Quantum processing units (QPUs) require precise tuning and continuous monitoring systems to be in a functional state, including stable frequency calibration and low-latency control respecting hardware timing constraints. Such systems, however, are complex, making QPUs less appealing and out of reach for many researchers and those without specialized RF/FPGA expertise. We give a brief review of Ramsey spectroscopy, the methodology used for sensitive qubit monitoring, and use QubiC 2.0’s clock-accurate simulation architecture to bridge this gap by validating pulse programs and parameter sweeps in silico before hardware bring-up. This establishes the cornerstone for future work involving hardware-in-the-loop applications where the QPU environment is simulated to work in conjunction with a high-speed DAC/ADC RFSoC FPGA setup, enabling prototyping and real-time calibration while preserving reproducibility.
Quantum processing units (QPUs) require precise tuning and continuous monitoring systems to be in a functional state, including stable frequency calibration and low-latency control respecting hardware timing constraints. Such systems, however, are complex, making QPUs less appealing and out of reach for many researchers and those without specialized RF/FPGA expertise. We give a brief review of Ramsey spectroscopy, the methodology used for sensitive qubit monitoring, and use QubiC 2.0’s clock-accurate simulation architecture to bridge this gap by validating pulse programs and parameter sweeps in silico before hardware bring-up. This establishes the cornerstone for future work involving hardware-in-the-loop applications where the QPU environment is simulated to work in conjunction with a high-speed DAC/ADC RFSoC FPGA setup, enabling prototyping and real-time calibration while preserving reproducibility.
Researchers manipulate and measure quantum processing units via the classical electronics control system. We developed an open-source FPGA-based quantum bit control system called QubiC for superconducting qubits. After years of qubit calibration and testing experience on QubiC 1.0, we recognized the need for mid-circuit measurement and feed-forward capabilities to implement advanced quantum algorithms, and upgraded the system to QubiC 2.0 on an AMD ZCU216 RFSoC evaluation board with all these enriched features. The system uses portable FPGA gateware with a simplified processor to handle commands on-the-fly. For design simplicity and straightforward scaling, we adopted a multi-core distributed architecture, assigning one processor core per qubit. The pulses combine the unique pulse envelope and carrier information specified in a command. Each pulse envelope is pre-stored in the FPGA's block RAMs, ensuring speed and reusability throughout the quantum circuit, while pulse parameters including amplitude, phase, and frequency can be updated from pulse to pulse. The software stack is developed in Python, running on both the FPGA's ARM core and a host computer via XML-RPC. Quantum circuits can be described in a high-level language supporting both pulse-level and native-gate-level programming with high-level control flow constructs, and are compiled into binary commands loaded into the FPGA. With QubiC 2.0, we achieved multi-FPGA synchronization and low-latency data communication, and demonstrated feed-forward experiments on conditional circuits. The system also serves as a platform for artificial intelligence and machine learning driven quantum control, such as FPGA-based real-time quantum state discrimination. The enhanced QubiC system provides researchers with powerful tools to explore and implement advanced quantum algorithms and applications.
Manipulating qubits and measuring quantum processors via quantum bit control systems is traditionally done using expensive, off-the-shelf commercial electronics setups. Previously, we have introduced QubiC, an open-source FPGA-based quantum bit control system developed at Lawrence Berkeley National Laboratory. After a few years of operating on the QubiC 1.0, we realized the urgent requirements on the mid-circuits measurement and feed forward to implement advanced quantum algorithms. This requirement led to engineering a novel QubiC 2.0 on top of the RFSoC technique, incorporating enriched features. The QubiC 2.0. presented in this manuscript is implemented on the Xilinx evaluation board ZCU216. We developed a portable FPGA gateware with a lean soft processor to handle the quantum commands on the fly. Further, we created a distributed architecture with multiple processors, with a one-to-one mapping between processors and qubits, creating a parallel qubit control and measurement. Like a regular CPU, these processors access pulse envelope commands with parameterized amplitude, phase, and frequency to generate pulse information on the FPGA to ensure the speed and reusability of the entire quantum circuit. The Python software layer abstracts the low-level gateware and provides the compiler, assembler, and remote procedure call for host-to-board communication. The compiled software runs on a Python runtime environment, interfacing with the low-level gateware and hardware connected to a Quantum Processing Unit, creating a full-stack quantum bit control system. We demonstrate the architecture and the broad capabilities of QubiC 2.0 with a simplified feed forward experiment on a conditional circuit.
Advanced quantum physics experiments place strict demands on control electronics, including phase-coherent multichannel RF/microwave generation, precise timing synchronization, low-latency response, and simultaneous support for wideband and near-DC signal processing. Existing control platforms often face trade-offs between channel count, integration level, and real-time performance, limiting scalability and experimental flexibility. In this work, we present a highly integrated real-time control system based on an XCZU49DR RFSoC. The platform integrates up to 16 RF DAC channels (9.85 GSa/s) and 16 RF ADC channels (2.5GSa/s) on a single chip, enabling synchronous multichannel signal generation and acquisition with deterministic timing. A custom RF front-end supports both broadband RF operation and low-frequency signal conditioning, while on-chip digital up/down conversion and programmable logic enable flexible waveform synthesis and real-time data processing. Performance characterization shows clean RF and microwave generation with spurious components suppressed below −59.0dBm at 200MHz and below −62.6dBm at 2.9GHz, while phase noise reaches −120.0dBc/Hz and −96.9dBc/Hz at 1kHz offset for 200MHz and 2.9GHz carriers, respectively, confirming low-noise and phase-coherent operation over a wide frequency range. The proposed platform provides a compact, scalable, and reconfigurable solution for quantum control and precision measurement. It is currently being applied to experiments involving NV centers and trapped ions, and can be readily adapted to other quantum systems, offering a practical foundation for next-generation
We present a control and measurement setup for superconducting qubits based on the Xilinx 16-channel radio-frequency system-on-chip (RFSoC) device. The proposed setup consists of four parts: multiple RFSoC boards, a setup to synchronize every digital to analog converter (DAC) and analog to digital converter (ADC) channel across multiple boards, a low-noise direct current supply for tuning the qubit frequency, and cloud access for remotely performing experiments. We also designed the setup to be free of physical mixers. The RFSoC boards directly generate microwave pulses using sixteen DAC channels up to the third Nyquist zone, which are directly sampled by its eight ADC channels between the fifth and the ninth zones.
<!--HTML-->The control of superconducting qubits, central to quantum computing, demands precise manipulation of fast microwave pulses. FPGAs offer ideal versatility for this task. However, due to FPGA complexity, institutions often opt for costly, pre-made solutions, limiting customization. We therefore presented Qibosoq, an open-source software package designed for radio frequency system on chip (RFSoC) platforms. Qibosoq bridges the RFSoC firmware provided by QICK, a Quantum Instrumentation Control Kit, with Qibo, an open-source quantum computing framework. By using RFSoC boards, it is possible to significantly lower the cost of a qubit-control hardware, while also maintaining high development flexibility. We present the Qibosoq software package, as well as the result of testing it with multiple qubits both as a characterization platform and as an algorithm executor, where we demonstrated the capability of the RFSoC board to accurately perform Quantum Machine Learning applications.
Abstract We present Qibosoq , an open-source server-side software package designed for radio frequency system on chip (RFSoC) for executing arbitrary pulse sequences and algorithms on self-hosted quantum processing units using only open-source software. Qibosoq connects the RFSoC firmware provided by Qick , a Quantum Instrumentation Control Kit, with Qibo , a quantum computing middleware framework that enables both experimental and gate-based applications. It simplifies the work of experimentalists and developers by managing client-server communication protocols, implementing tests, and validation procedures, thereby reducing the complexity of experimental setups. The client-side integration is achieved with dedicated drivers implemented in Qibolab , the specialized software module of Qibo for quantum hardware control. Therefore, this setup provides a seamless mechanism to deploy circuit-based algorithms on custom self-hosted quantum hardware platforms controlled by RFSoC electronics.