1.5k citations · 5.7k across the 57 of their papers we have counts for
59 papers
Connectivity determines the capability of sparse neural network quantum states
Brandon Barton, Juan Carrasquilla, Christopher Roth +1
The Lottery Ticket Hypothesis (LTH) posits that within overparametrized neural networks, there exist sparse subnetworks that are capable of matching the performance of the original…
Scalable quantum dynamics compilation via quantum machine learning
Yuxuan Zhang, Roeland Wiersema, Juan Carrasquilla +2
Quantum dynamics compilation is an important task for improving quantum simulation efficiency: It aims to synthesize multi-qubit target dynamics into a circuit consisting of as few…
Observation of a non-Hermitian supersonic mode on a trapped-ion quantum computer
Yuxuan Zhang, Juan Carrasquilla, Yong Baek Kim
Quantum computers have long been anticipated to excel in simulating quantum many-body physics. While most previous work has focused on Hermitian physics, we demonstrate the power o…
The statistical mechanics and machine learning of the -Rényi ensemble
Andrew Jreissaty, Juan Carrasquilla
We study the statistical physics of the classical Ising model in the so-called -Rényi ensemble, a finite-temperature thermal state approximation that minimizes a modified free e…
Beyond-classical computation in quantum simulation
Andrew D. King, Alberto Nocera, Marek M. Rams +60
Quantum computers hold the promise of solving certain problems that lie beyond the reach of conventional computers. However, establishing this capability, especially for impactful…
Geodesic Algorithm for Unitary Gate Design with Time-Independent Hamiltonians
Dylan Lewis, Roeland Wiersema, Juan Carrasquilla +1
Larger multi-qubit quantum gates allow shallower, more efficient quantum circuits, which could decrease the prohibitive effect of noise on algorithms for noisy intermediate-scale q…
Characterization of overparametrization in the simulation of realistic quantum systems
Matthew Duschenes, Juan Carrasquilla, Raymond Laflamme
Quantum computing devices require exceptional control of their experimental parameters to prepare quantum states and simulate other quantum systems. Classical optimization procedur…
Neural network approach to quasiparticle dispersions in doped antiferromagnets
Hannah Lange, Fabian Döschl, Juan Carrasquilla +1
Numerically simulating spinful, fermionic systems is of great interest from the perspective of condensed matter physics. However, the exponential growth of the Hilbert space dimens…
Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation
Kirill Neklyudov, Jannes Nys, Luca Thiede +4
Solving the quantum many-body Schrödinger equation is a fundamental and challenging problem in the fields of quantum physics, quantum chemistry, and material sciences. One of the c…
Composite QDrift-Product Formulas for Quantum and Classical Simulations in Real and Imaginary Time
Matthew Pocrnic, Matthew Hagan, Juan Carrasquilla +2
Recent work has shown that it can be advantageous to implement a composite channel that partitions the Hamiltonian for a given simulation problem into subsets and such…
Quantum-Classical Multiple Kernel Learning
Ara Ghukasyan, Jack S. Baker, Oktay Goktas +2
As quantum computers become increasingly practical, so does the prospect of using quantum computation to improve upon traditional algorithms. Kernel methods in machine learning is…
Here comes the SU(N): multivariate quantum gates and gradients
Roeland Wiersema, Dylan Lewis, David Wierichs +2
Variational quantum algorithms use non-convex optimization methods to find the optimal parameters for a parametrized quantum circuit in order to solve a computational problem. The…
Investigating Topological Order using Recurrent Neural Networks
Mohamed Hibat-Allah, Roger G. Melko, Juan Carrasquilla
Recurrent neural networks (RNNs), originally developed for natural language processing, hold great promise for accurately describing strongly correlated quantum many-body systems.…
Variational Benchmarks for Quantum Many-Body Problems
Dian Wu, Riccardo Rossi, Filippo Vicentini +30
The continued development of computational approaches to many-body ground-state problems in physics and chemistry calls for a consistent way to assess its overall progress. In this…
Kagome qubit ice
Alejandro Lopez-Bezanilla, Jack Raymond, Kelly Boothby +3
Topological phases of spin liquids with constrained disorder can host a kinetics of fractionalized excitations. However, spin-liquid phases with distinct kinetic regimes have prove…
Supplementing Recurrent Neural Network Wave Functions with Symmetry and Annealing to Improve Accuracy
Mohamed Hibat-Allah, Roger G. Melko, Juan Carrasquilla
Recurrent neural networks (RNNs) are a class of neural networks that have emerged from the paradigm of artificial intelligence and has enabled lots of interesting advances in the f…
Modern applications of machine learning in quantum sciences
Anna Dawid, Julian Arnold, Borja Requena +26
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learn…
Circuit connectivity boosts by quantum-classical-quantum interfaces
Roeland Wiersema, Leonardo Guerini, Juan Felipe Carrasquilla +1
High-connectivity circuits are a major roadblock for current quantum hardware. We propose a hybrid classical-quantum algorithm to simulate such circuits without swap-gate ladders.…
Quasiprobabilistic state-overlap estimator for NISQ devices
Leonardo Guerini, Roeland Wiersema, Juan Felipe Carrasquilla +1
As quantum technologies mature, the development of tools for benchmarking their ability to prepare and manipulate complex quantum states becomes increasingly necessary. A key conce…
Measurement-induced entanglement phase transitions in variational quantum circuits
Roeland Wiersema, Cunlu Zhou, Juan Felipe Carrasquilla +1
Variational quantum algorithms (VQAs), which classically optimize a parametrized quantum circuit to solve a computational task, promise to advance our understanding of quantum many…
Optimal control of quantum thermal machines using machine learning
Ilia Khait, Juan Carrasquilla, Dvira Segal
Identifying optimal thermodynamical processes has been the essence of thermodynamics since its inception. Here, we show that differentiable programming (DP), a machine learning (ML…
Neural Error Mitigation of Near-Term Quantum Simulations
Elizabeth R. Bennewitz, Florian Hopfmueller, Bohdan Kulchytskyy +2
Near-term quantum computers provide a promising platform for finding ground states of quantum systems, which is an essential task in physics, chemistry, and materials science. Near…
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…
Variational Neural Annealing
Mohamed Hibat-Allah, Estelle M. Inack, Roeland Wiersema +2
Many important challenges in science and technology can be cast as optimization problems. When viewed in a statistical physics framework, these can be tackled by simulated annealin…