Quantum computing faces significant challenges from decoherence and noise, which limit the practical implementation of quantum algorithms. While substantial progress has been made in improving individual qubit coherence times, the collective behavior of interconnected qubit systems remains incompletely understood. The connectivity architecture plays a crucial role in determining overall system susceptibility to environmental noise, yet systematic characterization of this relationship has been hindered by computational complexity. We develop a machine learning framework that bridges graph features with quantum device characterization to predict decoherence lifetime directly from connectivity patterns. By representing quantum architectures as connected graphs and using 14 topological features as input to supervised learning models, we achieve accurate lifetime predictions with R2>0.96 for both superconducting and semiconductor platforms. Our analysis reveals fundamentally distinct decoherence mechanisms: superconducting qubits show high sensitivity to global connectivity measures (betweenness centrality δ1=0.484, spectral entropy δ1=0.480), while semiconductor quantum dots exhibit exceptional sensitivity to system scale (node count δ2=0.919, importance = 1.860). The complete failure of cross-platform model transfer (R2 scores of -0.39 and -433.60) emphasizes the platform-specific nature of optimal connectivity design. Our approach enables rapid assessment of quantum architectures without expensive simulations, providing practical guidance for noise-optimized quantum processor design.
Quantum computing faces significant challenges from decoherence and noise, which limit the practical implementation of quantum algorithms. While substantial progress has been made in improving individual qubit coherence times, the collective behavior of interconnected qubit systems remains incompletely understood. The connectivity architecture plays a crucial role in determining overall system susceptibility to environmental noise, yet systematic characterization of this relationship has been hindered by computational complexity. We develop a machine learning framework that bridges graph features with quantum device characterization to predict decoherence lifetime directly from connectivity patterns. By representing quantum architectures as connected graphs and using 14 topological features as input to supervised learning models, we achieve accurate lifetime predictions with R2>0.96 for both superconducting and semiconductor platforms. Our analysis reveals fundamentally distinct decoherence mechanisms: superconducting qubits show high sensitivity to global connectivity measures (betweenness centrality δ1=0.484, spectral entropy δ1=0.480), while semiconductor quantum dots exhibit exceptional sensitivity to system scale (node count δ2=0.919, importance = 1.860). The complete failure of cross-platform model transfer (R2 scores of -0.39 and -433.60) emphasizes the platform-specific nature of optimal connectivity design. Our approach enables rapid assessment of quantum architectures without expensive simulations, providing practical guidance for noise-optimized quantum processor design.
Two-level systems (TLS) in superconducting quantum devices are a dominant source of decoherence, noise, and device variability, ultimately limiting qubit performance and scalability. Efficient and reliable extraction of TLS parameters from spectroscopy measurements is therefore essential for both device characterization and materials optimization. In this thesis, machine learning methods are developed to accelerate and enhance TLS characterization from superconducting qubit spectroscopy data. The approach combines physics-based simulation of driven qubit–TLS dynamics with supervised learning models trained on synthetic datasets to infer underlying physical parameters from two-dimensional spectroscopy maps. Particular emphasis is placed on understanding how different parameters are encoded in observable features, and on evaluating the extent to which the inverse problem is well-posed. The results demonstrate that machine-learning-assisted inference can significantly reduce the computational cost of parameter estimation compared to conventional fitting-based approaches, while maintaining high accuracy for spectrally encoded parameters such as TLS frequency and coupling strength. In contrast, decoherence parameters are shown to be intrinsically more challenging to extract from short-time spectroscopy data, highlighting fundamental limitations of the measurement protocol rather than the learning model. Overall, this work establishes a data-driven framework for scalable TLS characterization and provides insight into the interplay between physical observability and machine learning-based inference in quantum systems.
Introduction: The scalability of superconducting quantum processors is fundamentally limited by environmental noise and the exponential measurement overhead required for multi-qubit entanglement verification. Methods: In this work, we introduce a hybrid machine learning (ML) framework utilizing convolutional neural networks (CNNs) to simultaneously characterize decoherence channels and verify multipartite entanglement. The model is trained on synthetic noisy syndrome measurement data augmented with empirical SPAM models and applied to a 5-qubit superconducting device, where the neural network acts as a non-linear entanglement witness. Results: sweeps. Furthermore, it successfully verifies genuine multipartite entanglement with a fidelity prediction of 99.1%, while requiring only 12% of the measurement bases demanded by full quantum state tomography (QST). Discussion: Crucially, our methodology bypasses two distinct challenges: it drastically reduces the measurement complexity inherent in multi-qubit entanglement verification, and it inherently filters out state preparation and measurement (SPAM) errors without requiring noise-amplifying matrix inversions. These numerical results demonstrate that ML-assisted protocols provide a highly resource-efficient pathway for the characterization of noisy intermediate-scale quantum (NISQ) devices.
Accurately predicting quantum decoherence time is a core bottleneck in scaling up noisy intermediate-scale quantum (NISQ) devices. Traditional frameworks relying on the Lindblad master equation suffer from the curse of dimensionality and obscure the intuitive physical mechanisms of decoherence behind complex system-environment coupling assumptions. Based on graph algebraic connectivity, this paper proposes a deterministic topological model of quantum decoherence. We rigorously map multi-particle quantum entanglement to the algebraic connectivity (Fiedler value, ) of an underlying structural graph and redefine decoherence as a Poissonian random edge-deletion process. By modeling this topological evolution as a continuous-time Markov chain (CTMC), we derive a unified and exact recursive analytical equation for the expected hitting time of complete decoherence for arbitrary asymmetric topological states. The analytical solutions strictly prove the relative robustness of prototypical entangled states . Furthermore, the model demonstrates unprecedented computational advantages by instantly yielding the exact analytical decoherence time for a 10-body asymmetric complex network, which is perfectly validated by 50,000 Monte Carlo simulations with a relative error of . We calibrate the core parameter (single-edge fracture rate) using the latest public experimental data of superconducting quantum chips from IBM and Google, and the theoretical predictions are in high agreement with the experimental results within the measurement error range . This topological downgrade mechanism reduces the computational complexity of decoherence calculations to , providing a powerful theoretical and algorithmic tool for the optimization of quantum processing unit (QPU) underlying architectures and the design of fault-tolerant multi-particle quantum networks.
Abstract Accurately predicting quantum decoherence time is a core bottleneck in scaling up noisy intermediate-scale quantum (NISQ) devices. Traditional frameworks relying on the Lindblad master equation suffer from the curse of dimensionality (O(4^n )) and obscure the intuitive physical mechanisms of decoherence behind complex system-environment coupling assumptions. Based on graph algebraic connectivity, this paper proposes a deterministic topological model of quantum decoherence. We rigorously map multi-particle quantum entanglement to the algebraic connectivity (Fiedler value, λ_2) of an underlying structural graph and redefine decoherence as a Poissonian random edge-deletion process. By modeling this topological evolution as a continuous-time Markov chain (CTMC), we derive a unified and exact recursive analytical equation for the expected hitting time of complete decoherence for arbitrary asymmetric topological states. The analytical solutions strictly prove the relative robustness of prototypical entangled states (T_"Bell" >T_W≫T_"GHZ" ). Furthermore, the model demonstrates unprecedented computational advantages by instantly yielding the exact analytical decoherence time for a 10-body asymmetric complex network, which is perfectly validated by 50,000 Monte Carlo simulations with a relative error of <0.5%. We calibrate the core parameter γ (single-edge fracture rate) using the latest public experimental data of superconducting quantum chips from IBM and Google, and the theoretical predictions are in high agreement with the experimental results within the measurement error range (<3%). This topological downgrade mechanism reduces the computational complexity of decoherence calculations to O(n^3 ), providing a powerful theoretical and algorithmic tool for the optimization of quantum processing unit (QPU) underlying architectures and the design of fault-tolerant multi-particle quantum networks.
The development of practical quantum processors relies on the ability to control and predict their functioning despite the presence of noise. This is particularly challenging for temporarily correlated noise. Here we propose a physics-inspired supervised machine learning approach to efficiently and accurately predict the functioning of quantum processors in the presence of correlated noise, which only requires data from randomized benchmarking experiments. To demonstrate the efficacy of our technique, we analyze the data from a superconducting quantum processor with tunable correlated noise. We produce training data by evolving the system for a number of time steps, and with this, we fully quantify the correlated noise and accurately predict the dynamics of the system for times beyond the training data. This approach shows a path towards efficient and effective learning of noisy quantum dynamics and optimally control quantum processors over long and complex computations even in the presence of correlated noise. Combining standard quantum benchmarking techniques with a physics-inspired supervised machine learning algorithm, the authors efficiently and accurately predict the functioning of a superconducting qubit in the presence of complex noise. The proposed approach opens a path towards a better control of current quantum devices.
Superconducting qubits are a leading platform for quantum computation, yet decoherence and scalability challenges continue to limit progress toward fault-tolerant systems. This review synthesizes research on architectures, noise sources, and error correction to clarify performance trade-offs and guide development. The transition from Transmon to Fluxonium has dramatically improved qubit performance, achieving millisecond-scale coherence times. However, this success reveals a fundamental limit of coherence now being primarily restricted by the physical properties of the materials, specifically dielectric losses occurring at the Josephson junction leads and within the surrounding materials. As overcoming these material limits becomes increasingly difficult, system-level engineering determines whether high-coherence qubits can scale.Scaling quantum systems is difficult because current cooling and control systems create too much heat and this forces a shift to integrated digital superconducting circuits and cryogenic light-based links. While these hardware challenges restrict scaling, experiments in error correction show that logical qubits can already outperform physical ones. For example, a distance-7 surface code improved logical qubit lifetime by 2.4×. Achieving practical fault tolerance, however, requires moving beyond simple error models. Future systems must combine techniques like Dynamical Decoupling and Machine Learning-based Optimal Control with dynamic classical control capable of handling complex, correlated faults such as Multi-Bit Burst Errors. Progress in materials, cryogenic system integration for control, and adaptive error management must advance together to realize scalable, high-fidelity quantum hardware.