Solving Atomic Wave Functions Using Artificial Neural Networks

Carleo and Troyer [3] have recently pointed out the possibility of solving quantum many-body problems by using Artificial Neural Networks (ANN). Their work is based on minimizing a variational wave function to obtain the ground states for various spin-dependent systems. This work is primarily focused on developing efficient method using ANN to solve the ground state wave function for atomic systems. We have developed a theoretical groundwork to represent the wave function of a many-electron atom by using artificial neural network while still preserving its antisymmetric property. By using the Metropolis algorithm, Variational Monte Carlo (VMC), and Stochastic Reconfiguration (SR) methods for minimization, we were able to obtain a highly accurate ground state wave function for the He atom. To verify our optimization algorithm, we reproduced the results for the ground state of a three dimensional Simple Harmonic Oscillator (SHO) given by Teng [18]

Solving Atomic Wave Functions Using Artificial Neural Networks

Carleo and Troyer [3] have recently pointed out the possibility of solving quantum many-body problems by using Artificial Neural Networks (ANN). Their work is based on minimizing a variational wave function to obtain the ground states for various spin-dependent systems. This work is primarily focused on developing efficient method using ANN to solve the ground state wave function for atomic systems. We have developed a theoretical groundwork to represent the wave function of a many-electron atom by using artificial neural network while still preserving its antisymmetric property. By using the Metropolis algorithm, Variational Monte Carlo (VMC), and Stochastic Reconfiguration (SR) methods for minimization, we were able to obtain a highly accurate ground state wave function for the He atom. To verify our optimization algorithm, we reproduced the results for the ground state of a three dimensional Simple Harmonic Oscillator (SHO) given by Teng [18]

Adaptive variational preparation of the Fermi-Hubbard eigenstates

Approximating the ground states of strongly interacting electron systems in quantum chemistry and condensed matter physics is expected to be one of the earliest applications of quantum computers. In this paper, we prepare highly accurate ground states of the Fermi-Hubbard model for small grids up to 6 sites (12 qubits) by using an interpretable, adaptive variational quantum eigensolver(VQE) called ADAPT-VQE. In contrast with non-adaptive VQE, this algorithm builds a system-specific ansatz by adding an optimal gate built from one-body or two-body fermionic operators at each step. We show this adaptive method outperforms the non-adaptive counterpart in terms of fewer variational parameters, short gate depth, and scaling with the system size. The fidelity and energy of the prepared state appear to improve asymptotically with ansatz depth. We also demonstrate the application of adaptive variational methods by preparing excited states and Green functions using a proposed ADAPT-SSVQE algorithm. Lower depth, asymptotic convergence, noise tolerance of a variational approach, and a highly controllable, system-specific ansatz make the adaptive variational methods particularly well-suited for NISQ devices

Revealing microcanonical phase diagrams of strongly correlated systems via time-averaged classical shadows

Quantum computers and simulators promise to enable the study of strongly correlated quantum systems. Yet surprisingly, it is hard for them to compute ground states[Kitaev]. They can, however, efficiently compute the dynamics of closed quantum systems. We introduce time-average classical shadows(TACS) and machine learning(ML) methods to take advantage of this efficiency to study microcanonical quantum thermodynamics. Using the one-dimensional transverse field Ising model(1DTFIM), we first validate the unsupervised ML method of diffusion maps on classical shadows data from ground state calculations on 100 qubit systems. We then show that diffusion maps applied to TACS data from quantum dynamics simulations beginning from cat states show distinct phase-defining features and correctly identifies the quantum phase transition. Finally, we introduce a Bayesian inference method to compute the second Renyi entropy, a stand in for entropy, the primary thermodynamic potential of microcanonical ensemble. Our results provide evidence that quantum simulators and computers capable of outperforming classical computers at dynamics simulations can also produce quantum thermodynamic data with quantum advantage

Demographic and Clinical Profile in Patients with Liver Cirrhosis in a Tertiary Care Hospital in Central Nepal

Introduction: Liver cirrhosis is an important health problem worldwide and is a common disease in Nepal. The profile of cirrhosis may vary due to different factors. This study was undertaken to see the demographic and clinical profiles of patients with cirrhosis of liver attending a tertiary care hospital in Central Nepal. Methods: Six hundred patients with clinical features, laboratory and sonological findings suggestive of chronic liver dysfunction and endoscopic evidence of portal hypertension were included in the study. Their demographic and clinical profile, endoscopic findings, outcomes during hospitalization were studied. Ethical approval was taken from Institutional Review Committee of College of Medical Sciences. SPSS 20 was used for statistical analysis. Results: The mean age of subjects was 54±11.84 years with 435 males (72.5%) and 165 (27.5%) females. Majority of 203 (33.8%) patients were from Mongol ethnicity followed by 127 (21.2%) Khas. Two hundred and twenty (36.6%) were farmers followed by 169 (28.2%) retired personnel. A total of 338 (56.4 %) patients were from rural areas. The commonest aetiology of cirrhosis was chronic alcohol consumption and seen in 552 (92%) patients. Abdominal distension was commonest presenting sign and observed in 561 (93.5%) patients. Ascites seen in 555 (92.5%) patients was the commonest complication followed by UGI bleed in 326(54.3%) patients. Gastro-oesophageal varices observed in 345 (57.5%) patients, was the most common endoscopic finding followed by portal gastropathy, peptic ulcer and erosive mucosal diseases. In patient mortality was noted in 92 (15.3 %) patients. Conclusions: This study highlights the burden of cirrhosis, usually caused by chronic alcohol consumption in Central Nepal. Majority of subjects were male, middle aged, farmers, from rural areas and predominantly observed in some ethnicity like Mongols. Cirrhotic patients usually present late with varied complications and have high mortality.   Keywords: cirrhosis of liver; complications; endoscopy

Schizencephaly diagnosed after an episode of seizure during labor: A case report

Abstract Schizencephaly, an extremely rare anomaly of the cortex, is characterized by abnormal clefts in the cerebral cortex. Very often, this condition is diagnosed early in the childhood period but few instances exist in literature where schizencephaly‐associated seizures and hemiparesis have presented later in life too. Here, we report a rare case scenario of a lady in her late 30s who initially presented to us with obstetric concerns wherein schizencephaly remained an incidental finding despite the significantly large cortical cleft along with lobar holoprosencephaly and lissencephaly

Coarse-Grained Simulations of Aqueous Thermoresponsive Polyethers

Thermoresponsive polymers can change structure or solubility as a function of temperature. Block co-polymers of polyethers have a response that depends on polymer molecular weight and co-polymer composition. A coarse-grained model for aqueous polyethers is developed and applied to polyethylene oxide and polyethylene oxide-polypropylene oxide-polyethylene oxide triblock co-polymers. In this model, no interaction sites on hydrogen atoms are included, no Coulombic interactions are present, and all interactions are short-ranged, treated with a combination of two- and three-body terms. Our simulations find that The triblock co-polymers tend to associate at temperatures above 350 K. The aggregation is stabilized by contact between The hydrophobic methyl groups on The propylene oxide monomers and involves a large, favorable change in entropy