Native Approach to Controlled-Z Gates in Inductively Coupled Fluxonium Qubits.

The fluxonium qubits have emerged as a promising platform for gate-based quantum information processing. However, their extraordinary protection against charge fluctuations comes at a cost: when coupled capacitively, the qubit-qubit interactions are restricted to XX interactions. Consequently, effective ZZ or XZ interactions are only constructed either by temporarily populating higher-energy states, or by exploiting perturbative effects under microwave driving. Instead, we propose and demonstrate an inductive coupling scheme, which offers a wide selection of native qubit-qubit interactions for fluxonium. In particular, we leverage a built-in, flux-controlled ZZ interaction to perform qubit entanglement. To combat the increased flux-noise-induced dephasing away from the flux-insensitive position, we use a continuous version of the dynamical decoupling scheme to perform noise filtering. Combining these, we demonstrate a 20 ns controlled-z gate with a mean fidelity of 99.53%. More than confirming the efficacy of our gate scheme, this high-fidelity result also reveals a promising but rarely explored parameter space uniquely suitable for gate operations between fluxonium qubits.

Fluxonium: An Alternative Qubit Platform for High-Fidelity Operations.

Superconducting qubits provide a promising path toward building large-scale quantum computers. The simple and robust transmon qubit has been the leading platform, achieving multiple milestones. However, fault-tolerant quantum computing calls for qubit operations at error rates significantly lower than those exhibited in the state of the art. Consequently, alternative superconducting qubits with better error protection have attracted increasing interest. Among them, fluxonium is a particularly promising candidate, featuring large anharmonicity and long coherence times. Here, we engineer a fluxonium-based quantum processor that integrates high qubit coherence, fast frequency tunability, and individual-qubit addressability for reset, readout, and gates. With simple and fast gate schemes, we achieve an average single-qubit gate fidelity of 99.97% and a two-qubit gate fidelity of up to 99.72%. This performance is comparable to the highest values reported in the literature of superconducting circuits. Thus our work, within the realm of superconducting qubits, reveals an alternative qubit platform that is competitive with the transmon system.

Ultrahigh Kinetic Inductance Superconducting Materials from Spinodal Decomposition.

Disordered superconducting nitrides with kinetic inductance have long been considered to be leading material candidates for high-inductance quantum-circuit applications. Despite continuing efforts toward reducing material dimensions to increase the kinetic inductance and the corresponding circuit impedance, achieving further improvements without compromising material quality has become a fundamental challenge. To this end, a method to drastically increase the kinetic inductance of superconducting materials via spinodal decomposition while maintaining a low microwave loss is proposed. Epitaxial Ti0.48 Al0.52 N is used as a model system and the utilization of spinodal decomposition to trigger the insulator-to-superconductor transition with a drastically enhanced material disorder is demonstrated. The measured kinetic inductance increases by two to three orders of magnitude compared with the best disordered superconducting nitrides reported to date. This work paves the way for substantially enhancing and deterministically controlling the inductance for advanced superconducting quantum circuits.

Efficient parallelization of tensor network contraction for simulating quantum computation.

We develop an algorithmic framework for contracting tensor networks and demonstrate its power by classically simulating quantum computation of sizes previously deemed out of reach. Our main contribution, index slicing, is a method that efficiently parallelizes the contraction by breaking it down into much smaller and identically structured subtasks, which can then be executed in parallel without dependencies. We benchmark our algorithm on a class of random quantum circuits, achieving greater than 105 times acceleration over the original estimate of the simulation cost. We then demonstrate applications of the simulation framework for aiding the development of quantum algorithms and quantum error correction. As tensor networks are widely used in computational science, our simulation framework may find further applications.