Constructing global functional maps between molecular potentials and quantum observables

2001 ◽  
Vol 114 (21) ◽  
pp. 9325-9336 ◽  
Author(s):  
J. M. Geremia ◽  
Herschel Rabitz ◽  
Carey Rosenthal
Keyword(s):  
Molecular Potentials ◽  
Quantum Observables

scholarly journals Precise measurement of quantum observables with neural-network estimators

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Giacomo Torlai ◽  
Guglielmo Mazzola ◽  
Giuseppe Carleo ◽  
Antonio Mezzacapo
Keyword(s):  
Neural Network ◽  
Precise Measurement ◽  
Quantum Observables

Electrostatic molecular potentials: Mulliken approximation

1975 ◽  
Vol 9 (2) ◽  
pp. 193-214 ◽  
Author(s):  
Ramón Carbó ◽  
Miguel Martin
Keyword(s):  
Molecular Potentials

scholarly journals Manifestation of pointer-state correlations in complex weak values of quantum observables

2016 ◽  
Vol 94 (5) ◽  
Author(s):  
Som Kanjilal ◽  
Girish Muralidhara ◽  
Dipankar Home
Keyword(s):  
Weak Values ◽  
Quantum Observables

Simulating Surface Forces Between Iron Oxide Surfaces Immersed in Methyl Oleate Using Molecular Dynamics

2018 ◽  
Author(s):  
William W. F. Chong ◽  
Hedong Zhang
Keyword(s):  
Molecular Dynamics ◽  
Iron Oxide ◽  
Methyl Oleate ◽  
Md Simulation ◽  
Surface Forces ◽  
Oxide Surfaces ◽  
Load Bearing ◽  
Multi Scale ◽  
Molecular Potentials ◽  
Solvation Forces

Using Molecular Dynamics (MD) simulation, the current study determined the surface forces between iron oxide surfaces when immersed in methyl oleate. Condensed-phase Optimized Molecular Potentials for Atomistic Simulation Studies (COMPASS) force field was used to model the methyl oleate molecules. For the nano-confinement simulation, the iron oxide wall was modelled from its crystal structure. The nano-confinement simulation model was setup in a manner where the confined methyl oleate molecules were in contact with the bulk molecules surrounding each side of the iron oxide walls. Through the simulation, the load-separation gap profile was obtained by reducing the separation gap between the ferric oxide walls. When the separation gap was reduced from 2.75 nm to 1.88 nm, the load is shown to increase monotonically. Such increase in load bearing ability of the contact is observed to correspond to a more densely packed methyl oleate molecules, reflected by four well-formed layers across the separation gap. As the gap is dropped from 1.88 nm to 1.63 nm, the load instead reduces, indicating deteriorating load bearing ability of the contact. However, the load bearing ability of the contact is then shown to recover when the gap was further reduced till 1.38 nm. This oscillatory load trend is shown to be as a result of a layer of methyl oleate molecules being squeezed out of contact, corroborated by the density profile change where four well-formed layers were reduced to only three layers from 1.88 nm to 1.38 nm gap. This also indicates that the simulated contact exhibits structural forces, known as solvation forces. Thus, the MD simulation discussed in this study is demonstrated to be capable of providing a foundation to allow for a multi-scale simulation, integrating various force laws at different length scales, to study larger scale tribological contacts.

scholarly journals Error mitigation with Clifford quantum-circuit data

Quantum ◽  
2021 ◽  
Vol 5 ◽  
pp. 592
Author(s):  
Piotr Czarnik ◽  
Andrew Arrasmith ◽  
Patrick J. Coles ◽  
Lukasz Cincio
Keyword(s):  
State Energy ◽  
Quantum Computer ◽  
Quantum Circuit ◽  
Training Data ◽  
Quantum Circuits ◽  
Accurate Estimation ◽  
Error Mitigation ◽  
Order Of Magnitude ◽  
Quantum Observables ◽  
Near Term

Achieving near-term quantum advantage will require accurate estimation of quantum observables despite significant hardware noise. For this purpose, we propose a novel, scalable error-mitigation method that applies to gate-based quantum computers. The method generates training data {Xinoisy,Xiexact} via quantum circuits composed largely of Clifford gates, which can be efficiently simulated classically, where Xinoisy and Xiexact are noisy and noiseless observables respectively. Fitting a linear ansatz to this data then allows for the prediction of noise-free observables for arbitrary circuits. We analyze the performance of our method versus the number of qubits, circuit depth, and number of non-Clifford gates. We obtain an order-of-magnitude error reduction for a ground-state energy problem on 16 qubits in an IBMQ quantum computer and on a 64-qubit noisy simulator.

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