1.5k citations · 5.7k across the 57 of their papers we have counts for
24 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
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…
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…
Observation of topological phenomena in a programmable lattice of 1,800 qubits
Andrew D. King, Juan Carrasquilla, Isil Ozfidan +20
The celebrated work of Berezinskii, Kosterlitz and Thouless in the 1970s revealed exotic phases of matter governed by topological properties of low-dimensional materials such as th…