Multi-Metric Quantum State Analysis and Decoherence Profiling in Quantum Dot Systems: A Theoretical Approach with Deep Learning-Based Validation
Blessed Yahweh, Aniekan Martin Ekanem, Nyakno Jimmy George, Esther O. Oduntan, S. S. Naina Mohammed, Abdulhamid Abdulhamid, Burch Ndifon Takim, J. E. Ante, Yohanna Emmanuel, S. O. Essang
Abstract Quantum coherence and fidelity are essential ingredients for scalable quantum technologies, particularly in solid-state platforms such as quantum dots (QDs). In this work, we introduce a physics-inspired framework for the multi-metric characterization of QDs confined to a spherical potential. We obtain the energy eigenvalues using Nikiforov-Uvarov Functional Analysis (NUFA) and calculate the thermodynamic and information-theoretic quantities of purity, Rényi-2 entropy, and dynamical loss of coherence, to give quantitative descriptors of the confinement geometry, excitation dynamics, and decoherence sensitivity. For predictive modeling, we develop a supervised deep neural network (DNN) that learns to map quantum energy features to the corresponding state metrics, providing a quick and accurate estimator that adheres to the underlying physics. Our findings indicate that the low-energy and highly localized states have the lowest entropy and highest purity, whereas the higher excited states exhibit significant decoherence and thermal leakage. This hybrid data-modeling strategy not only enables a systematic connection between the energy-level physics and quantum information-theoretic measures but also provides an enabling step towards intelligent coherence management in QD systems. The framework can be readily extended to other related near-term intermediate-scale quantum (NISQ) systems for a generalized pathway to fidelity-guided quantum design and diagnostics.
Abstract Quantum coherence and fidelity are essential ingredients for scalable quantum technologies, particularly in solid-state platforms such as quantum dots (QDs). In this work, we introduce a physics-inspired framework for the multi-metric characterization of QDs confined to a spherical potential. We obtain the energy eigenvalues using Nikiforov-Uvarov Functional Analysis (NUFA) and calculate the thermodynamic and information-theoretic quantities of purity, Rényi-2 entropy, and dynamical loss of coherence, to give quantitative descriptors of the confinement geometry, excitation dynamics, and decoherence sensitivity. For predictive modeling, we develop a supervised deep neural network (DNN) that learns to map quantum energy features to the corresponding state metrics, providing a quick and accurate estimator that adheres to the underlying physics. Our findings indicate that the low-energy and highly localized states have the lowest entropy and highest purity, whereas the higher excited states exhibit significant decoherence and thermal leakage. This hybrid data-modeling strategy not only enables a systematic connection between the energy-level physics and quantum information-theoretic measures but also provides an enabling step towards intelligent coherence management in QD systems. The framework can be readily extended to other related near-term intermediate-scale quantum (NISQ) systems for a generalized pathway to fidelity-guided quantum design and diagnostics.
The remarkable generalization capabilities of overparameterized neural networks remain one of the most profound mysteries in modern machine learning. Despite having sufficient capacity to simply memorize training data, these networks consistently achieve superior performance on unseen test examples. Traditional analyses focusing on weight distributions, loss landscapes, and optimization dynamics have provided partial insights but fail to capture the complex correlational structure that emerges between neurons during learning. This paper introduces a comprehensive quantum information framework for analyzing neural network representations, treating hidden layer activations as quantum states and characterizing them through density matrices, von Neumann entropy, purity, and related quantum information metrics. Through systematic experiments on synthetic classification tasks and algorithmic reasoning problems spanning over 3000 cumulative training epochs and 5000 Wang-Landau sampling iterations, we demonstrate that these quantum metrics reveal structure completely invisible to classical analysis. Our results show that stochastic gradient descent systematically reduces von Neumann entropy while increasing purity as networks learn, transitioning from highly mixed quantum states with entropy approximately 1.42 and purity approximately 0.29 to purer configurations with entropy approximately 1.19 and purity approximately 0.37 that achieve superior test accuracy of 99.5%. Using Wang-Landau sampling to explore the equilibrium entropy landscape, we discover that neural networks naturally favor high-entropy states with entropy approximately 1.42 and moderate accuracy of 62.0%, and that optimization actively purifies the quantum state to achieve task performance. On modular arithmetic tasks exhibiting the grokking phenomenon, we observe that von Neumann entropy increases dramatically from 1.55 to 3.29, representing a 112% increase, during an extended 2000-epoch memorization phase before any improvement in generalization, while purity decreases correspondingly from 0.444 to 0.042, a 90.5% decrease, approaching the maximally mixed limit of 0.01. This entropy accumulation phase appears necessary for eventual generalization, with the network building quantum correlations at a rate of approximately 0.00097 nats per epoch during the plateau. Weight entropy, in contrast, shows minimal change of only 11% increase over the same period. Correlation analysis reveals that von Neumann entropy and purity are almost perfectly anticorrelated with a correlation coefficient of 1-0.99, while weight entropy shows weak correlation with quantum metrics with absolute correlation coefficients less than 0.3. The effective rank of the density matrix stabilizes near 2.7 to 3.0 for the 6-neuron classifier, indicating that the network effectively uses approximately three independent activation modes. These findings establish quantum information metrics as powerful tools for understanding neural network learning dynamics, provide the first quantitative evidence for the entropy accumulation interpretation of grokking, and suggest that neural networks undergo a quantum-inspired phase transition during learning. We discuss implications for generalization theory, optimization algorithm design, and the statistical physics of deep learning, and outline a comprehensive research agenda for extending these methods to larger architectures and more complex tasks.
The remarkable generalization capabilities of overparameterized neural networks remain one of the most profound mysteries in modern machine learning. Despite having sufficient capacity to simply memorize training data, these networks consistently achieve superior performance on unseen test examples. Traditional analyses focusing on weight distributions, loss landscapes, and optimization dynamics have provided partial insights but fail to capture the complex correlational structure that emerges between neurons during learning. This paper introduces a comprehensive quantum information framework for analyzing neural network representations, treating hidden layer activations as quantum states and characterizing them through density matrices, von Neumann entropy, purity, and related quantum information metrics. Through systematic experiments on synthetic classification tasks and algorithmic reasoning problems spanning over 3000 cumulative training epochs and 5000 Wang-Landau sampling iterations, we demonstrate that these quantum metrics reveal structure completely invisible to classical analysis. Our results show that stochastic gradient descent systematically reduces von Neumann entropy while increasing purity as networks learn, transitioning from highly mixed quantum states with entropy approximately 1.42 and purity approximately 0.29 to purer configurations with entropy approximately 1.19 and purity approximately 0.37 that achieve superior test accuracy of 99.5%. Using Wang-Landau sampling to explore the equilibrium entropy landscape, we discover that neural networks naturally favor high-entropy states with entropy approximately 1.42 and moderate accuracy of 62.0%, and that optimization actively purifies the quantum state to achieve task performance. On modular arithmetic tasks exhibiting the grokking phenomenon, we observe that von Neumann entropy increases dramatically from 1.55 to 3.29, representing a 112% increase, during an extended 2000-epoch memorization phase before any improvement in generalization, while purity decreases correspondingly from 0.444 to 0.042, a 90.5% decrease, approaching the maximally mixed limit of 0.01. This entropy accumulation phase appears necessary for eventual generalization, with the network building quantum correlations at a rate of approximately 0.00097 nats per epoch during the plateau. Weight entropy, in contrast, shows minimal change of only 11% increase over the same period. Correlation analysis reveals that von Neumann entropy and purity are almost perfectly anticorrelated with a correlation coefficient of 1 -0.99, while weight entropy shows weak correlation with quantum metrics with absolute correlation coefficients less than 0.3. The effective rank of the density matrix stabilizes near 2.7 to 3.0 for the 6-neuron classifier, indicating that the network effectively uses approximately three independent activation modes. These findings establish quantum information metrics as powerful tools for understanding neural network learning dynamics, provide the first quantitative evidence for the entropy accumulation interpretation of grokking, and suggest that neural networks undergo a quantum-inspired phase transition during learning. We discuss implications for generalization theory, optimization algorithm design, and the statistical physics of deep learning, and outline a comprehensive research agenda for extending these methods to larger architectures and more complex tasks.
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 decoherence, a fundamental challenge in quantum information processing, represents the loss of quantum coherence due to interaction with the environment. This paper explores a novel mathematical framework for understanding and quantifying this phenomenon, proposing a 'Quantum Decoherence Metric' – a rigorously defined function that captures the rate of decoherence and offers a precise tool for analysis. This work addresses a longstanding difficulty in modeling and analyzing decoherence, aiming for a more mathematically solid and controllable approach. The core mechanism involves the creation of a metric specifically designed to quantify the degradation of quantum states. This development builds upon existing theoretical concepts, providing a quantitative lens through which to examine and potentially mitigate decoherence effects. The resulting framework offers a significant step towards a deeper understanding of quantum systems and their stability.
Machine learning methods have been used to infer specific properties of limited families of optical quantum states, but a unified model that predicts a broad range of properties for practically relevant-especially multimode non-Gaussian-states without full tomography is still lacking. Here we introduce the first foundation model for the characterization of optical quantum states across a wide range of complexity, defined by three key factors: non-Gaussianity, number of modes, and degree of squeezing. We show that a single model pretrained on low-complexity states can be directly applied to characterize states of higher complexity. With limited fine-tuning, the model adapts to downstream tasks such as predicting quantum fidelity and Wigner negativity over a broad class of experimentally relevant states, including strongly non-Gaussian Schrödinger cat states, multimode systems with up to ten modes, and highly squeezed states with squeezing levels up to 10.4dB. Our results establish a unified framework for characterizing optical quantum states from limited measurement data, enabling efficient certification of quantum states relevant to optical quantum information computation, communication and metrology.
In the NISQ (Noisy intermediate-scale quantum) area, Quantum computers can be utilized for deep learning by treating variational quantum circuits as neural network models. This can be achieved by first encoding the input data onto quantum computers using nonparametric unitary gates. An alternative approach is to train the data encoding to map input data from different classes to separated locations in the Hilbert space. The separation is achieved with metric loss functions, hence the naming ``Quantum Metric Learning". With the limited number of qubits in the NISQ area, this approach works naturally as a hybrid classical-quantum computation enabling embedding of high-dimensional feature data into a small number of qubits. Here, we consider an example of the global QCD color structure of hard b-jets emerging from color singlet scalar decays to optimize the signal to background discrimination with a hybrid classical-quantum metric learning. Due to the sparsity of data, self-supervised methods with data augmentation have been utilized so far. Compared to the this classical self-supervised approach, our hybrid method shows the better classification performance without data augmentations. We emphasize that performance enhancements independent of data augmentation techniques are devoid of the artificial risks introduced by data augmentation.