Related papers: Tensor-network methodology for super-moir\'e excit…
The momentum-space signatures of excitons can be experimentally accessed through time-resolved (pump-probe) photoelectron spectroscopy. In this work, we develop a computational framework for exciton photoemission orbital tomography (exPOT)…
We resume the recent successes of the grid-based tensor numerical methods and discuss their prospects in real-space electronic structure calculations. These methods, based on the low-rank representation of the multidimensional functions and…
Tensor network methods are a class of numerical tools and algorithms to study many-body quantum systems in and out of equilibrium, based on tailored variational wave functions. They have found significant applications in simulating lattice…
Moir\'e superlattices provide a compelling platform for exploring exotic correlated physics. Electronic interference within these systems often results in flat bands with localized electrons, which are typically described by effective…
Tensor network states provide an efficient class of states that faithfully capture strongly correlated quantum models and systems in classical statistical mechanics. While tensor networks can now be seen as becoming standard tools in the…
Engineering and probing excitonic properties at the nanoscale remains a central challenge in quantum photonics and optoelectronics. While exciton confinement via electrical control and strain engineering has been demonstrated in 2D…
Simulating quantum many-body systems (QMBS) is one of the long-standing, highly non-trivial challenges in condensed matter physics and quantum information due to the exponentially growing size of the system's Hilbert space. To date, tensor…
Non-Hermitian many-body systems can be spectrally unstable, so small perturbations may induce large eigenvalue shifts. The pseudospectrum quantifies this instability and provides a perturbation-robust diagnostic. For inverse-polynomially…
Excitons -- quasiparticles formed by the binding of an electron and a hole through electrostatic attraction -- hold promise in the fields of quantum light confinement and optoelectronic sensing. Atomically thin transition metal…
This work introduces a tensor-based method to perform supervised classification on spatiotemporal data processed in an echo state network. Typically when performing supervised classification tasks on data processed in an echo state network,…
The calculated quasiparticle band structure of bulk hexagonal boron nitride using the all-electron GW approximation shows that this compound is an indirect-band-gap semiconductor. The solution of the Bethe-Salpeter equation for the…
An exciton beam splitter is designed and computationally implemented, offering the prospect of excitonic interferometry. Exciton interaction between propagation conduits is modeled using a coupling parameter that varies with position. In…
We present a new methodology to calculate the strong light-matter coupling between photonic modes in microcavities and large molecular aggregates that consist of hundreds of molecular fragments. To this end, we combine our fragment…
Excitons -- bound electron-hole pairs -- play a central role in light-matter interaction phenomena, and are crucial for wide-ranging applications from light harvesting and generation to quantum information processing. A long-standing…
The performance of tensor network methods has seen constant improvements over the last few years. We add to this effort by introducing a new algorithm that efficiently applies tree tensor network operators to tree tensor network states…
We propose a method for the efficient quantum simulation of fermionic systems with superconducting circuits. It consists in the suitable use of Jordan-Wigner mapping, Trotter decomposition, and multiqubit gates, be with the use of a quantum…
We consider the problem of approximating a subset $M$ of a Hilbert space $X$ by a low-dimensional manifold $M_n$, using samples from $M$. We propose a nonlinear approximation method where $M_n $ is defined as the range of a smooth nonlinear…
Binary Neural Networks (BNNs) enable efficient deep learning by saving on storage and computational costs. However, as the size of neural networks continues to grow, meeting computational requirements remains a challenge. In this work, we…
We propose a method for approximating the contraction of a tensor network by partitioning the network into a sum of computationally cheaper networks. This method, which we call a partitioned network expansion (PNE), builds upon recent work…
We present a tensor-network-based method for simulating a weakly-measured quantum circuit. In particular, we use a Markov chain to efficiently sample measurements and contract the tensor network, propagating their effect forward along the…