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quant-ph2026

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-…

quant-ph2026

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…

quant-ph2026

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…

quant-ph2026

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…

quant-ph2026

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…

quant-ph2026

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…