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U(1) symmetric recurrent neural networks for quantum state reconstruction
Stewart Morawetz, Isaac J. S. De Vlugt, Juan Carrasquilla +1
Generative models are a promising technology for the enhancement of quantum simulators. These machine learning methods are capable of reconstructing a quantum state from experiment…
Autoregressive Transformer Neural Network for Simulating Open Quantum Systems via a Probabilistic Formulation
Di Luo, Zhuo Chen, Juan Carrasquilla +1
The theory of open quantum systems lays the foundations for a substantial part of modern research in quantum science and engineering. Rooted in the dimensionality of their extended…
Protocol Discovery for the Quantum Control of Majoranas by Differentiable Programming and Natural Evolution Strategies
Luuk Coopmans, Di Luo, Graham Kells +2
Quantum control, which refers to the active manipulation of physical systems described by the laws of quantum mechanics, constitutes an essential ingredient for the development of…
Exploring entanglement and optimization within the Hamiltonian Variational Ansatz
Roeland Wiersema, Cunlu Zhou, Yvette de Sereville +3
Quantum variational algorithms are one of the most promising applications of near-term quantum computers; however, recent studies have demonstrated that unless the variational quan…
Quantum process tomography with unsupervised learning and tensor networks
Giacomo Torlai, Christopher J. Wood, Atithi Acharya +3
The impressive pace of advance of quantum technology calls for robust and scalable techniques for the characterization and validation of quantum hardware. Quantum process tomograph…
Attention-based Quantum Tomography
Peter Cha, Paul Ginsparg, Felix Wu +3
With rapid progress across platforms for quantum systems, the problem of many-body quantum state reconstruction for noisy quantum states becomes an important challenge. Recent work…
Watch and learn -- a generalized approach for transferrable learning in deep neural networks via physical principles
Kyle Sprague, Juan Carrasquilla, Steve Whitelam +1
Transfer learning refers to the use of knowledge gained while solving a machine learning task and applying it to the solution of a closely related problem. Such an approach has ena…
Machine Learning for Quantum Matter
Juan Carrasquilla
Quantum matter, the research field studying phases of matter whose properties are intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter physics, ma…
Visualizing Strange Metallic Correlations in the 2D Fermi-Hubbard Model with AI
Ehsan Khatami, Elmer Guardado-Sanchez, Benjamin M. Spar +3
Strongly correlated phases of matter are often described in terms of straightforward electronic patterns. This has so far been the basis for studying the Fermi-Hubbard model realiz…
Recurrent Neural Network Wave Functions
Mohamed Hibat-Allah, Martin Ganahl, Lauren E. Hayward +2
A core technology that has emerged from the artificial intelligence revolution is the recurrent neural network (RNN). Its unique sequence-based architecture provides a tractable li…