A U-Net trained on one small quantum dot device predicts electrostatic potentials for devices up to 99 dots, giving a 1000x faster simulator that reproduces real-device tuning features.
RISC-Q: A Generator for Real-Time Quantum Control System-on-Chips Compatible with RISC-V
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abstract
Quantum computing imposes stringent requirements for the precise control of large-scale qubit systems, including, for example, microsecond-latency feedback and nanosecond-precision timing of gigahertz signals -- demands that far exceed the capabilities of conventional real-time systems. The rapidly evolving and highly diverse nature of quantum control necessitates the development of specialized hardware accelerators. While a few custom real-time systems have been developed to meet the tight timing constraints of specific quantum platforms, they face major challenges in scaling and adapting to increasingly complex control demands -- largely due to fragmented toolchains and limited support for design automation. To address these limitations, we present RISC-Q -- an open-source flexible generator for Quantum Control System-on-Chip (QCSoC) designs, featuring a programming interface compatible with the RISC-V ecosystem. Developed using SpinalHDL, RISC-Q enables efficient automation of highly parameterized and modular QCSoC architectures, supporting agile and iterative development to meet the evolving demands of quantum control. We demonstrate that RISC-Q can replicate the performance of existing QCSoCs with significantly reduced development effort, facilitating efficient exploration of the hardware-software co-design space for rapid prototyping and customization.
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NeuroQD: A Learning-Based Simulation Framework For Quantum Dot Devices
A U-Net trained on one small quantum dot device predicts electrostatic potentials for devices up to 99 dots, giving a 1000x faster simulator that reproduces real-device tuning features.