Hamiltonian Parameters Research Articles

Spectroscopic and Theoretical Investigation of High-Spin Square-Planar and Trigonal Fe(II) Complexes Supported by Fluorinated Alkoxides.

The electronic structures and spectroscopic behavior of three high-spin FeII complexes of fluorinated alkoxides were studied: square-planar {K(DME)2}2[Fe(pinF)2] (S) and quasi square-planar {K(C222)}2[Fe(pinF)2] (S') and trigonal-planar {K(18C6)}[Fe(OC4F9)3] (T) where pinF = perfluoropinacolate and OC4F9 = tris-perfluoro-t-butoxide. The zero-field splitting (ZFS) and hyperfine structure parameters of the S = 2 ground states were determined using field-dependent 57Fe Mössbauer and high-field and -frequency electron paramagnetic resonance (HFEPR) spectroscopies. The spin Hamiltonian parameters were analyzed with crystal field theory and corroborated by density functional theory (DFT) and ab initio complete active space self-consistent field (CASSCF) calculations. Whereas the ZFS tensor of S has a small rhombicity, E/D = 0.082, and a positive D = 15.17 cm-1, T exhibits a negative D = -9.16 cm-1 and a large rhombicity, E/D = 0.246. Computational investigation of the structural factors suggests that the ground-state electronic configuration and geometry of T's Fe site are determined by the interaction of [Fe(OC4F9)3]- with {K(18C6)}+. In contrast, two distinct countercations of S/S' have a negligible influence on their [Fe(pinF)2]2- moieties. Instead, the distortions in S' are likely induced by the chelate ring conformation change from δλ, observed for S, to the δδ conformation, determined for S'.

Mitigated barren plateaus in the time-nonlocal optimization of analog quantum-algorithm protocols

Quantum machine learning has emerged as a promising utilization of near-term quantum computation devices. However, algorithmic classes such as variational quantum algorithms have been shown to suffer from barren plateaus due to vanishing gradients in their parameters spaces. We present an approach to quantum algorithm optimization that is based on trainable Fourier coefficients of Hamiltonian system parameters. Our is exclusive to the extension of discrete quantum variational algorithms to analog quantum optimal control schemes and is nonlocal in time. We demonstrate the viability of our on the objectives of compiling the quantum Fourier transform and preparing ground states of random problem Hamiltonians. In comparison to the temporally local discretization in quantum optimal control and parametrized circuits, our exhibits faster and more consistent convergence. We uniformly sample objective gradients across the parameter space and find that in our the variance decays at a nonexponential rate with the number of qubits, while it decays at an exponential rate in the temporally local benchmark . This indicates the mitigation of barren plateaus in our . We propose our as a viable candidate for near-term quantum machine learning. Published by the American Physical Society 2024

Third-order exceptional line in a nitrogen-vacancy spin system.

Exceptional points (EPs) are singularities in non-Hermitian systems, where k (k ≥ 2) eigenvalues and eigenstates coalesce. High-order EPs exhibit richer topological characteristics and better sensing performance than second-order EPs. Theory predicts even richer non-Hermitian topological phases for high-order EP geometries, such as lines or rings formed entirely by high-order EPs. However, experimental exploration of high-order EP geometries has hitherto proved difficult due to the demand for more degrees of freedom in the Hamiltonian's parameter space or a higher level of symmetries. Here we observe a third-order exceptional line in an atomic-scale system. To this end, we use a nitrogen-vacancy spin in diamond and introduce multiple symmetries in the non-Hermitian Hamiltonian realized with the system. Furthermore, we show that the symmetries play an essential role in the occurrence of high-order EP geometries. Our approach can in future be further applied to explore high-order EP-related topological physics at the atomic scale and, potentially, for applications of high-order EPs in quantum technologies.

Quantum phase transitions in skewed ladder systems.

In this brief review, we introduce a new spin ladder system called skewed spin ladders and discuss the exotic quantum phases of this system. The spin ladders studied are the 5/7, 3/4 and 3/5 systems corresponding to alternately fused 5 and 7 membered rings; 3 and 4 membered rings; and 3 and 5 membered rings. These ladders show completely different behaviour as the Hamiltonian model parameter is changed. When the Hamiltonian parameter is increased the 5/7 ladder switches from an initial singlet ground state to progressively higher spin ground state and then to a reentrant singlet state before finally settling to the highest spin ground state whose spin equals the number of unit cells in the system. The 3/4 ladder goes from a singlet ground state to a high spin ground state with each unit cell contributing spin 1 to the state, as the model parameter is increased. The 3/5 ladder shows a singlet ground state for small parameters and high spin ground state for intermediate values of the parameter and for still higher parameters, a reentrant singlet ground state. They can also show interesting magnetization plateaus as illustrated by studies on a specific spin ladder.

Hamiltonian learning with real-space impurity tomography in topological moiré superconductors

Extracting Hamiltonian parameters from available experimental data is a challenge in quantum materials. In particular, real-space spectroscopy methods such as scanning tunneling spectroscopy allow probing electronic states with atomic resolution, yet even in those instances extracting the effective Hamiltonian is an open challenge. Here we show that impurity states in modulated systems provide a promising approach to extracting non-trivial Hamiltonian parameters of a quantum material. We show that by combining the real-space spectroscopy of different impurity locations in a moiré topological superconductor, modulations of exchange and superconducting parameters can be inferred via machine learning. We demonstrate our strategy with a physically-inspired harmonic expansion combined with a fully-connected neural network that we benchmark against a conventional convolutional architecture. We show that while both approaches allow extracting exchange modulations, only the former approach allows inferring the features of the superconducting order. Our results demonstrate the potential of machine learning methods to extract Hamiltonian parameters by real-space impurity spectroscopy as local probes of a topological state.

Probing structural and dynamic properties of MAPbCl3 hybrid perovskite using Mn2+ EPR.

Hybrid methylammonium (MA) lead halide perovskites have emerged as materials exhibiting excellent photovoltaic performance related to their rich structural and dynamic properties. Here, we use multifrequency (X-, Q-, and W-band) electron paramagnetic resonance (EPR) spectroscopy of Mn2+ impurities in MAPbCl3 to probe the structural and dynamic properties of both the organic and inorganic sublattices of this compound. The temperature dependent continuous-wave (CW) EPR experiments reveal a sudden change of the Mn2+ spin Hamiltonian parameters at the phase transition to the ordered orthorhombic phase indicating its first-order character and significant slowing down of the MA cation reorientation. Pulsed EPR experiments are employed to measure the temperature dependences of the spin-lattice relaxation T1 and decoherence T2 times of the Mn2+ ions in the orthorhombic phase of MAPbCl3 revealing a coupling between the spin center and vibrations of the inorganic framework. Low-temperature electron spin echo envelope modulation (ESEEM) experiments of the protonated and deuterated MAPbCl3 analogues show the presence of quantum rotational tunneling of the ammonium groups, allowing to accurately probe their rotational energy landscape.

Delving into theoretical and computational considerations for accurate calculation of chemical shifts in paramagnetic transition metal systems using quantum chemical methods.

The chemical shielding tensor for a paramagnetic system has been derived from the macroscopically observed magnetization using the perturbation theory. An approach to calculate the paramagnetic chemical shifts in transition metal systems based on the spin-only magnetic susceptibility directly evaluated from the ab initio Hilbert space of the electronic Zeeman Hamiltonian has been discussed. Computationally, several advantages are associated with this approach: (a) it includes the state-specific paramagnetic Curie (first-order) and Van Vleck (second-order) contributions of the paramagnetic ion to the paramagnetic chemical shifts; (b) thus it avoids the system-specific modeling and evaluating effectively in terms of the electron paramagnetic resonance (EPR) spin Hamiltonian parameters of the magnetic moment of the paramagnetic ion formulated previously; (c) it can be utilized both in the point-dipole (PD) approximation (in the long-range) and with the quantum chemical (QC) method based the hyperfine tensors (in the short-range). Additionally, we have examined the predictive performance of various density functional theory (DFT) functionals of different families and commonly used core-augmented basis sets for nuclear magnetic resonance (NMR) chemical shifts. A selection of transition metal ion complexes with and without first-order orbital contributions, namely the [M(AcPyOx)3(BPh)]+ complexes of M = Mn2+, Ni2+ and Co2+ ions and CoTp2 complex and their reported NMR chemical shifts are studied from QC methods for illustration.

Mean Field Study of a Cylindrical Ferrimagnetic Nanotube with Different Anisotropies

In this paper, the ferrimagnetic mixed spins (1, 5/2) Blume-Capel model is proposed to investigate the phase diagrams and hysteresis behaviors of a magnetic cylindrical nanotube with a core-shell structure using the mean-field approximation based on the Bogoliubov inequality for the Gibbs free energy. The core sites are occupied by σ= ±1, 0 spins, whereas the shell sites are filled by S= ±5/2, ±3/2, ±1/2 spins. The effects of exchange couplings (Jin, JS) and single-ion anisotropies (DC, DS) on core, shell, and total magnetizations are investigated, as well as hysteresis behaviors. The entropy, free energy, and specific heat are analyzed to establish the stability of the solutions. The presentation and discussion of phase diagrams is detailed. The system shows a first-order and second-order phase transitions, as well as tricritical and critical end- points. In addition, the system shows compensation and reentrant behaviors. Various multiple hysteresis loop behaviors are seen according on the Hamiltonian parameters, such as the presence of triple, quintuple, and septuple hysteresis loops.

EPR of LiNaGe4O9:Mn crystal

EPR spectra of manganese ions were studied in lithium-sodium tetragermanate LiNaGe4O9 crystal. The observed spectrum was typical for doping ions in tetravalent Mn4+ state (3d 3, S=3/2) and for main orientations of static magnetic field along the crystal axes (T=296 K) consisted of hyperfine sextuplet from electronic transition Ms= +1/2 ↔ – 1/2. Transitions Ms= ±3/2 ↔±1/2 were not detected owing to strong interaction of the spin system with crystal field as compared with electron Zeeman interaction in the X frequency range of EPR registration. The angular variations of the spectrum were measured for static field B rotated in three main crystal planes. Since only central transition (Ms= +1/2 ↔ – 1/2) was detected and the splitting of the fine structure remained unknown, the spectrum was described in the approximation of effective spin Seff=1/2. The parameters of the spin Hamiltonian, including electron Zeeman and hyperfine interactions, were calculated. The magnetic multiplicity of the spectrum (km=2) evidenced that paramagnetic centers occupied the sites with monoclinic C2 point symmetry. Taking into consideration the positional symmetry as well ionic charges and radii of the guest ion and the hosts, it was concluded that paramagnetic Mn4+ centers substituted for Ge4+ within oxygen octahedral complexes in the crystal lattice of LiNaGe4O9.

Enhancing Spin-Transport Characteristics, Spin-Filtering Efficiency, and Negative Differential Resistance in Exchange-Coupled Dinuclear Co(II) Complexes for Molecular Spintronics Applications.

Single-molecule spintronics, where electron transport occurs via a paramagnetic molecule, has gained wide attention due to its potential applications in the area of memory devices to switches. While numerous organic and some inorganic complexes have been employed over the years, there are only a few attempts to employ exchange coupled dinuclear complexes at the interface, and the advantage of fabricating such a molecular spintronics device in the observation of switchable Kondo resonance was demonstrated recently in the dinuclear [Co2(L)(hfac)4] (1) complex (Wagner et al., Nat. Nanotechnol. 2013, 8, 575-579). In this work, employing an array of theoretical tools such as density functional theory (DFT), the ab initio CASSCF/NEVPT2 method, and DFT combined with nonequilibrium Green Function (NEGF) formalism, we studied in detail the role of magnetic coupling, ligand field, and magnetic anisotropy in the transport characteristics of complex 1. Particularly, our calculations not only reproduce the current-voltage (I-V) characteristics observed in experiments but also unequivocally establish that these arise from an exchange-coupled singlet state that arises due to antiferromagnetic coupling between two high-spin Co(II) centers. Further, the estimated spin Hamiltonian parameters such as J, g values, and D and E/D values are only marginally altered for the molecule at the interface. Further, the exchange-coupled state was found to have very similar transport responses, despite possessing significantly different geometries. Our transport calculations unveil a new feature of the negative differential resistance (NDR) effect on 1 at the bias voltage of 0.9 V, which agrees with the experimental I-V characteristics reported. The spin-filtering efficiency (SFE) computed for the spin-coupled states was found to be only marginal (∼25%); however, if the ligand field is fine-tuned to obtain a low-spin Co(II) center, a substantial SFE of 44% was noted. This spin-coupled state also yields a very strong NDR with a peak-to-valley ratio (PVR) of ∼56 - a record number that has not been witnessed so far in this class of compounds. Additionally, we have established further magnetostructural-transport correlations, providing valuable insights into how microscopic spin Hamiltonian parameters can be associated with SFE. Several design clues to improve the spin-transport characteristics, SFE and NDR in this class of molecule, are offered.

Influence of a new long-range interaction on the magnetic properties of a 2D Ising layered model by using Monte Carlo method

This paper presents a novel interpretation of the long-range interaction in interlayers to analyze the magnetic properties of a 2D Ising layered model. To describe the long-range interaction, spin chains are introduced, while magnetic and thermodynamic behaviors of the 2D Ising layered model are investigated by Monte Carlo methods. Phase diagrams in various parameter planes are produced. Furthermore, we investigate the hysteresis behaviors and critical exponents, which are consistent with previous findings. Finally, we analyze the magnetocaloric effects, including entropy change and relative cooling power (RCP), caused by different Hamiltonian parameters.

Theoretical studies on the local structure and spin Hamiltonian parameters for Cu2+ ions in LiTaO3 crystal.

The local structure and spin Hamiltonian parameters (SHPs) g factors (gx , gy , gz ) and the hyperfine structure constants (Ax , Ay , Az ) for Cu2+ doped in the LiTaO3 crystal are theoretically investigated by the perturbation formulas for a 3d9 ion under rhombically elongated octahedral based on the cluster approach. The impurity Cu2+ was assumed to occupy the host trigonally-distorted octahedral Li+ site and experience the Jahn-Teller (JT) distortion from the host trigonal octahedral [TaO6 ]10- to the impurity rhombically elongated octahedral [CuO6 ]10- . Based on the calculations, the impurity-ligand bond lengths parallel and perpendicular to the C2 -axis are found to be R|| (≈ 2.305 Å) and R⊥ (≈ 2.112 Å) for the studied [CuO6 ]10- cluster, with the planar bond angle θ (≈ 78.2°). Meanwhile, the ground-state wave function for Cu2+ center in LiTaO3 was also obtained. The calculated SHPs based on the above local lattice distortions agree well with the experimental data, and the results are discussed.

Entanglement dynamics with string measurement operators

We explain how to apply a Gaussian-preserving operator to a fermionic Gaussian state. We use this method to study the evolution of the entanglement entropy of an Ising spin chain undergoing a quantum-jump dynamics with string measurement operators. We find that, for finite-range string operators, the asymptotic entanglement entropy exhibits a crossover from a logarithmic to an area-law scaling with the system size, depending on the Hamiltonian parameters as well as on the measurement strength. For ranges of the string which scale extensively with the system size, the asymptotic entanglement entropy shows a volume-law scaling, independently of the measurement strength and the Hamiltonian dynamics. The same behavior is observed for the measurement-only dynamics, suggesting that measurements may play a leading role in this context.

Powering quantum Otto engines only with q-deformation of the working substance.

We consider a quantum Otto cycle with a q-deformed quantum oscillator working substance and classical thermal baths. We investigate the influence of the quantum statistical deformation parameter q on the work and efficiency of the cycle. In usual quantum Otto cycle, a Hamiltonian parameter is varied during the quantum adiabatic stages while the quantum statistical character of the working substance remains fixed. We point out that even if the Hamiltonian parameters are not changing, work can be harvested by quantum statistical changes of the working substance. Work extraction from thermal resources using quantum statistical mutations of the working substance makes a quantum Otto cycle without any classical analog.

Thermal local quantum uncertainty in a two-qubit-superconducting system under decoherence

By considering the local quantum uncertainty (LQU) as a measure of quantum correlations, the thermal evolution of a two-qubit-superconducting system is investigated. We show that the thermal LQU can be increased by manipulating the Hamiltonian parameters such as the mutual coupling and Josephson energies, however, it undergoes sudden transitions at specific temperatures. Furthermore, a detailed analysis is presented regarding the impact of decohering channels on thermal LQU. This controllable LQU in engineering applications can disclose the advantage enabled in the superconducting charge qubits for designing quantum computers and quantum batteries.

Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions.

A key goal of molecular modeling is the accurate reproduction of the true quantum mechanical potential energy of arbitrary molecular ensembles with a tractable classical approximation. The challenges are that analytical expressions found in general purpose force fields struggle to faithfully represent the intermolecular quantum potential energy surface at close distances and in strong interaction regimes; that the more accurate neural network approximations do not capture crucial physics concepts, e.g., nonadditive inductive contributions and application of electric fields; and that the ultra-accurate narrowly targeted models have difficulty generalizing to the entire chemical space. We therefore designed a hybrid wide-coverage intermolecular interaction model consisting of an analytically polarizable force field combined with a short-range neural network correction for the total intermolecular interaction energy. Here, we describe the methodology and apply the model to accurately determine the properties of water, the free energy of solvation of neutral and charged molecules, and the binding free energy of ligands to proteins. The correction is subtyped for distinct chemical species to match the underlying force field, to segment and reduce the amount of quantum training data, and to increase accuracy and computational speed. For the systems considered, the hybrid ab initio parametrized Hamiltonian reproduces the two-body dimer quantum mechanics (QM) energies to within 0.03 kcal/mol and the nonadditive many-molecule contributions to within 2%. Simulations of molecular systems using this interaction model run at speeds of several nanoseconds per day.

Structural Time Series Model using Hamiltonian Monte Carlo for Rice Price

Although forecasts of future events are simply uncertain, predicting is one of the most important aspects of future planning. Accurate rice price predictions tend to be helpful for wholesalers, producers, and farmers to develop plans and strategies to reduce the risks that can be faced. Structural time series models are the most plausible alternative for long-term forecasting. This paper proposes an alternate method for modeling average rice prices using structural time series along with Bayesian parameter inference via Hamiltonian Monte Carlo (HMC). The model has been built using the monthly average wholesale rice price from January 2010 to December 2019. For working out both structural time series and HMC, the TensorFlow Probability Library was used. Linear trend, seasonal, and autoregressive components were combined as an additive model to the structural time model. The proposed Hamiltonian parameter produces an optimal acceptance rate. Their trace plot was used to diagnose the convergence of their chain. One of the predictive accuracy of models was assessed using the mean absolute percent error (MAPE). Through both single and multiple chain iterations, the prediction accuracy of a year-ahead is highly accurate, with MAPE less than 2%. Long-term iteration draws during Hamiltonian Monte Carlo should be considered when attempting to achieve more convergence.

Broadband EPR Spectroscopy of the Triplet State: Multi-Frequency Analysis of Copper Acetate Monohydrate.

Electron paramagnetic resonance spectroscopy is a long-standing method for the exploration of electronic structures of transition ion complexes. The difficulty of its analysis varies considerably, not only with the nature of the spin system, but more so with the relative magnitudes of the magnetic interactions to which the spin is subject, where particularly challenging cases ensue when two interactions are of comparable magnitude. A case in point is the triplet system S = 1 of coordination complexes with two unpaired electrons when the electronic Zeeman interaction and the electronic zero-field interaction are similar in strength. This situation occurs in the X-band spectra of the thermally excited triplet state of dinuclear copper(II) complexes, exemplified by copper acetate monohydrate. In this study, applicability of the recently developed low-frequency broadband EPR spectrometer to S = 1 systems is investigated on the analysis of multi-frequency, 0.5-16 GHz, data from [Cu(CH3COO)2H2O]2. Global fitting affords the spin Hamiltonian parameters gz = 2.365 ± 0.008; gy = 2.055 ± 0.010; gx = 2.077 ± 0.005; Az = 64 gauss; D = 0.335 ± 0.002 cm-1; E = 0.0105 ± 0.0003 cm-1. The latter two define zero-field absorptions at ca. 630, 7730, and 10,360 MHz, which show up in the spectra as one half of a sharpened symmetrical line. Overall, the EPR line shape is Lorentzian, reflecting spin-lattice relaxation, which is a combination of an unusual, essentially temperature-independent, inverted Orbach process via the S = 0 ground state, and a Raman process proportional to T2. Other broadening mechanisms are limited to at best minor contributions from a distribution in E values, and from dipolar interaction with neighboring copper pairs. Monitoring of a first-order double-quantum transition between 8 and 35 GHz shows a previously unnoticed very complex line shape behavior, which should be the subject of future research.

Synergy of semiempirical models and machine learning in computational chemistry.

Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions of chemical space, e.g., new bonding types, new many-body interactions. Another important limitation is the spatial locality assumption in model architecture, and this limitation cannot be overcome with larger or more diverse datasets. The outlined challenges are primarily associated with the lack of electronic structure information in surrogate models such as interatomic potentials. Given the fast development of machine learning and computational chemistry methods, we expect some limitations of surrogate models to be addressed in the near future; nevertheless spatial locality assumption will likely remain a limiting factor for their transferability. Here, we suggest focusing on an equally important effort-design of physics-informed models that leverage the domain knowledge and employ machine learning only as a corrective tool. In the context of material science, we will focus on semi-empirical quantum mechanics, using machine learning to predict corrections to the reduced-order Hamiltonian model parameters. The resulting models are broadly applicable, retain the speed of semiempirical chemistry, and frequently achieve accuracy on par with much more expensive abinitio calculations. These early results indicate that future work, in which machine learning and quantum chemistry methods are developed jointly, may provide the best of all worlds for chemistry applications that demand both high accuracy and high numerical efficiency.

Control optimization for parametric Hamiltonians by pulse reconstruction

Optimal control techniques provide a means to tailor the control pulses required to generate customized quantum gates, which helps to improve the resilience of quantum simulations to gate errors and device noise. However, the significant amount of (classical) computation required to generate customized gates can quickly undermine the effectiveness of this approach, especially when pulse optimization needs to be iterated. We propose a method to reduce the computational time required to generate the control pulse for a Hamiltonian that is parametrically dependent on a time-varying quantity. We use simple interpolation schemes to accurately reconstruct the control pulses from a set of pulses obtained in advance for a discrete set of predetermined parameter values. We obtain a reconstruction with very high fidelity and a significant reduction in computational effort. We report the results of the application of the proposed method to device-level quantum simulations of the unitary (real) time evolution of two interacting neutrons based on superconducting qubits.