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Bulletin of the American Physical Society

Tunable, Flexible and Efficient Optimization of Control Pulses for Superconducting Qubits, part II - Applications

Elie Assémat, Shai Machnes, David J. Tannor, Frank K. Wilhelm

Published Mar 15, 2017• 0 citations
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This paper presents the design and test results of a CMOS current-based Digital to Analog Converter (DAC) that operates at cryogenic temperatures and that can be used to precisely control the amount of flux coupled to qubits or that can be used in the readout of superconducting circuits. The current pulse output can be controlled in terms of its amplitude, rise and fall slopes, via digital controls, and pulse width, via external triggers, while driving a superconducting circuit. The design has been implemented in a planar 90 nm CMOS process and test results closely match circuit predictions. The solution's wide degree of digital tunability affords potential application of the same device to many types of quantum circuits, beyond those discussed here. Due to the wide-ranging flexibility of digital CMOS control, we envision that this DAC design will enable the next generation of high-fidelity cryo-CMOS control architectures for superconducting qubits.
One significant advantage of superconducting processors is their extensive design flexibility, which encompasses various types of qubits and interactions. Given the large number of tunable parameters of a processor, the ability to perform gradient optimization would be highly beneficial. Efficient backpropagation for gradient computation requires a tightly integrated software library, for which no open-source implementation is currently available. In this work, we introduce SuperGrad, a simulator that accelerates the design of superconducting quantum processors by incorporating gradient computation capabilities. SuperGrad offers a user-friendly interface for constructing Hamiltonians and computing both static and dynamic properties of composite systems. This differentiable simulation is valuable for a range of applications, including optimal control, design optimization, and experimental data fitting. In this paper, we demonstrate these applications through examples and code snippets.