A critical Schr\"odinger cat qubit
Luca Gravina, Fabrizio Minganti, Vincenzo Savona

TL;DR
This paper introduces a critical cat code combining two-photon loss and Kerr nonlinearity with out-of-resonance drive, demonstrating enhanced robustness and phase transition phenomena for scalable quantum error correction.
Contribution
It proposes a novel critical cat code with out-of-resonance drive, analyzing its performance via spectral Liouvillian theory and revealing phase transition effects for improved quantum error correction.
Findings
Large detunings and non-negligible two-photon loss optimize performance.
First-order dissipative phase transition leads to a squeezed vacuum steady state.
Metastable state initialization enhances logical bit-flip suppression.
Abstract
Encoding quantum information onto bosonic systems is a promising route to quantum error correction. In a cat code, this encoding relies on the confinement of the system's dynamics onto the two-dimensional manifold spanned by Schr\"odinger cats of opposite parity. In dissipative cat qubits, an engineered dissipation scheme combining two-photon drive and loss has been used to autonomously stabilize this manifold, ensuring passive protection against bit-flip errors, regardless of their origin. In Kerr cat qubits, where highly-performing gates can be engineered, two-photon drive and Kerr nonlinearity cooperate to confine the system to a two-fold degenerate ground state manifold spanned by cats of opposite parity. Dissipative, Hamiltonian, and hybrid confinements have been investigated at resonance. Here, we propose a critical cat code, where both two-photon loss and Kerr nonlinearity are…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Information and Cryptography · Quantum Computing Algorithms and Architecture · Neural Networks and Reservoir Computing
