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IEEE International Conference on Quantum Computing and Engineering (QCE)

QubiCML: ML-Powered Real-Time Quantum State Discrimination Enabling Mid-Circuit Measurements

Neel R Vora, Yilun Xu, Akel Hashim, Neelay Fruitwala, Nam Hoai Nguyen, Haoran Liao, J. T. Balewski, Abhi Rajagopala, Kasra Nowrouzi, Qing Ji, K. Birgitta Whaley, Irfan A. Siddiqi, Phuc Ngoc Nguyen, Gang Huang

Published Sep 15, 2024• 6 citations
Publisher

Abstract

This work addresses the critical challenge of accurately and swiftly identifying quantum states in superconducting quantum processors, with a particular focus on qubit (two-level) states. It introduces QubiCML, an innovative FPGA-based system designed for in-situ, real-time quantum state discrim-ination, which is essential for mid-circuit measurements and the implementation of advanced error correction techniques. By integrating a multi-layer neural network on the FPGA platform, QubiCML achieves exceptional accuracy and low latency in distinguishing quantum states. Traditional methods of quantum state discrimination rely heavily on offline data processing, which is inadequate due to the brief coherence time of quantum states and significant communication delays. QubiCML overcomes these limitations by utilizing in-situ machine learning for state discrimination, enabling real-time state determination in just 54 ns. It optimizes readout RF pulses and digital local oscillator (DLO) signals, significantly enhancing the fidelity of state discrimination. This capability enables mid-circuit measurements (MCM), which we successfully deployed using the QubiCML system. The system has been rigorously evaluated on in-house superconducting quantum processors, demonstrating an impressive average accuracy of 98.5%. QubiCML's modular architecture supports scalability across various qubits, making it adaptable to different quantum computing setups. Its real-time feedback capabilities are particularly beneficial for efficient quantum algorithm development and optimization. This system represents a groundbreaking advance-ment in the field of quantum computing, offering a robust tool for real-time quantum state discrimination. QubiCML has the potential to become the standard method for state discrimination, providing the quantum computing community with a powerful solution to push the boundaries of current technology.

DOI
10.1109/qce60285.2024.10332

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This work addresses the critical challenge of accurately and swiftly identifying quantum states in superconducting quantum processors, with a particular focus on qubit (two-level) states. It introduces QubiCML, an innovative FPGA-based system designed for in-situ, real-time quantum state discrim-ination, which is essential for mid-circuit measurements and the implementation of advanced error correction techniques. By integrating a multi-layer neural network on the FPGA platform, QubiCML achieves exceptional accuracy and low latency in distinguishing quantum states. Traditional methods of quantum state discrimination rely heavily on offline data processing, which is inadequate due to the brief coherence time of quantum states and significant communication delays. QubiCML overcomes these limitations by utilizing in-situ machine learning for state discrimination, enabling real-time state determination in just 54 ns. It optimizes readout RF pulses and digital local oscillator (DLO) signals, significantly enhancing the fidelity of state discrimination. This capability enables mid-circuit measurements (MCM), which we successfully deployed using the QubiCML system. The system has been rigorously evaluated on in-house superconducting quantum processors, demonstrating an impressive average accuracy of 98.5%. QubiCML's modular architecture supports scalability across various qubits, making it adaptable to different quantum computing setups. Its real-time feedback capabilities are particularly beneficial for efficient quantum algorithm development and optimization. This system represents a groundbreaking advance-ment in the field of quantum computing, offering a robust tool for real-time quantum state discrimination. QubiCML has the potential to become the standard method for state discrimination, providing the quantum computing community with a powerful solution to push the boundaries of current technology.
This work presents QubiCML, a novel FPGA-based system engineered for real-time, in-situ discrimination of two- and three-level quantum states (quitrits) in superconducting quantum processors. Accurate and low-latency identification of quantum states is a critical enabler for scaling quantum computing, particularly as higher-dimensional systems such as qutrits are explored to increase information density, circuit depth, and gate expressivity. QubiCML integrates a lightweight, multi-layer neural network directly into FPGA hardware to classify the qutrit states∣g⟩,∣e⟩\vert g\rangle,\vert e\rangle∣g⟩,∣e⟩, and∣f⟩\vert f\rangle∣f⟩with high fidelity and minimal latency. Traditional state discrimination methods rely on offline post-processing, resulting in delays incompatible with the short coherence times of superconducting qubits. QubiCML overcomes this bottleneck by performing all inference operations within the FPGA fabric itself, achieving full quantum state discrimination within 66 ns (with softmax) or 40 ns (without softmax). The system supports fast reconfiguration via BRAM-stored parameters, enabling compatibility with varying quantum hardware and dynamic calibration environments. Additionally, we propose FPGA-friendly techniques for data normalization and fixed-point quantization to eliminate the overhead of floating-point and division operations—further optimizing the architecture for real-time deployment. We evaluate QubiCML on in-house superconducting hardware across multiple qubits and qutrits. The system achieves up to 97 % average accuracy for qutrits and up to 99 % for two-level systems, using readout windows as short as 650-750 ns. These results establish QubiCML as one of the first practical implementations of real-time qutrit state discrimination, marking an essential step toward mid-circuit measurements, dynamic feedback, and advanced error correction in higher-dimensional quantum computing.
Identifying the state of a quantum bit (qubit), known as quantum state discrimination, is a crucial operation in quantum computing. However, it has been the most error-prone and time-consuming operation on superconducting quantum processors. Due to stringent timing constraints and algorithmic complexity, most qubit state discrimination methods are executed offline. In this work, we present an enhanced real-time quantum state discrimination system leveraging FPGA-based AI Engine technology. A multi-layer neural network has been developed and implemented on the AMD Xilinx VCK190 FPGA platform, enabling accurate in-situ state discrimination and supporting mid-circuit measurement experiments for multiple qubits. Our approach leverages recent advancements in architecture research and design, utilizing specialized AI/ML accelerators to optimize quantum experiments and reduce the use of FPGA resources.
Accurate and timely quantum state discrimination is critical for quantum computing, particularly in protocols requiring mid-circuit measurement (MCM) and conditional feed-forward. While classical state readout in transistors is near-instantaneous, identifying the state of superconducting qubits remains latency-limited and error-prone. Existing approaches rely on post-processing measurement data transferred to host computers, introducing delays on the order of milliseconds -- vastly exceeding the coherence time of quantum states, which typically last only hundreds of microseconds. To bridge this latency gap, we present an in-situ machine learning (ML) inference engine implemented on a field-programmable gate array (FPGA), enabling real-time quantum state discrimination within 40 ns. Our system supports both two-level (qubit) and three-level (qutrit) systems and performs inference directly on digitized readout signals without host-side intervention. This low-latency operation facilitates mid-circuit measurements, where qubit states are measured and reused within a single quantum circuit, and enables real-time conditional operations essential for quantum error correction. Crucially, fast and accurate mid-circuit readout is a prerequisite for implementing practical fault-tolerant error correction, where errors must be detected and corrected within a single circuit cycle to preserve logical state coherence. We validate our system using superconducting transmon devices, demonstrating robust discrimination fidelity across multiple qubit and qutrit channels. Further, we implement a conditional qutrit logic protocol based on FPGA-resident state classification, highlighting the practical benefits of our approach for NISQ-era quantum algorithms and scalable fault-tolerant architectures.
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.
Quantum circuits utilizing real time feedback techniques (such as active reset and mid-circuit measurement) are a powerful tool for NISQ-era quantum computing. Such techniques are crucial for implementing error correction protocols, and can reduce the resource requirements of certain quantum algorithms. Realizing these capabilities requires flexible, low-latency classical control. We have developed a custom FPGA-based processor architecture for QubiC, an open source platform for superconducting qubit control. Our architecture is distributed in nature, and consists of a bank of lightweight cores, each configured to control a small (1-3) number of signal generator channels. Each core is capable of executing parameterized control and readout pulses, as well as performing arbitrary control flow based on mid-circuit measurement results. We have also developed a modular compiler stack and domain-specific intermediate representation for programming the processor. Our representation allows users to specify circuits using both gate and pulse-level abstractions, and includes high-level control flow constructs (e.g. if-else blocks and loops). The compiler stack is designed to integrate with quantum software tools and programming languages, such as TrueQ, pyGSTi, and OpenQASM3. In this work, we will detail the design of both the processor and compiler stack, and demonstrate its capabilities with a quantum state teleportation experiment using transmon qubits at the LBNL Advanced Quantum Testbed.