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Gauge equivariant neural networks for quantum lattice gauge theories
Di Luo, Giuseppe Carleo, Bryan K. Clark +1
Gauge symmetries play a key role in physics appearing in areas such as quantum field theories of the fundamental particles and emergent degrees of freedom in quantum materials. Mot…
Classical variational simulation of the Quantum Approximate Optimization Algorithm
Matija Medvidovic, Giuseppe Carleo
A key open question in quantum computing is whether quantum algorithms can potentially offer a significant advantage over classical algorithms for tasks of practical interest. Unde…
Phases of two-dimensional spinless lattice fermions with first-quantized deep neural-network quantum states
James Stokes, Javier Robledo Moreno, Eftychios A. Pnevmatikakis +1
First-quantized deep neural network techniques are developed for analyzing strongly coupled fermionic systems on the lattice. Using a Slater-Jastrow inspired ansatz which exploits…
Variational Monte Carlo calculations of nuclei with an artificial neural-network correlator ansatz
Corey Adams, Giuseppe Carleo, Alessandro Lovato +1
The complexity of many-body quantum wave functions is a central aspect of several fields of physics and chemistry where non-perturbative interactions are prominent. Artificial neur…
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…
Natural evolution strategies and variational Monte Carlo
Tianchen Zhao, Giuseppe Carleo, James Stokes +1
A notion of quantum natural evolution strategies is introduced, which provides a geometric synthesis of a number of known quantum/classical algorithms for performing classical blac…