Author

J. Eisert

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Preprint Sep 2026

Fermionic quantum error correction is never free

Fermionic platforms offer compelling architectures for quantum computing, ranging from topologically protected Majorana-based qubits to fermionic cold atoms. To achieve scalability, however, they require quantum error correction. In this work, we prove that any exact and sufficiently accurate approximate fermionic quan...

Yi-Fan Tang, I. Roth, P. Faist et al. · 1 citation

#machine learning Oct 2024

Interactive proofs for verifying (quantum) learning and testing

It is proved that a resource-constrained learner cannot gain any advantage through classical interaction with an untrusted prover, and it is shown that for the vast majority of testing and learning problems, a memory-constrained quantum algorithm cannot overcome its limitations via classical communication with a memory...

Matthias C. Caro, J. Eisert, M. Hinsche et al. · arXiv.org · 4 citations