From the 1 of 10 linked papers with an AI index.
10 papers
Approximate sampling from decoded quantum interferometry via Markov chain Monte Carlo methods
Elies Gil-Fuster, Matan Ninio, Lennart Bittel +4
The paper investigates whether classical Markov chain Monte Carlo methods, especially block‑Gibbs sampling, can reproduce the optimization performance of decoded quantum interferom…
Mitigating errors in state preparation and measurement with noncomputational states
Conrad J. Haupt, Almudena Carrera Vazquez, Laurin E. Fischer +2
Error mitigation has enabled quantum computing applications with over one hundred qubits and deep circuits. Many error mitigation methods are noise-aware, relying on a faithful cha…
Bowtie VarQTE: A Resource-Efficient Quantum State Preparation Primitive
Marc Drudis, Alberto Baiardi, Mattia Chiurco +3
The preparation of quantum states is a fundamental requirement for many quantum algorithms. A native route to preparing physically structured states is based on short-time simulati…
Efficient Fourier-Based Linear Combination of Unitaries and Applications in Quantum Optimization
Almudena Carrera Vazquez, Daniel J. Egger, Stefan Woerner
We investigate ancilla-free linear combination of unitaries (LCU) as a framework for approximating complex quantum circuits. This is particularly effective for quantum optimization…
The Quest for Quantum Advantage in Combinatorial Optimization: End-to-end Benchmarking of Quantum Solvers vs. Multi-core Classical Solvers
Pranav Chandarana, Alejandro Gomez Cadavid, Enrique Solano +3
We perform an end-to-end benchmark of a hybrid sequential quantum computing (HSQC) solver for higher-order unconstrained binary optimization (HUBO), executed on IBM Heron r3 quantu…
Breaking concentration barriers for quantum extreme learning on digital quantum processors
Timothée Dao, Ege Yilmaz, Ibrahim Shehzad +8
Reservoir computing leverages rich, non-linear dynamics to process temporal data. Quantum variants promise enhanced expressivity from high-dimensional Hilbert spaces, yet their pra…