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20242026
most citedFrozonium: Freezing Anharmonicity in Floquet Superconducting Circuits

3 citations · 3 across the 3 of their papers we have counts for

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quant-ph2025

A Mathematical Structure for Amplitude-Mixing Error-Transparent Gates for Binomial Codes

Owen C. Wetherbee, Saswata Roy, Baptiste Royer +1

Bosonic encodings of quantum information offer hardware-efficient, noise-biased approaches to quantum error correction relative to qubit register encodings. Implementations have fo…

quant-ph2025

Full characterization of measurement-induced transitions of a superconducting qubit

Thomas Connolly, Pavel D. Kurilovich, Vladislav D. Kurilovich +12

Repeated quantum non-demolition measurement is a cornerstone of quantum error correction protocols. In superconducting qubits, the speed of dispersive state readout can be enhanced…

quant-ph2025

Kramers-protected hardware-efficient error correction with Andreev spin qubits

Haoran Lu, Isidora Araya Day, Anton R. Akhmerov +2

We propose an architecture for bit-flip error correction of Andreev spins that is protected by Kramers' degeneracy. Specifically, we show that a coupling network of linear inductor…

quant-ph2025

Synthetic high angular momentum spin dynamics in a microwave oscillator

Saswata Roy, Alen Senanian, Christopher S. Wang +10

Spins and oscillators are foundational to much of physics and applied sciences. For quantum information, a spin 1/2 exemplifies the most basic unit, a qubit. High angular momentum…

quant-ph2025

High-frequency readout free from transmon multi-excitation resonances

Pavel D. Kurilovich, Thomas Connolly, Charlotte G. L. Bøttcher +12

Quantum computation will rely on quantum error correction to counteract decoherence. Successfully implementing an error correction protocol requires the fidelity of qubit operation…

quant-ph2024

Microwave signal processing using an analog quantum reservoir computer

Alen Senanian, Sridhar Prabhu, Vladimir Kremenetski +8

Quantum reservoir computing (QRC) has been proposed as a paradigm for performing machine learning with quantum processors where the training is efficient in the number of required…