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Sustainable Implementation of Machine Learning for Particle Accelerators [Slides]

Tia Miceli

Instrumentation Control Kit (QICK). Integrating machine learning methods on QICK opens several pathways for efficient real-time processing of quantum circuits.

Oct 18, 20220 citations

Preliminary Demonstration of Spin Qubit Control Using the Quantum Instrumentation Control Kit (QICK)

Dana El Hajj, Vivien Schmitt, X. Jehl +4 more

Communications in computer and information scienceJan 1, 20260 citations

HLS4ML Integration with QICK

Mohamud Ali

The QICK (Quantum Instrumentation Control Kit) integration aims to enhance the readout of superconducting qubits by leveraging machine learning (ML) techniques. These techniques offer high accuracy, increased speed, and better state preservation for qubit readouts. The integration process employs neural network algorithms to optimize system performance and achieve high accuracy rates. The QICK board uses an FPGA, allowing for efficient real-time processing and high-performance execution of machine learning algorithms.The QICK (Quantum Instrumentation Control Kit) integration aims to enhance the readout of superconducting qubits by leveraging machine learning (ML) techniques. These techniques offer high accuracy, increased speed, and better state preservation for qubit readouts. The integration process employs neural network algorithms to optimize system performance and achieve high accuracy rates. The QICK board uses an FPGA, allowing for efficient real-time processing and high-performance execution of machine learning algorithms.

Aug 15, 20240 citations

Machine-learning tools for rapid control, calibration and characterization of QPUs and other quantum devices

Nicolas Wittler, Federico Roy, Kevin Pack +6 more

Bulletin of the American Physical SocietyMar 19, 20210 citations

End-to-End Workflow for Machine-Learning-Based Qubit Readout With QICK and hls4ml

Giuseppe Di Guglielmo, Botao Du, Javier Campos +10 more

We present an end-to-end workflow for superconducting qubit readout that embeds co-designed Neural Networks (NNs) into the Quantum Instrumentation Control Kit (QICK). Capitalizing on the custom firmware and software of the QICK platform, which is built on Xilinx RFSoC FPGAs, we aim to leverage machine learning (ML) to address critical challenges in qubit readout accuracy and scalability. The workflow utilizes thehls4mlpackage and employs quantization-aware training to translate ML models into hardware-efficient FPGA implementations via user-friendly Python APIs. We experimentally demonstrate the design, optimization, and integration of an ML algorithm for single transmon qubit readout, achieving 96% single-shot fidelity with a latency of 32.25 ns and less than 16% FPGA look-up table resource utilization. Our results offer the community an accessible workflow to advance ML-driven readout and adaptive control in quantum information processing applications.We present an end-to-end workflow for superconducting qubit readout that embeds co-designed Neural Networks (NNs) into the Quantum Instrumentation Control Kit (QICK). Capitalizing on the custom firmware and software of the QICK platform, which is built on Xilinx RFSoC FPGAs, we aim to leverage machine learning (ML) to address critical challenges in qubit readout accuracy and scalability. The workflow utilizes thehls4mlpackage and employs quantization-aware training to translate ML models into hardware-efficient FPGA implementations via user-friendly Python APIs. We experimentally demonstrate the design, optimization, and integration of an ML algorithm for single transmon qubit readout, achieving 96% single-shot fidelity with a latency of 32.25 ns and less than 16% FPGA look-up table resource utilization. Our results offer the community an accessible workflow to advance ML-driven readout and adaptive control in quantum information processing applications.

arXiv:2501.14663IEEE Transactions on Quantum EngineeringJan 1, 202511 citations

Experimental advances with the QICK (Quantum Instrumentation Control Kit) for superconducting quantum hardware

Chunyang Ding, Martın Di Federico, Michael Hatridge +14 more

The Quantum Instrumentation Control Kit (QICK) is a standalone open-source qubit controller that was first introduced in 2022. In this follow-up work, we present recent upgrades to the QICK and the experimental use cases they uniquely enabled for superconducting qubit systems. These include multiplexed signal generation and readout, mixer-free readout, predistorted fast flux pulses, and phase-coherent pulses for parametric operations, including high-fidelity parametric entangling gates. We explain in detail how the QICK was used to enable these experiments. Published by the American Physical Society 2024The Quantum Instrumentation Control Kit (QICK) is a standalone open-source qubit controller that was first introduced in 2022. In this follow-up work, we present recent upgrades to the QICK and the experimental use cases they uniquely enabled for superconducting qubit systems. These include multiplexed signal generation and readout, mixer-free readout, predistorted fast flux pulses, and phase-coherent pulses for parametric operations, including high-fidelity parametric entangling gates. We explain in detail how the QICK was used to enable these experiments. Published by the American Physical Society 2024

arXiv:2311.17171Physical Review ResearchMar 20, 202423 citations

Novel Fast Qubit Readout Approaches Enabled By Qubit Cloaking

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Novel Fast Qubit Readout Approaches Enabled By Qubit Cloaking

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