Solving Quasiparticle Band Spectra of Real Solids using Neural-Network
Quantum States
- URL: http://arxiv.org/abs/2010.01358v2
- Date: Mon, 24 May 2021 07:41:10 GMT
- Title: Solving Quasiparticle Band Spectra of Real Solids using Neural-Network
Quantum States
- Authors: Nobuyuki Yoshioka and Wataru Mizukami and Franco Nori
- Abstract summary: We show that artificial neural networks are excellent tool for first-principles calculations of extended periodic materials.
We show that the ground-state energies in real solids in one-, two-, and three-dimensional systems are simulated precisely.
This work opens up a path to elucidate the intriguing and complex many-body phenomena in solid-state systems.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Establishing a predictive ab initio method for solid systems is one of the
fundamental goals in condensed matter physics and computational materials
science. The central challenge is how to encode a highly-complex
quantum-many-body wave function compactly. Here, we demonstrate that artificial
neural networks, known for their overwhelming expressibility in the context of
machine learning, are excellent tool for first-principles calculations of
extended periodic materials. We show that the ground-state energies in real
solids in one-, two-, and three-dimensional systems are simulated precisely,
reaching their chemical accuracy. The highlight of our work is that the
quasiparticle band spectra, which are both essential and peculiar to
solid-state systems, can be efficiently extracted with a computational
technique designed to exploit the low-lying energy structure from neural
networks. This work opens up a path to elucidate the intriguing and complex
many-body phenomena in solid-state systems.
Related papers
- Demonstration of a variational quantum eigensolver with a solid-state spin system under ambient conditions [15.044543674753308]
Quantum simulators offer the potential to utilize the quantum nature of a physical system to study another physical system.
The variational-quantum-eigensolver algorithm is a particularly promising application for investigating molecular electronic structures.
Spin-based solid-state qubits have the advantage of long decoherence time and high-fidelity quantum gates.
arXiv Detail & Related papers (2024-07-23T09:17:06Z) - Programmable Simulations of Molecules and Materials with Reconfigurable
Quantum Processors [0.3320294284424914]
We introduce a simulation framework for strongly correlated quantum systems that can be represented by model spin Hamiltonians.
Our approach leverages reconfigurable qubit architectures to programmably simulate real-time dynamics.
We show how this method can be used to compute key properties of a polynuclear transition-metal catalyst and 2D magnetic materials.
arXiv Detail & Related papers (2023-12-04T19:00:01Z) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - Robust Hamiltonian Engineering for Interacting Qudit Systems [50.591267188664666]
We develop a formalism for the robust dynamical decoupling and Hamiltonian engineering of strongly interacting qudit systems.
We experimentally demonstrate these techniques in a strongly-interacting, disordered ensemble of spin-1 nitrogen-vacancy centers.
arXiv Detail & Related papers (2023-05-16T19:12:41Z) - Learning Quantum Systems [0.0]
Quantum technologies hold the promise to revolutionise our society with ground-breaking applications in secure communication, high-performance computing and ultra-precise sensing.
One of the main features in scaling up quantum technologies is that the complexity of quantum systems scales exponentially with their size.
This poses severe challenges in the efficient calibration, benchmarking and validation of quantum states and their dynamical control.
arXiv Detail & Related papers (2022-07-01T09:47:26Z) - Neural-Network Quantum States for Periodic Systems in Continuous Space [66.03977113919439]
We introduce a family of neural quantum states for the simulation of strongly interacting systems in the presence of periodicity.
For one-dimensional systems we find very precise estimations of the ground-state energies and the radial distribution functions of the particles.
In two dimensions we obtain good estimations of the ground-state energies, comparable to results obtained from more conventional methods.
arXiv Detail & Related papers (2021-12-22T15:27:30Z) - Nuclear two point correlation functions on a quantum-computer [105.89228861548395]
We use current quantum hardware and error mitigation protocols to calculate response functions for a highly simplified nuclear model.
In this work we use current quantum hardware and error mitigation protocols to calculate response functions for a modified Fermi-Hubbard model in two dimensions with three distinguishable nucleons on four lattice sites.
arXiv Detail & Related papers (2021-11-04T16:25:33Z) - The Hintons in your Neural Network: a Quantum Field Theory View of Deep
Learning [84.33745072274942]
We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles.
On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing.
arXiv Detail & Related papers (2021-03-08T17:24:29Z) - Probing quantum information propagation with out-of-time-ordered
correlators [41.12790913835594]
Small-scale quantum information processors hold the promise to efficiently emulate many-body quantum systems.
Here, we demonstrate the measurement of out-of-time-ordered correlators (OTOCs)
A central requirement for our experiments is the ability to coherently reverse time evolution.
arXiv Detail & Related papers (2021-02-23T15:29:08Z) - Learning the ground state of a non-stoquastic quantum Hamiltonian in a
rugged neural network landscape [0.0]
We investigate a class of universal variational wave-functions based on artificial neural networks.
In particular, we show that in the present setup the neural network expressivity and Monte Carlo sampling are not primary limiting factors.
arXiv Detail & Related papers (2020-11-23T05:25:47Z) - Variational Monte Carlo calculations of $\mathbf{A\leq 4}$ nuclei with
an artificial neural-network correlator ansatz [62.997667081978825]
We introduce a neural-network quantum state ansatz to model the ground-state wave function of light nuclei.
We compute the binding energies and point-nucleon densities of $Aleq 4$ nuclei as emerging from a leading-order pionless effective field theory Hamiltonian.
arXiv Detail & Related papers (2020-07-28T14:52:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.