825 citations · 2.8k across the 22 of their papers we have counts for
22 papers
Hardware-efficient quantum error correction via concatenated bosonic qubits
Harald Putterman, Kyungjoo Noh, Connor T. Hann +118
In order to solve problems of practical importance, quantum computers will likely need to incorporate quantum error correction, where a logical qubit is redundantly encoded in many…
Recurrent neural network wave functions for Rydberg atom arrays on kagome lattice
Mohamed Hibat-Allah, Ejaaz Merali, Giacomo Torlai +2
Rydberg atom array experiments have demonstrated the ability to act as powerful quantum simulators, preparing strongly-correlated phases of matter which are challenging to study fo…
Corner entanglement of a resonating valence bond wavefunction
Giacomo Torlai, Roger G. Melko
We perform a quantum Monte Carlo simulation of the resonating valence bond wavefunction on a two-dimensional square lattice with periodic boundary conditions. Using two replicas of…
Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition
Juan Carrasquilla, Mohamed Hibat-Allah, Estelle Inack +4
Binary neural networks, i.e., neural networks whose parameters and activations are constrained to only two possible values, offer a compelling avenue for the deployment of deep lea…
Provably efficient machine learning for quantum many-body problems
Hsin-Yuan Huang, Richard Kueng, Giacomo Torlai +2
Classical machine learning (ML) provides a potentially powerful approach to solving challenging quantum many-body problems in physics and chemistry. However, the advantages of ML o…
Simulating a measurement-induced phase transition for trapped ion circuits
Stefanie Czischek, Giacomo Torlai, Sayonee Ray +2
The rise of programmable quantum devices has motivated the exploration of circuit models which could realize novel physics. A promising candidate is a class of hybrid circuits, whe…
Neural networks in quantum many-body physics: a hands-on tutorial
Juan Carrasquilla, Giacomo Torlai
Over the past years, machine learning has emerged as a powerful computational tool to tackle complex problems over a broad range of scientific disciplines. In particular, artificia…
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…
Precise measurement of quantum observables with neural-network estimators
Giacomo Torlai, Guglielmo Mazzola, Giuseppe Carleo +1
The measurement precision of modern quantum simulators is intrinsically constrained by the limited set of measurements that can be efficiently implemented on hardware. This fundame…
The learnability scaling of quantum states: restricted Boltzmann machines
Dan Sehayek, Anna Golubeva, Michael S. Albergo +3
Generative modeling with machine learning has provided a new perspective on the data-driven task of reconstructing quantum states from a set of qubit measurements. As increasingly…
Wavefunction positivization via automatic differentiation
Giacomo Torlai, Juan Carrasquilla, Matthew T. Fishman +2
We introduce a procedure to systematically search for a local unitary transformation that maps a wavefunction with a non-trivial sign structure into a positive-real form. The trans…
Machine learning quantum states in the NISQ era
Giacomo Torlai, Roger G. Melko
We review the development of generative modeling techniques in machine learning for the purpose of reconstructing real, noisy, many-qubit quantum states. Motivated by its interpret…
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…
NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems
Giuseppe Carleo, Kenny Choo, Damian Hofmann +15
We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques. The framework is built around a general and…
QuCumber: wavefunction reconstruction with neural networks
Matthew J. S. Beach, Isaac De Vlugt, Anna Golubeva +6
As we enter a new era of quantum technology, it is increasingly important to develop methods to aid in the accurate preparation of quantum states for a variety of materials, matter…
Reconstructing quantum states with generative models
Juan Carrasquilla, Giacomo Torlai, Roger G. Melko +1
A major bottleneck in the quest for scalable many-body quantum technologies is the difficulty in benchmarking their preparations, which suffer from an exponential `curse of dimensi…
Schmidt gap in random spin chains
Giacomo Torlai, Kenneth D. McAlpine, Gabriele De Chiara
We numerically investigate the low-lying entanglement spectrum of the ground state of random one-dimensional spin chains obtained after partition of the chain into two equal halves…
Latent Space Purification via Neural Density Operators
Giacomo Torlai, Roger G. Melko
Machine learning is actively being explored for its potential to design, validate, and even hybridize with near-term quantum devices. A central question is whether neural networks…
Many-body quantum state tomography with neural networks
Giacomo Torlai, Guglielmo Mazzola, Juan Carrasquilla +3
The experimental realization of increasingly complex synthetic quantum systems calls for the development of general theoretical methods, to validate and fully exploit quantum resou…
A Neural Decoder for Topological Codes
Giacomo Torlai, Roger G. Melko
We present an algorithm for error correction in topological codes that exploits modern machine learning techniques. Our decoder is constructed from a stochastic neural network call…
Learning Thermodynamics with Boltzmann Machines
Giacomo Torlai, Roger G. Melko
A Boltzmann machine is a stochastic neural network that has been extensively used in the layers of deep architectures for modern machine learning applications. In this paper, we de…
Dynamics of the entanglement spectrum in spin chains
G. Torlai, L. Tagliacozzo, G. De Chiara
We study the dynamics of the entanglement spectrum, that is the time evolution of the eigenvalues of the reduced density matrices after a bipartition of a one-dimensional spin chai…