146 citations · 152 across the 14 of their papers we have counts for
14 papers
A priori Assessment of Tensor-Network Encoding for Isotropic Turbulent Flows
Massen Esmaeili, Hirad Alipanah, Robert Pinkston +5
Tensor networks (TNs), originally developed for simulating many-body quantum systems, provide a systematic framework for approximating high-dimensional fields. This is achieved by…
Efficient Treatment of Non-Linearity in Quantum Computational Fluid Dynamics Using Hybrid Tensor Networks
Pia Siegl, Nis-Luca van Hülst, Maximilian Mandelt Buxadé +2
Nonlinear terms present a fundamental challenge for quantum computational fluid dynamics, as their implementation on inherently linear quantum hardware typically requires resource-…
Iterative tensor network transformations for element-wise evaluation of elementary and filtering functions
Xiao Wang, Tomohiro Hashizume, Pia Siegl +1
Tensor networks are powerful formats for compressing large-scale data. However, their application to general data processing has been limited by the difficulty of performing nonlin…
Time evolution of nonlinear dynamics on a quantum processor
José Diogo da Costa Jesus, Abhishek Setty, Tommaso Calarco +3
From fluid flow and transport to collective dynamics, numerical simulation of nonlinear partial differential equations underpins modern scientific computing. Extending this capabil…
How Hard Is Quantum Advantage? A Cloud Microphysics Stress Test for Variational Quantum Models
Felix Herbort, Ellen Sarauer, Daniel Ohl de Mello +7
Quantum machine learning (QML) could have the potential to leverage advantages of quantum over classical computing but still lacks strong evidence of actual improvements and scalab…
Optimizing Symmetry Informed Probabilistic Error Cancellation
Tom O'Leary, Daniel J. Egger, Dieter Jaksch
We show that combining quantum error detection (QED) with probabilistic error cancellation (PEC) gives more accurate and lower-variance estimates than PEC alone, provided that the…
Quantum computation at the edge of chaos
Tomohiro Hashizume, Zhengjun Wang, Frank Schlawin +1
A key challenge in classical machine learning is to mitigate overparameterization by selecting sparse solutions. We translate this concept to the quantum domain, introducing quantu…
Quantum-Inspired Simulation of 2D Turbulent Rayleigh-Bénard Convection
Nis-Luca van Hülst, Mario Guillaume Cecile, Hai-Yen Van +3
Turbulent thermal convection governs heat transport in systems ranging from stellar interiors to industrial heat exchangers. Two-dimensional Rayleigh-Bénard convection serves as a…
Symplectic Optimization on Bosonic Gaussian States
Christopher Willby, Tomohiro Hashizume, Jason Crain +1
Computing bosonic Gaussian ground states via variational optimization is challenging because the covariance matrices must satisfy the uncertainty principle, rendering constrained o…
Resource-Efficient Quantum Optimization via Higher-Order Encoding
Frederik Koch, Shahram Panahiyan, Rick Mukherjee +2
Quantum approaches to combinatorial optimization problems (COPs) are often limited by the resource demands of Quadratic Unconstrained Binary Optimization (QUBO) encodings, which en…
Quantum-Inspired Tensor-Network Fractional-Step Method for Incompressible Flow in Curvilinear Coordinates
Nis-Luca van Hülst, Pia Siegl, Paul Over +5
We introduce an algorithmic framework based on tensor networks for computing fluid flows around immersed objects in curvilinear coordinates. We show that the tensor network simulat…
Dynamical quantum phase transitions on random networks
Tomohiro Hashizume, Felix Herbort, Joseph Tindall +1
We investigate two types of dynamical quantum phase transitions (DQPTs) in the transverse field Ising model on ensembles of Erdős-Rényi networks of size . These networks consist…
Tensor-Programmable Quantum Circuits for Solving Differential Equations
Pia Siegl, Greta Sophie Reese, Tomohiro Hashizume +2
We present a quantum solver for partial differential equations based on a flexible matrix product operator representation. Utilizing mid-circuit measurements and a state-dependent…
Ultracold molecules for quantum simulation: rotational coherences in CaF and RbCs
Jacob A Blackmore, Luke Caldwell, Philip D Gregory +9
We explore the uses of ultracold molecules as a platform for future experiments in the field of quantum simulation, focusing on two molecular species, CaF and …