Juan Carrasquilla
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View article: CMT-Benchmark: A Benchmark for Condensed Matter Theory Built by Expert Researchers
CMT-Benchmark: A Benchmark for Condensed Matter Theory Built by Expert Researchers Open
Large language models (LLMs) have shown remarkable progress in coding and math problem-solving, but evaluation on advanced research-level problems in hard sciences remains scarce. To fill this gap, we present CMT-Benchmark, a dataset of 50…
View article: Leveraging Transfer Learning for Determining Germination Percentages in Gray Mold Disease (Botrytis cinerea)
Leveraging Transfer Learning for Determining Germination Percentages in Gray Mold Disease (Botrytis cinerea) Open
The rapid and accurate identification of pathogenic spores is essential for the early diagnosis of diseases in modern agriculture. Gray mold disease, caused by Botrytis cinerea, is a significant threat to several crops and is traditionally…
View article: Accurate ground states of $SU(2)$ lattice gauge theory in 2+1D and 3+1D
Accurate ground states of $SU(2)$ lattice gauge theory in 2+1D and 3+1D Open
We present a neural network wavefunction framework for solving non-Abelian lattice gauge theories in a continuous group representation. Using a combination of $SU(2)$ equivariant neural networks alongside an $SU(2)$ invariant, physics-insp…
View article: Entanglement and optimization within autoregressive neural quantum states
Entanglement and optimization within autoregressive neural quantum states Open
Neural quantum states (NQS) are powerful variational ansätze capable of representing highly entangled quantum many-body wavefunctions. While the average entanglement properties of ensembles of restricted Boltzmann machines are well underst…
View article: Recurrent neural network wave functions for Rydberg atom arrays on kagome lattice
Recurrent neural network wave functions for Rydberg atom arrays on kagome lattice Open
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 for conventional computer simulations. A key direction has be…
View article: Functional Neural Wavefunction Optimization
Functional Neural Wavefunction Optimization Open
We propose a framework for the design and analysis of optimization algorithms in variational quantum Monte Carlo, drawing on geometric insights into the corresponding function space. The framework translates infinite-dimensional optimizati…
View article: Leveraging recurrence in neural network wavefunctions for large-scale simulations of Heisenberg antiferromagnets on the triangular lattice
Leveraging recurrence in neural network wavefunctions for large-scale simulations of Heisenberg antiferromagnets on the triangular lattice Open
Variational Monte Carlo simulations have been crucial for understanding quantum many-body systems, especially when the Hamiltonian is frustrated and the ground-state wavefunction has a non-trivial sign structure. In this paper, we use recu…
View article: Geodesic algorithm for unitary gate design with time-independent Hamiltonians
Geodesic algorithm for unitary gate design with time-independent Hamiltonians Open
Larger multiqubit quantum gates allow shallower, more efficient quantum circuits, which could decrease the prohibitive effect of noise on algorithms for noisy intermediate-scale quantum (NISQ) devices and fault-tolerant error correction sc…
View article: Beyond-classical computation in quantum simulation
Beyond-classical computation in quantum simulation Open
Quantum computers hold the promise of solving certain problems that lie beyond the reach of conventional computers. However, establishing this capability, especially for impactful and meaningful problems, remains a central challenge. Here,…
View article: Leveraging recurrence in neural network wavefunctions for large-scale simulations of Heisenberg antiferromagnets on the square lattice
Leveraging recurrence in neural network wavefunctions for large-scale simulations of Heisenberg antiferromagnets on the square lattice Open
Machine-learning-based variational Monte Carlo simulations are a promising approach for targeting quantum many-body ground states, especially in two dimensions and in cases where the ground state is known to have a non-trivial sign structu…
View article: Autoregressive neural quantum states of Fermi Hubbard models
Autoregressive neural quantum states of Fermi Hubbard models Open
Neural quantum states (NQSs) have emerged as a powerful ansatz for variational quantum Monte Carlo studies of strongly correlated systems. Here, we apply recurrent neural networks (RNNs) and autoregressive transformer neural networks to th…
View article: Statistical mechanics and machine learning of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>α</mml:mi></mml:math>-Rényi ensemble
Statistical mechanics and machine learning of the -Rényi ensemble Open
We study the statistical physics of the classical Ising model in the so-called α-Rényi ensemble, a finite-temperature thermal state approximation that minimizes a modified free energy based on the α-Rényi entropy. We begin by characterizin…
View article: Autoregressive neural quantum states of Fermi Hubbard models
Autoregressive neural quantum states of Fermi Hubbard models Open
Neural quantum states (NQS) have emerged as a powerful ansatz for variational quantum Monte Carlo studies of strongly-correlated systems. Here, we apply recurrent neural networks (RNNs) and autoregressive transformer neural networks to the…
View article: Scalable quantum dynamics compilation via quantum machine learning
Scalable quantum dynamics compilation via quantum machine learning Open
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 elementary gates as possible. Compared to deterministic me…
View article: Data: Characterization of Overparameterization in Simulation of Realistic Quantum Systems
Data: Characterization of Overparameterization in Simulation of Realistic Quantum Systems Open
Raw data, figures, plot settings, and simulation settings for "Characterization of Overparameterization in Simulation of Realistic Quantum Systems" Phys. Rev. A 109, 062607 (2024) https://doi.org/10.1103/PhysRevA.109.062607
View article: Neural network approach to quasiparticle dispersions in doped antiferromagnets
Neural network approach to quasiparticle dispersions in doped antiferromagnets Open
Numerically simulating large, spinful, fermionic systems is of great interest in condensed matter physics. However, the exponential growth of the Hilbert space dimension with system size renders exact quantum state parameterizations imprac…
View article: Recurrent neural network wave functions for Rydberg atom arrays on kagome lattice
Recurrent neural network wave functions for Rydberg atom arrays on kagome lattice Open
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 for conventional computer simulations. A key direction has be…
View article: The statistical mechanics and machine learning of the $α$-Rényi ensemble
The statistical mechanics and machine learning of the $α$-Rényi ensemble Open
We study the statistical physics of the classical Ising model in the so-called $α$-Rényi ensemble, a finite-temperature thermal state approximation that minimizes a modified free energy based on the $α$-Rényi entropy. We begin by character…
View article: Here comes the SU(N): multivariate quantum gates and gradients
Here comes the SU(N): multivariate quantum gates and gradients Open
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 choice of the circuit ansatz, which consists of parameteriz…
View article: Composite Qdrift-product formulas for quantum and classical simulations in real and imaginary time
Composite Qdrift-product formulas for quantum and classical simulations in real and imaginary time Open
Recent study has shown that it can be advantageous to implement a composite channel that partitions the Hamiltonian H for a given simulation problem into subsets A and B such that H=A+B, where the terms in A are simulated with a Trotter-Su…
View article: Beyond-classical computation in quantum simulation
Beyond-classical computation in quantum simulation Open
Quantum computers hold the promise of solving certain problems that lie beyond the reach of conventional computers. However, establishing this capability, especially for impactful and meaningful problems, remains a central challenge. Here,…
View article: A framework for demonstrating practical quantum advantage: comparing quantum against classical generative models
A framework for demonstrating practical quantum advantage: comparing quantum against classical generative models Open
View article: Geodesic Algorithm for Unitary Gate Design with Time-Independent Hamiltonians
Geodesic Algorithm for Unitary Gate Design with Time-Independent Hamiltonians Open
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 quantum (NISQ) devices and fault-tolerant error correction s…
View article: Characterization of overparametrization in the simulation of realistic quantum systems
Characterization of overparametrization in the simulation of realistic quantum systems Open
Quantum computing devices require exceptional control of their experimental parameters to prepare quantum states and simulate other quantum systems. Classical optimization procedures used to find such optimal control parameters, have furth…
View article: Data: Characterization of Overparameterization in Simulation of Realistic Quantum Systems
Data: Characterization of Overparameterization in Simulation of Realistic Quantum Systems Open
<p>Raw data, figures, plot settings, and simulation settings for "Characterization of Overparameterization in Simulation of Realistic Quantum Systems"</p>
View article: Neural network approach to quasiparticle dispersions in doped antiferromagnets
Neural network approach to quasiparticle dispersions in doped antiferromagnets Open
Numerically simulating spinful, fermionic systems is of great interest from the perspective of condensed matter physics. However, the exponential growth of the Hilbert space dimension with system size renders an exact parameterization of l…
View article: A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models
A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models Open
Generative modeling has seen a rising interest in both classical and quantum machine learning, and it represents a promising candidate to obtain a practical quantum advantage in the near term. In this study, we build over a proposed framew…
View article: Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation
Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation Open
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 common computational approaches to this problem is Quantum V…
View article: Composite QDrift-Product Formulas for Quantum and Classical Simulations in Real and Imaginary Time
Composite QDrift-Product Formulas for Quantum and Classical Simulations in Real and Imaginary Time Open
Recent work has shown that it can be advantageous to implement a composite channel that partitions the Hamiltonian $H$ for a given simulation problem into subsets $A$ and $B$ such that $H=A+B$, where the terms in $A$ are simulated with a T…
View article: Measurement-induced entanglement phase transitions in variational quantum circuits
Measurement-induced entanglement phase transitions in variational quantum circuits Open
Variational quantum algorithms (VQAs), which classically optimize a parametrized quantum circuit to solve a computational task, promise to advance our understanding of quantum many-body systems and improve machine learning algorithms using…