arXiv subjects

Ashish Joshi

Publications and source records attributed to Ashish Joshi.

5 recordsLinked to original sources

arXiv2026

Quantum MeanFlow: single-shot generative sampling on NISQ hardware

Quantum generative models offer a promising framework for exploring whether quantum computation can enhance generative machine learning. Flow matching is a generative method in which samples are generated by transporting a simple, known distribution to the target data distribution with a learned velocity field. Its quantum counterpart, known as quantum flow matching (QFM), was introduced recently, and, like its classical counterpart, requires integrating an ordinary differential equation over many time steps during inference. As each step requires the output from the previous step, the circuit submission is sequential and a drawback on quantum computers as they have high input/output costs. To alleviate this problem, we introduce Quantum MeanFlow (QMF), the quantum analogue of the MeanFlow formulation, which allows single-step sample generation. While the QFM learns an instantaneous velocity field at each time step, QMF learns the average velocity over a time interval. We use a parameterized quantum circuit to learn these velocity fields and benchmark the two methods on the MNIST dataset. We show that while single-step QMF has lower image quality compared to multi-step QFM, it performs better than the single-step QFM sampling at every shot count. Both of our models are executed on IBM quantum computers and best-of-N rejection sampling recovers most of the accuracy lost to device noise without modifying the circuit. This is especially advantageous for QMF which has only one circuit evaluation per image. Here, We establish QMF as a viable method for single-step quantum generative sampling, saving on quantum circuit evaluations per generated sample.

quant-ph↗

arXiv2024

Quantum skyrmion dynamics studied by neural network quantum states

We study the dynamics of quantum skyrmions under a magnetic field gradient using neural network quantum states. First, we obtain a quantum skyrmion lattice ground state using variational Monte Carlo with a restricted Boltzmann machine as the variational ansatz for a quantum Heisenberg model with Dzyaloshinskii-Moriya interaction. Then, using the time-dependent variational principle, we study the real-time evolution of quantum skyrmions after a Hamiltonian quench with an inhomogeneous external magnetic field. We show that field gradients are an effective way of manipulating and moving quantum skyrmions. Furthermore, we demonstrate that quantum skyrmions can decay when interacting with each other. This work shows that neural network quantum states offer a promising way of studying the real-time evolution of quantum magnetic systems that are outside the realm of exact diagonalization.

cond-mat.dis-nn↗

arXiv2023

Ground State Properties of Quantum Skyrmions described by Neural Network Quantum States

We investigate the ground state properties of quantum skyrmions in a ferromagnet using variational Monte Carlo with the neural network quantum state as variational ansatz. We study the ground states of a two-dimensional quantum Heisenberg model in the presence of the Dzyaloshinskii-Moriya interaction (DMI). We show that the ground state accommodates a quantum skyrmion for a large range of parameters, especially at large DMI. The spins in these quantum skyrmions are weakly entangled, and the entanglement increases with decreasing DMI. We also find that the central spin is completely disentangled from the rest of the lattice, establishing a non-destructive way of detecting this type of skyrmion by local magnetization measurements. While neural networks are well suited to detect weakly entangled skyrmions with large DMI, they struggle to describe skyrmions in the small DMI regime due to nearly degenerate ground states and strong entanglement. In this paper, we propose a method to identify this regime and a technique to alleviate the problem. Finally, we analyze the workings of the neural network and explore its limits by pruning. Our work shows that neural network quantum states can be efficiently used to describe the quantum magnetism of large systems exceeding the size manageable in exact diagonalization by far.

cond-mat.mes-hall↗

arXiv2021

Mott transition and magnetism in a fragile topological insulator

We study the effects of electronic correlations on fragile topology using dynamical mean-field theory. Fragile topological insulators (FTIs) offer obstruction to the formation of exponentially localized Wannier functions, but they can be trivialized by adding certain trivial degrees of freedom. For the same reason, FTIs do not host symmetry-protected flow of edge states between bulk bands in cylindrical boundary conditions but are expected to have a spectral flow between the fragile bands and other bands under certain twisted boundary conditions. We here analyze commonly observed effects of strong correlations, such as the Mott-insulator transition and magnetism, on a known model hosting fragile topology. We show that in the nonmagnetic case, fragile topology, along with the twisted boundary states, is stable with interactions below a critical interaction strength. Above this interaction strength, a transition to the Mott insulating phase occurs, and the twisted boundary states disappear. Furthermore, by applying a homogeneous magnetic field, the fragile topology is destroyed. However, we show that a magnetic field can induce a topological phase transition which converts a fragile topological insulator to a Chern insulator. Finally, we study ferromagnetic solutions of the fragile topological model.

cond-mat.str-el↗

arXiv2019

Microwave derived monoclinic Ba1-xSnxNb2O6 materials as an alternative of ITO

Microwave synthesis was optimized for preparing novel monoclinic Tin-doped Barium Niobate ceramics (Ba1-xSnxNb2O6; x= 0.0, 0.01, 0.05, 0.1, 0.2, 0.3) BSN. The intensity of monoclinic phase formation was observed to decrease on increasing tin as dopant indicating decreased crystallinity. Strained crystalline phase was observed in undoped sample that became severe on doping tin. Monoclinic metal alloy Sn2O3 formation was confirmed on increasing tin doping beyond 5%. Electronic configuration of tin (II) oxide supports the local site-wise monoclinic disorder in crystal structure due to sterically active lone pair. Such a disorder arranges increased number of degenerate energy states and reduce effective energy gap between conduction band minimum and valence band maximum. All BSN compositions were investigated for monoclinic phase stabilization, ultra-violet absorption, dielectric response, raman modes and type of carrier concentration along with hall resistivity. All measurements possessed inflexion corresponding to 5 atomic % tin doping indicating successful site substitution and estimated Sn2O3 metal formation beyond this. The values of reducing optical energy band gap, transparency to visible spectrum and hall resistivity indicated utility of these materials as a substitute transparent conducting oxide (TCO) for well-known indium tin oxide (ITO).

cond-mat.mtrl-sci↗