11 papers
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…
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 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…