Multiplexed readout of superconducting qubits using a three-dimensional reentrant-cavity filter
Physical Review Applied American Physical Society (APS) 23:5 (2025) 054089
Complementing the transmon by integrating a geometric shunt inductor
(2024)
Encoding optimization for quantum machine learning demonstrated on a superconducting transmon qutrit
Quantum Science and Technology IOP Publishing 9:4 (2024) 045037
Abstract:
A qutrit represents a three-level quantum system, so that one qutrit can encode more information than a qubit, which corresponds to a two-level quantum system. This work investigates the potential of qutrit circuits in machine learning classification applications. We propose and evaluate different data-encoding schemes for qutrits, and find that the classification accuracy varies significantly depending on the used encoding. We therefore propose a training method for encoding optimization that allows to consistently achieve high classification accuracy, and show that it can also improve the performance within a data re-uploading approach. Our theoretical analysis and numerical simulations indicate that the qutrit classifier can achieve high classification accuracy using fewer components than a comparable qubit system. We showcase the qutrit classification using the encoding optimization method on a superconducting transmon qutrit, demonstrating the practicality of the proposed method on noisy hardware. Our work demonstrates high-precision ternary classification using fewer circuit elements, establishing qutrit quantum circuits as a viable and efficient tool for quantum machine learning applications.Efficient Characterization of Qudit Logical Gates with Gate Set Tomography Using an Error-Free Virtual Z Gate Model.
Physical review letters 133:12 (2024) 120802
Abstract:
Gate set tomography (GST) characterizes the process matrix of quantum logic gates, along with measurement and state preparation errors in quantum processors. GST typically requires extensive data collection and significant computational resources for model estimation. We propose a more efficient GST approach for qudits, utilizing the qudit Hadamard and virtual Z gates to construct fiducials while assuming virtual Z gates are error-free. Our method reduces the computational costs of estimating characterization results, making GST more practical at scale. We experimentally demonstrate the applicability of this approach on a superconducting transmon qutrit.Emulating two qubits with a four-level transmon qudit for variational quantum algorithms
Quantum Science and Technology IOP Publishing 9:3 (2024) 035003