3 papers
quant-ph2026
Provable and scalable quantum Gaussian processes for quantum learning
Jonas Jäger, Paolo Braccia, Pablo Bermejo +3
Despite rapid recent advances in quantum machine learning, the field is in many ways stuck. Existing approaches can exhibit serious limitations, and we still lack learning framewor…
quant-ph2026
Near-Term Fermionic Simulation with Subspace Noise Tailored Quantum Error Mitigation
Miha PapiÄ, Manuel G. Algaba, Emiliano Godinez-Ramirez +4
Quantum error mitigation (QEM) has emerged as a powerful tool for the extraction of useful quantum information from quantum devices. Here, we introduce the Subspace Noise Tailoring…
quant-ph2025
Fermion-to-qubit encodings with arbitrary code distance
Manuel G. Algaba, Miha PapiÄ, Inés de Vega +2
We introduce a framework which allows to systematically and arbitrarily scale the code distance of local fermion-to-qubit encodings in one and two dimensions without growing the we…