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.

Quantum Instruction Set Design for Performance.

A quantum instruction set is where quantum hardware and software meet. We develop characterization and compilation techniques for non-Clifford gates to accurately evaluate its designs. Applying these techniques to our fluxonium processor, we show that replacing the iSWAP gate by its square root SQiSW leads to a significant performance boost at almost no cost. More precisely, on SQiSW we measure a gate fidelity of up to 99.72% and averaging at 99.31%, and realize Haar random two-qubit gates with an average fidelity of 96.38%. This is an average error reduction of 41% for the former and a 50% reduction for the latter compared to using iSWAP on the same processor.

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.

PCR-detectable Candida DNA exists a short period in the blood of systemic candidiasis murine model.

Invasive candidiasis is a major challenge to clinical medicine today. However, traditional fungal diagnostic techniques and empirical treatments have shown great limitations. Although efforts are necessarily needed in methodology standardization and multicenter validation, polymerase chain reaction (PCR) is a very promising assay in detecting fungal pathogens. Using a "heat-shock" DNA preparation method, a rapid and simple PCR protocol for quantification of the Candida albicans (C. albicans) ribosomal DNA was established. The PCR assay could detect Candida DNA as low as 10 CFU/mL in samples prepared by the heat-shock protocol, without any cross-reaction with DNA prepared from other Candida spp. and bacterial pathogens. For simulated blood samples, the PCR test sensitivity of whole blood samples was better than that of plasma and blood cells. In the systemic candidiasis murine model, detectable DNA was only observed within 24 h after C. albicans SC5314 injection, which is much shorter than that observed in the kidney.

Tensor renormalization group with randomized singular value decomposition.

An algorithm of the tensor renormalization group is proposed based on a randomized algorithm for singular value decomposition. Our algorithm is applicable to a broad range of two-dimensional classical models. In the case of a square lattice, its computational complexity and memory usage are proportional to the fifth and the third power of the bond dimension, respectively, whereas those of the conventional implementation are of the sixth and the fourth power. The oversampling parameter larger than the bond dimension is sufficient to reproduce the same result as full singular value decomposition even at the critical point of the two-dimensional Ising model.