Abstracts

Both adiabatic quantum computing / quantum annealing and the quantum approximate optimization algorithm combine a problem Hamiltonian with a non-commuting driver Hamiltonian in order to efficiently explore the complete state space of an optimization problem. We develop a representation of such algorithms as a real-time path integral that directly and rigorously implements the otherwise colloquial idea that quantum algorithms follow all possible computations at the same time [...]

A high density of modes can be produced using metamaterial resonant structures made from arrays of lumped circuit elements, to which a flux-tunable transmon qubit can be coupled. For such a system, we have measured the coupling strength of the qubit to multiple modes by tuning the flux and observing the splitting in the transmission of each mode. [...]

Non-stoquastic interactions are hard to realize in experimental setups using superconducting qubits. On the other hand they are important or even necessary for the construction of adiabatic quantum computers wich show a real quantum speedup. [...]

Last year. we proposed an efficient gap-independent cooling scheme for a quantum annealer that benefits from finite temperatures. [...]

The practical application of optimal control techniques, especially in superconducting settings, requires extensive calibration to reach high fidelities. By controlling both experiment and a high performance numerical simulation, the C3 procedure provides a framework to systematically design and apply even intricate open loop optimal control pulses.We demonstrate the characterization and tune-up of a quantum computing device with a few qubit, as well as a quantum memory device composed of a transmon with a 3D microwave cavity [...]

As the era of NISQ is dawning, calibration and characterization of QPUs is becoming an increasingly complex task due to the growing amount of qubits and high fidelity requirements. To tackle this problem, we developed a machine learning driven approach for Combined Calibration and Characterization procedure, C^3. [...]

The current methodology of designing control pulses for superconducting circuits often results in an absurd situation: simplified analytic models which do not predict gate fidelities to high accuracy, and calibrated pulse shapes which achieve good fidelities, but do not correspond to the model. For large number of qubits the QPU calibration is long and difficult, and determining a detailed error budget is nearly impossible.To rectify the situation, we have implemented a novel procedure of Combined Calibration and Characterization (C3): An interative combination of open-loop pulse optimization, model-free tune-up and iterative model fitting and refinement, utilizing a high-performance TensorFlow simulator [...]

relies on a large number of tailored routines to extract individual model parameters (characterization), and the huge effort required inevitably this leads to incomplete characterization, partial insight into the sources of error, and threfore slow progress in improving gate fidelities.To rectify the situation, we provide a new integrated open-source tool-set for Control, Calibration and Characterization (C3) [1]. We present a methodology to combine these tools to find a quantitatively accurate system model, high-fidelity gates and an approximate error budget.In this talk I shall present the overall concept, and insights into future directions. [...]

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