This whitepaper serves as an overview of Aquila and its capabilities: how it works under the hood, key performance benchmarks, and examples that demonstrate some quintessential applications. This includes an overview of neutral-atom quantum computing, as well as five examples of increasing complexity from single-qubit dynamics to combinatorial optimization, implemented on Aquila.
Led by the team at BMW, this paper proposes and analyzes the use of Bayesian optimization techniques to design quantum annealing schedules with minimal user and resource requirements. It showcases their scheme with results for two paradigmatic spin models. The results were illustrated with experiments on the Aquila 256-qubit machine on Amazon Braket.
On the topic of mid-circuit measurements, the team at UIUC paired an external constant magnetic field and fluorescence detection, together with the electronic structure of Yb. They manage to isolate a single decay channel for their measurement process, making it a close-to-textbook projective measurement (in contrast with situations where measurement, for example, fully removes the atom, and thus is not projective or reinitializable).
Also related to mid-circuit measurement, this multi-institute collaboration was led by Jeff Thompson at Princeton. In this work, the team not only demonstrate repetitive readout (similarly using a 1S0 – 3P1 transition as the work above), but also further emphasize the importance of using ytterbium’s 3P0 metastable state as a qubit. With this scheme, the team furthermore achieves respectful 0.999 1-qubit and 0.98 2-qubit gate fidelities.
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