Abstract In this proceedings we present Qibocal , an open-source software package for calibration and characterization of quantum processing units (QPUs) based on the Qibo framework. Qibocal is specifically designed for self-hosted QPUs and provides the groundwork to easily develop, deploy and distribute characterization and calibration routines for all levels of hardware abstraction. Qibocal is based on a modular QPU platform agnostic approach and it provides a general purpose toolkit for superconducting qubits with the possibility of extensions to other quantum technologies. After motivating the need for such a module, we explain the program’s flow and show examples of actual use for QPU calibration. We also showcase additional features provided by the library including automatic report generation and live plotting.
Abstract In this proceedings we present Qibocal , an open-source software package for calibration and characterization of quantum processing units (QPUs) based on the Qibo framework. Qibocal is specifically designed for self-hosted QPUs and provides the groundwork to easily develop, deploy and distribute characterization and calibration routines for all levels of hardware abstraction. Qibocal is based on a modular QPU platform agnostic approach and it provides a general purpose toolkit for superconducting qubits with the possibility of extensions to other quantum technologies. After motivating the need for such a module, we explain the program’s flow and show examples of actual use for QPU calibration. We also showcase additional features provided by the library including automatic report generation and live plotting.
We present Qibolab , an open-source software library for quantum hardware control integrated with the Qibo quantum computing middleware framework. Qibolab provides the software layer required to automatically execute circuit-based algorithms on custom self-hosted quantum hardware platforms. We introduce a set of objects designed to provide programmatic access to quantum control through pulses-oriented drivers for instruments, transpilers and optimization algorithms. Qibolab enables experimentalists and developers to delegate all complex aspects of hardware implementation to the library so they can standardize the deployment of quantum computing algorithms in a extensible hardware-agnostic way, using superconducting qubits as the first officially supported quantum technology. We first describe the status of all components of the library, then we show examples of control setup for superconducting qubits platforms. Finally, we present successful application results related to circuit-based algorithms.
The development of quantum computers needs reliable quantum hardware and tailored software for controlling electronics specific to various quantum platforms. Middleware is a type of computer software program that aims to provide standardized software tools across the entire pipeline, from high level execution of quantum computing algorithms to low level driver instructions tailored to specific experimental setups, including instruments. This paper presents updates to Qibolab, a software library that leverages Qibo capabilities to execute quantum algorithms on self hosted quantum hardware platforms. Qibolab offers an application programming interface (API) for instrument control through arbitrary pulses and driver operations including sweepers. This paper offers an overview of the new features implemented in Qibolab, including the redefined boundaries between platform and channel classes, the integration of an emulator for simulating quantum hardware behaviour, and it shows updated execution times benchmarks for superconducting single qubit calibration routines.
Abstract We present Qibo , a new open-source software for fast evaluation of quantum circuits and adiabatic evolution which takes full advantage of hardware accelerators. The growing interest in quantum computing and the recent developments of quantum hardware devices motivates the development of new advanced computational tools focused on performance and usage simplicity. In this work we introduce a new quantum simulation framework that enables developers to delegate all complicated aspects of hardware or platform implementation to the library so they can focus on the problem and quantum algorithms at hand. This software is designed from scratch with simulation performance, code simplicity and user friendly interface as target goals. It takes advantage of hardware acceleration such as multi-threading Central Processing Unit (CPU), single Graphics Processing Unit (GPU) and multi-GPU devices.
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.
<!--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.
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.