Scaling of superconducting-based quantum computers has been possible since the invention of transmon qubits due to their low charge noise sensitivity. However, pursuing fault-tolerant and computationally powerful quantum processors may require incorporating more qubits, which may amplify the complexity of the large design space exploration and simulation. Inspired by the recent CMOS Machine Learning assisted electronic design automation to solve similar issues, we claim this work is the first to demonstrate applying machine learning in superconducting circuit design. To start, we attempt to predict the characteristics of individual transmon qubits with a machine learning-based approach based on the simulation data collected with relevant software frameworks like Qiskit Metal and ANSYS Electronics. Our approach aims to perform forward modeling to predict the qubit engineering parameters such as frequency. On the other hand, the machine learning-based model can also infer, through inverse modeling, the candidate sets of design space parameters like the device dimensions satisfying a particular specification while obeying the geometrical constraint of the transmon types. This work paves an alternative way for future applications in designing and automating superconducting quantum computing circuits.
Scaling of superconducting-based quantum computers has been possible since the invention of transmon qubits due to their low charge noise sensitivity. However, pursuing fault-tolerant and computationally powerful quantum processors may require incorporating more qubits, which may amplify the complexity of the large design space exploration and simulation. Inspired by the recent CMOS Machine Learning assisted electronic design automation to solve similar issues, we claim this work is the first to demonstrate applying machine learning in superconducting circuit design. To start, we attempt to predict the characteristics of individual transmon qubits with a machine learning-based approach based on the simulation data collected with relevant software frameworks like Qiskit Metal and ANSYS Electronics. Our approach aims to perform forward modeling to predict the qubit engineering parameters such as frequency. On the other hand, the machine learning-based model can also infer, through inverse modeling, the candidate sets of design space parameters like the device dimensions satisfying a particular specification while obeying the geometrical constraint of the transmon types. This work paves an alternative way for future applications in designing and automating superconducting quantum computing circuits.
Machine learning (ML) is a promising approach for performing challenging quantum-information tasks such as device characterization, calibration, and control.ML models can train directly on the data produced by a quantum device while remaining agnostic to the quantum nature of the learning task.However, these generic models lack physical interpretability and usually require large datasets in order to learn accurately.Here we incorporate features of quantum mechanics in the design of our ML approach to characterize the dynamics of a quantum device and learn device parameters.This physics-inspired approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data obtained from continuous weak measurement of a driven superconducting transmon qubit.This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task, thus laying the groundwork for more scalable characterization techniques.
Designing a superconducting qubit to realize specific Hamiltonian parameters typically requires iterating through a time and compute-intensive forward loop in which the designer chooses a layout geometry, simulates it, extracts circuit parameters such as capacitances, and refines the geometry. We study the inverse version of this task using a neural-network workflow that maps target Hamiltonian parameters directly to component-level layout parameters, which we subsequently demonstrate on a planar transmon layout. During training, we pair the inverse model with a frozen forward surrogate model and evaluate the loss in Hamiltonian space rather than in layout-parameter space. In validation against a conventional EM solver, 97% of generated designs produce usable geometries, and the inverse-plus-surrogate pipeline reaches mean percent errors of 0.73% for qubit frequency and 1.58% for anharmonicity, comparable to or below the fabrication and simulation-to-measurement uncertainty expected for academic-process transmon devices of this type. A single pipeline query takes ~60 ms on CPU, versus ~2 min for a conventional EM capacitance extraction on the same hardware, a speedup of approximately 2,000x. Batching minimizes the AI model inference overhead, reducing the runtime to 3.1 microseconds per sample on CPU and 2.6 microseconds per sample on GPU at a batch size of 2048, resulting in speedups of 3.9 x 10^7 and 4.6 x 10^7, respectively, relative to a single conventional CPU EM extraction. Our results indicate that component-level inverse design usefully extends and complements conventional EM simulation, including for small datasets on the order of 1,000 samples.
Superconducting qubits represent a highly promising and scalable quantum hardware technology. The design of these qubits follows a typical microwave circuit design process, where the circuit encompasses multiple resonance frequencies associated with both the resonators and the qubits. Chip design involves the meticulous engineering of resonators and qubits to align the circuit’s various frequencies with predefined values. Additionally, adjusting the coupling between resonators and qubits is essential to meet specific quantum parameters. As the demand for scaling to thousands of qubits grows, it becomes imperative to incorporate systematic engineering and design automation. In this article, we will explore the present requirements for electronic design automation (EDA) that shall enable the design of future superconducting quantum computers.
Machine Learning (ML) has been widely applied across numerous domains due to its ability to automatically identify informative patterns from data for various tasks. The availability of large-scale data and advanced computational power enables the development of sophisticated models and training strategies, leading to state-of-the-art performance, but it also introduces substantial challenges. Quantum Computing (QC), which exploits quantum mechanisms for computation, has attracted growing attention and significant global investment as it may address these challenges. Consequently, Quantum Machine Learning (QML), the integration of these two fields, has received increasing interest, with a notable rise in related studies in recent years. We are motivated to review these existing contributions regarding quantum circuit-based learning models for classical data analysis and highlight the identified potentials and challenges of this technique. Specifically, we focus not only on QML models, both kernel-based and neural network-based, but also on recent explorations of their integration with classical machine learning layers within hybrid frameworks. Moreover, we examine both theoretical analysis and empirical findings to better understand their capabilities, and we also discuss the efforts on noise-resilient and hardware-efficient QML that could enhance its practicality under current hardware limitations. In addition, we cover several emerging paradigms for advanced quantum circuit design and highlight the adaptability of QML across representative application domains. This study aims to provide an overview of the contributions made to bridge quantum computing and machine learning, offering insights and guidance to support its future development and pave the way for broader adoption in the coming years.
Abstract The past half century has witnessed the rapid development of integrated circuits from several to billions of electronic components, where a crucial enabling method was the electronic computer-aided design. Similarly, simulations of the quantum electrocircuits will play a key role in designing advanced quantum computers. While these simulations could be done classically on a small scale, the resources required for the classical simulations will grow exponentially with the complexity of the strongly-correlated quantum hardwares. Here, we demonstrate quantum computer-aided design of a new high-performance superconducting qubit based on variational quantum simulations. The discovered new qubits intrinsically have important features superior to the widely used transmons qubits, because the new design eliminates the charge noise but without sacrificing anharmonicity and thus has a larger anharmonicity than the transmons. The new qubit - which we call “plasmonium” - is compatible with the control technologies in the transmon, exhibits long coherence time, and can bring in a number of practical advantages, including shrinking physical sizes, improving gate and readout fidelity, reducing state leakage, and alleviating frequency crowding in multi-qubit devices.
Implementing fast and high-fidelity quantum operations using open-loop quantum optimal control relies on having an accurate model of the quantum dynamics. Any deviations between this model and the complete dynamics of the device, such as the presence of spurious modes or pulse distortions, can degrade the performance of optimal controls in practice. Here, we propose an experimentally simple approach to realize optimal quantum controls tailored to the device parameters and environment while specifically characterizing this quantum system. Concretely, we use physics-inspired machine learning to infer an accurate model of the dynamics from experimentally available data and then optimize our experimental controls on this trained model. We show the power and feasibility of this approach by optimizing arbitrary single-qubit operations on a superconducting transmon qubit, using detailed numerical simulations. We demonstrate that this framework produces an accurate description of the device dynamics under arbitrary controls, together with the precise pulses achieving arbitrary single-qubit gates with a high fidelity of about 99.99%.