb diamonds in quantum dot arrays. We simulate these arrays using the constant-interaction model, and rely only on…
A learning algorithm with emergent scaling behavior for classifying phase transitions
Nishad Maskara, Michael Buchhold, Manuel Endres +1
Machine learning-inspired techniques have emerged as a new paradigm for analysis of phase transitions in quantum matter. In this work, we introduce a supervised learning algorithm…
Hamiltonian Learning for Quantum Error Correction
Agnes Valenti, Evert van Nieuwenburg, Sebastian Huber +1
The efficient validation of quantum devices is critical for emerging technological applications. In a wide class of use-cases the precise engineering of a Hamiltonian is required b…
Integrating Neural Networks with a Quantum Simulator for State Reconstruction
Giacomo Torlai, Brian Timar, Evert P. L. van Nieuwenburg +9
We demonstrate quantum many-body state reconstruction from experimental data generated by a programmable quantum simulator, by means of a neural network model incorporating known e…
Renormalization group approach to symmetry protected topological phases
Evert P. L. van Nieuwenburg, Andreas P. Schnyder, Wei Chen
A defining feature of a symmetry protected topological phase (SPT) in one-dimension is the degeneracy of the Schmidt values for any given bipartition. For the system to go through…
Learning phase transitions from dynamics
Evert van Nieuwenburg, Eyal Bairey, Gil Refael
We propose the use of recurrent neural networks for classifying phases of matter based on the dynamics of experimentally accessible observables. We demonstrate this approach by tra…
Particle statistics and lossy dynamics of ultracold atoms in optical lattices
Jorge Yago Malo, Evert P. L. van Nieuwenburg, Mark H. Fischer +1
Experimental control over ultracold quantum gases has made it possible to investigate low-dimensional systems of both bosonic and fermionic atoms. In closed 1D systems there are a…
Discriminative Cooperative Networks for Detecting Phase Transitions
Ye-Hua Liu, Evert P. L. van Nieuwenburg
The classification of states of matter and their corresponding phase transitions is a special kind of machine-learning task, where physical data allow for the analysis of new algor…
Sensing Floquet-Majorana fermions via heat transfer
Paolo Molignini, Evert van Nieuwenburg, R. Chitra
Time periodic modulations of the transverse field in the closed XY spin-1/2 chain generate a very rich dynamical phase diagram, with a hierarchy of topological phases characterized…
Learning phase transitions by confusion
Evert P. L. van Nieuwenburg, Ye-Hua Liu, Sebastian D. Huber
Classifying phases of matter is a central problem in physics. For quantum mechanical systems, this task can be daunting owing to the exponentially large Hilbert space. Thanks to th…