Simulating Neutron Scattering on an Analog Quantum Processor
- URL: http://arxiv.org/abs/2410.03958v1
- Date: Fri, 4 Oct 2024 22:39:29 GMT
- Title: Simulating Neutron Scattering on an Analog Quantum Processor
- Authors: Nora Bauer, Victor Ale, Pontus Laurell, Serena Huang, Seth Watabe, David Alan Tennant, George Siopsis,
- Abstract summary: We present a method for simulating neutron scattering on QuEra's Aquila processor.
We provide numerical simulations and experimental results for the performance of the procedure on the hardware.
We also confirm bipartite entanglement in the system experimentally.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Neutron scattering characterization of materials allows for the study of entanglement and microscopic structure, but is inefficient to simulate classically for comparison to theoretical models and predictions. However, quantum processors, notably analog quantum simulators, have the potential to offer an unprecedented, efficient method of Hamiltonian simulation by evolving a state in real time to compute phase transitions, dynamical properties, and entanglement witnesses. Here, we present a method for simulating neutron scattering on QuEra's Aquila processor by measuring the dynamic structure factor (DSF) for the prototypical example of the critical transverse field Ising chain, and propose a method for error mitigation. We provide numerical simulations and experimental results for the performance of the procedure on the hardware, up to a chain of length $L=25$. Additionally, the DSF result is used to compute the quantum Fisher information (QFI) density, where we confirm bipartite entanglement in the system experimentally.
Related papers
- Photonic Simulation of Localization Phenomena Using Boson Sampling [0.0]
We propose boson sampling as an alternative compact synthetic platform performing at room temperature.
By mapping the time-evolution unitary of a Hamiltonian onto an interferometer via continuous-variable gate decompositions, we present proof-of-principle results of localization characteristics of a single particle.
arXiv Detail & Related papers (2024-10-17T18:00:05Z) - Simulating Non-Markovian Dynamics in Multidimensional Electronic Spectroscopy via Quantum Algorithm [0.0]
We present a general approach for the simulation of the optical response of multi-chromophore systems in a structured environment.
A key step of the procedure is the pseudomode embedding of the system-environment problem resulting in a finite set of quantum states evolving.
This formulation is then solved by a collision model integrated into a quantum algorithm designed to simulate linear and nonlinear response functions.
arXiv Detail & Related papers (2024-09-09T12:13:41Z) - A Multi-Grained Symmetric Differential Equation Model for Learning
Protein-Ligand Binding Dynamics [74.93549765488103]
In drug discovery, molecular dynamics simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites.
We propose NeuralMD, the first machine learning surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding.
We show the efficiency and effectiveness of NeuralMD, with a 2000$times$ speedup over standard numerical MD simulation and outperforming all other ML approaches by up to 80% under the stability metric.
arXiv Detail & Related papers (2024-01-26T09:35:17Z) - Modeling the unphysical pseudomode model with physical ensembles:
simulation, mitigation, and restructuring of non-Markovian quantum noise [0.9549646359252346]
We consider the potential benefits of an analog or digital quantum simulation of the pseudomode model itself.
We show that the effects of the unphysical pseudomode model can still be reproduced using measurement ensemble of physical systems.
arXiv Detail & Related papers (2023-11-26T08:56:42Z) - Capturing dynamical correlations using implicit neural representations [85.66456606776552]
We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data.
In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
arXiv Detail & Related papers (2023-04-08T07:55:36Z) - Importance sampling for stochastic quantum simulations [68.8204255655161]
We introduce the qDrift protocol, which builds random product formulas by sampling from the Hamiltonian according to the coefficients.
We show that the simulation cost can be reduced while achieving the same accuracy, by considering the individual simulation cost during the sampling stage.
Results are confirmed by numerical simulations performed on a lattice nuclear effective field theory.
arXiv Detail & Related papers (2022-12-12T15:06:32Z) - Hybrid Physical-Neural ODEs for Fast N-body Simulations [0.22419496088582863]
We present a new scheme to compensate for the small-scales approximations resulting from Particle-Mesh schemes for cosmological N-body simulations.
We find that our approach outperforms PGD for the cross-correlation coefficients, and is more robust to changes in simulation settings.
arXiv Detail & Related papers (2022-07-12T13:06:06Z) - Probing finite-temperature observables in quantum simulators of spin
systems with short-time dynamics [62.997667081978825]
We show how finite-temperature observables can be obtained with an algorithm motivated from the Jarzynski equality.
We show that a finite temperature phase transition in the long-range transverse field Ising model can be characterized in trapped ion quantum simulators.
arXiv Detail & Related papers (2022-06-03T18:00:02Z) - Likelihood-Free Inference in State-Space Models with Unknown Dynamics [71.94716503075645]
We introduce a method for inferring and predicting latent states in state-space models where observations can only be simulated, and transition dynamics are unknown.
We propose a way of doing likelihood-free inference (LFI) of states and state prediction with a limited number of simulations.
arXiv Detail & Related papers (2021-11-02T12:33:42Z) - Randomizing multi-product formulas for Hamiltonian simulation [2.2049183478692584]
We introduce a scheme for quantum simulation that unites the advantages of randomized compiling on the one hand and higher-order multi-product formulas on the other.
Our framework reduces the circuit depth by circumventing the need for oblivious amplitude amplification.
Our algorithms achieve a simulation error that shrinks exponentially with the circuit depth.
arXiv Detail & Related papers (2021-01-19T19:00:23Z) - State preparation and measurement in a quantum simulation of the O(3)
sigma model [65.01359242860215]
We show that fixed points of the non-linear O(3) sigma model can be reproduced near a quantum phase transition of a spin model with just two qubits per lattice site.
We apply Trotter methods to obtain results for the complexity of adiabatic ground state preparation in both the weak-coupling and quantum-critical regimes.
We present and analyze a quantum algorithm based on non-unitary randomized simulation methods.
arXiv Detail & Related papers (2020-06-28T23:44:12Z)
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.