arXiv subjects

Le Bin Ho

Publications and source records attributed to Le Bin Ho.

At least 19 recordsLinked to original sources

arXiv2026

Interference-induced state engineering and Hamiltonian control for noisy collective-spin metrology

Interference provides a fundamental mechanism for generating and manipulating entanglement in many-body quantum systems. Here, we develop an interference framework in which the nonlinear dynamics of collective spin-$\tfrac{1}{2}$ ensembles are mapped onto phase accumulation and self-interference in phase space, providing a direct and physically transparent description of entanglement formation. Within this framework, one-axis twisting produces Greenberger-Horne-Zeilinger (GHZ) states, while two-axis twisting generates multi-component GHZ superpositions relevant for multiparameter quantum metrology. Building on this interference-based description, we analyze metrological performance under realistic Markovian noise, including local and collective emission, pumping, and dephasing, and examine the role of Hamiltonian control based on linear and nonlinear interactions. We show that the optimal control enhances sensitivity in both single- and multiparameter estimation across noise-dependent regimes. These results establish interference as a unifying principle linking nonlinear dynamics, entanglement generation, and metrological performance. This framework offers a broadly applicable route to robust quantum-enhanced sensing in noisy many-body systems.

quant-ph↗

arXiv2026

Qmes: Quantum Meta-Learning for Encoding Selection in Quantum Kernel Methods

Selecting an effective encoding quantum circuit is a key challenge in quantum kernel methods because different feature maps can lead to different performance. Conventional methods require constructing and evaluating every circuit for each new dataset, making it computationally expensive. We present Qmes, an open-source Python package that automatically recommends circuits through meta-learning. Qmes characterizes a dataset using classical complexity measures and queries a pre-trained model to recommend circuits without quantum evaluation at inference time. The package provides modular components for meta-feature extraction, quantum-kernel evaluation, recommender training, model selection, and user-defined circuit extension. We validate Qmes on 105 classification and 86 regression benchmark datasets. Qmes reduces the mean recommendation regret by 2.2x and 4.2x for classification and regression, respectively, compared to a non-adaptive baseline, with statistical significance confirmed via a paired Wilcoxon signed-rank test ($p < 10^{-4}$). Qmes thus enables efficient and practical encoding-circuit selection for quantum kernel methods.

quant-ph↗

arXiv2026

Boosting the performance of a Lipkin-Meshkov-Glick quantum battery via symmetry-breaking quenches or a single-mode bosonic charger

We explore the operation of quantum batteries in the Lipkin-Meshkov-Glick (LMG) model, when they are charged either through a sudden quench in the magnetic field strength or by coupling them to a single-mode bosonic charger. Through initializing the battery in either the symmetric or broken symmetry phases of the LMG model we analyze how the different spectral properties can affect the performance of both the charging and discharging of the battery. In particular, we show that by quenching the magnetic field strength from the symmetric phase to the broken phase, we can achieve a significant enhancement in stored energy, as well as stable and efficient ergotropy extraction. Similar observations can be made when introducing weak coupling between the battery with the bosonic charger, while the amount of stored work and ergotropy saturate at strong coupling. These findings emphasize the importance of the magnetic field dynamics or environmental coupling in optimizing charging performance, which could lead to practical applications in quantum energy storage.

quant-ph↗

arXiv2026

Analytic correspondence between multipartite entanglement and quantum phase transitions

We derive an analytic correspondence between multipartite concentratable entanglement (CE) and quantum phase transitions in one-dimensional quantum spin systems. We prove that CE shares the same analyticity structure as generalized order parameters, identifies the same quantum phases, and, for Gaussian ground states, is completely determined by the same single-particle correlation matrix. Numerical validation on the transverse-field Ising and generalized cluster-Ising models confirms these analytical predictions for both symmetry-breaking and symmetry-protected topological quantum phase transitions. Since CE can be measured directly using a constant-depth parallelized SWAP-test circuit, our results establish CE as an experimentally accessible, model-independent probe of quantum phase transitions without requiring model-specific order parameters.

quant-ph↗

arXiv2026

Multiparameter sensing of axion dark matter with superconducting-qubit networks

We propose a quantum sensor network for direct detection of quantum chromodynamics (QCD) axion dark matter using entangled superconducting qubits. In the presence of a static magnetic field, the oscillating axion field induces a coherent qubit rotation described by an encoding angle proportional to the axion-photon coupling and an unknown dark-matter phase. Treating the phase as an additional unknown parameter turns axion detection into a two-parameter quantum estimation problem. We formulate these signals in Cartesian coordinates, thereby avoiding the singularity that arises in the weak-signal regime and establishing a regular framework for multiparameter quantum estimation. Using this framework, we optimize the quantum sensor network and combine it with Bayesian inference to reconstruct the axion-photon coupling. The optimized protocol accurately recovers the KSVZ and DFSZ benchmark couplings over the mass range $m_a\in[0.1,10]~μ{\rm eV}$, generalizes to previously unseen masses, and remains robust against realistic superconducting-qubit decoherence and gate errors.

quant-ph↗

arXiv2026

Majoron dark matter detection via hybrid magnon transmon qubit system

Ultralight Majorons are well-motivated dark-matter candidates that can couple to electron spins, generating an oscillating pseudo-magnetic field. We propose a hybrid magnon-qubit haloscope that exploits this interaction to search for Majoron dark matter. In our scheme, the Majoron field resonantly drives the Kittel mode of a ferrimagnetic yttrium iron garnet (YIG) sphere, producing a collective magnon response enhanced by the large spin ensemble. The resulting magnon population is transduced to a superconducting transmon qubit through a cavity-mediated dispersive interaction and detected using quantum-nondemolition Ramsey interferometry. Using experimentally demonstrated parameters for magnon-cavity-qubit systems, we derive the Majoron-induced signal, evaluate the projected sensitivity, and analyze the corresponding mass-scan strategy enabled by magnetic-field tuning, benchmarking the projected reach against existing laboratory and astrophysical constraints on the axion/Majoron-electron coupling. Our results establish hybrid magnon-qubit architectures as a promising quantum-sensing platform for searches for Majoron dark matter and, more generally, ultralight bosonic fields coupled to electronic spins.

hep-th↗

arXiv2026

Benchmarking loss functions for trainable quantum feature maps

Many quantum machine learning models employ quantum feature maps to encode classical data into quantum states. While fixed feature maps often lack sufficient expressivity for complex nonlinear classification tasks, trainable quantum feature maps (TQFMs) enable adaptive quantum kernels with enhanced learning capability. Different loss functions can induce distinct optimization dynamics, yet their effects remain poorly understood. In this work, we apply the Log-Likelihood Loss function for TQFMs and provide a systematic comparison with Distance Loss and Measurement Loss. Through extensive numerical experiments, we compare their optimization dynamics, computational costs, and classification performance. Our results show that Log-Likelihood Loss consistently achieves more stable optimization than Measurement Loss while retaining linear computational complexity. The resulting benchmark offers practical guidance for balancing trainability, computational efficiency, and predictive performance in quantum kernel optimization.

quant-ph↗

arXiv2026

Measurement Geometry as a Resource for Certifying Network Nonlocality

Quantum networks can exhibit nonclassical correlations that cannot be explained by classical models with independent sources. While the role of entanglement is well understood, the impact of measurement design remains largely unexplored. Here we develop an operational framework for certifying network nonlocality in the bilocal Alice--Bob--Charlie network using ancilla-assisted meters to evaluate the nonlocal observables required for bilocal and fully network nonlocal (FNN) witnesses. The approach successfully reproduces both bilocal and FNN correlations in simulation. On the 156-qubit superconducting processor \textit{ibm\_kingston}, we observe bilocal nonlocality with $\mathcal{S}_{\rm BLHV}=1.067(6)>1$ after readout-error mitigation, while the FNN witnesses reach $99\%$ and $96\%$ of their certification thresholds, implying the substantially stronger requirements for FNN certification. We further show that Bob's joint measurement determines the accessible level of network nonlocality: bilocal and FNN certification are optimized by different measurement settings, while both violations can disappear even for maximally entangled states. These results identify measurement geometry as an independent resource for network nonlocality and provide a practical route toward its certification on programmable quantum processors.

quant-ph↗

arXiv2026

Towards Automated Selection of Quantum Encoding Circuits via Meta-Learning

In recent years, quantum kernel methods have shown promising applications on near-term quantum devices. However, selecting an appropriate encoding circuit for a given dataset requires costly evaluation of multiple candidates, formulated as a meta-learning problem. In this paper, we propose an automated recommender that utilizes the intrinsic characteristics of datasets to predict the optimal circuit without any quantum evaluation. Nine candidates are assessed alongside 24 classical complexity metrics serving as features, evaluated through two training approaches with four configurations, along with 14 machine learning models. Both approaches achieve Top-3 accuracy of up to 85.7% in identifying the best-performing encoding circuit, and demonstrate that classical data complexity metrics provide sufficient predictive signal for circuit selection.

quant-ph↗

arXiv2026

Exact gradient for general cost functions in variational quantum algorithms

We present a unitary-based gradient formulation for variational quantum algorithms (VQAs) that applies to general differentiable cost function defined by a parameterized quantum circuit composed of Pauli-generated rotations. The gradient is obtained directly from the underlying unitary evolution, without assuming a specific expectation-value form of the cost function. The resulting expressions can be accessed on quantum hardware using the Hadamard and Hilbert-Schmidt tests. We demonstrate the method in variational quantum compilation, where it yields stable and accurate gradient estimates. This unitary-based framework therefore provides a broadly applicable and hardware-compatible tool for gradient evaluation in VQAs.

quant-ph↗

arXiv2026

Quantum simulation of strong charge-parity violation and Peccei-Quinn mechanism

Quantum Chromodynamics (QCD) admits a topological $\barθ$ term that violates charge-parity ($CP$) symmetry, yet experiments indicate that $\barθ$ is extremely small. To investigate this problem in a controlled setting, we derive a Hamiltonian formulation of QCD through a $(1+1)$-dimensional Schwinger-model analogue. Fermionic and gauge degrees of freedom are encoded into qubits using Jordan-Wigner and quantum-link mappings, yielding a compact Pauli Hamiltonian that preserves the essential topological vacuum structure. Ground states are prepared using a feedback-based quantum optimization protocol, providing access to the vacuum energy on few-qubit simulators. We observe vacuum minima at $\barθ=0$ and $2π$, consistent with the continuum QCD expectations within the accessible regime. Upon coupling to a dynamical axion field, the system relaxes to $θ_{\rm eff}=0$, realizing the Peccei-Quinn mechanism within a minimal quantum simulation. These results demonstrate how quantum simulation can probe $CP$ violation and its dynamical resolution in gauge theories.

hep-lat↗

arXiv2026

Advancing quantum process tomography through quantum compilation

Quantum process tomography (QPT) plays a central role in characterizing quantum gates and circuits, diagnosing quantum devices, calibrating hardware, and supporting quantum error correction. However, conventional QPT methods face challenges related to scalability and sensitivity to noise. In this work, we propose a QPT framework based on quantum compilation, which represents quantum processes using optimized Kraus operators and Choi matrices. By formulating QPT as a compilation and optimization problem, our approach significantly reducing measurement and computational overhead while maintaining reconstruction accuracy. We benchmark the method using numerical simulations of Haar-random unitary gates and demonstrate a reliable process reconstruction. We further apply the framework to dephasing channels with both time-homogeneous and time-inhomogeneous noise, as well as to depolarizing and amplitude-damping channels, where stable performance is observed across different noise regimes. These results indicate that quantum compilation-based QPT can serve as a practical alternative to standard QPT methods for quantum process characterization and device validation.

quant-ph↗

arXiv2026

Feedback-Based Quantum Control for Safe and Synergistic Drug Combination Design

Drug-drug interactions (DDIs) strongly affect the safety and efficacy of combination therapies. Despite the availability of large DDI databases, selecting optimal multi-drug combinations that balance safety, therapeutic benefit, and regimen size remains a challenging combinatorial optimization problem. Here, we present a quantum-control-based framework for DDI-aware drug combination optimization, in which known harmful and synergistic interactions are encoded into Ising Hamiltonians as penalties and rewards, respectively. The optimization is performed using the feedback-based quantum algorithm FALQON, a gradient-free variational approach. We study two clinically motivated tasks: the Maximum Safe Subset problem and the Synergy-Constrained Optimization problem. Numerical simulations using interaction data from Drugs.com and SYNERGxDB demonstrate efficient convergence and high-quality solutions for clinically relevant drug sets, including COVID-19 case studies.

quant-ph↗

arXiv2025

Trade-off relation between integrated metrological gain and local dissipation in magnetic-field sensing by quantum spin ensemble

Quantum metrology plays a central role in precision sensing, where quantum enhancement of detection performance is crucial for both fundamental studies and practical applications. In this work, we derive a tight performance bound for magnetic-field sensing with a spin ensemble in the presence of dissipation. The metrological performance is quantified by the integrated metrological gain (IMG), which explicitly incorporates the time evolution of the measurement apparatus. By combining the Lindblad master equation with the quantum Fisher information, we obtain analytically exact trade-off relations between the IMG and the dissipation rate for local dephasing and local emission processes, showing that the gain scales inversely with the dissipation strength. This trade-off complements the Heisenberg limit, which addresses only the scaling with the number of spins and neglects dissipative dynamics. We analyze various initial state preparations and elucidate the role of quantum entanglement in the presence of dissipation. Notably, while entanglement is essential for achieving Heisenberg scaling at short times, it also accelerates dissipative degradation during time evolution. Consequently, for sufficiently long observation times, comparable metrological performance can be achieved even without entanglement.

quant-ph↗

arXiv2025

Imaginary-time-enhanced feedback-based quantum algorithms for universal ground-state preparation

Preparing ground states of strongly correlated quantum systems is a central goal in quantum simulation and optimization. The feedback-based quantum algorithm (FALQON) provides an attractive alternative to variational methods with a fully quantum feedback rule, but it fails in the presence of spectral degeneracies, where the feedback signal collapses and the evolution cannot reach the ground state. Using the Fermi-Hubbard model on lattices up to 3x3, we show that this breakdown appears at half-filling on the 2x2 lattice and extends to both half-filled and doped configurations on the 3x3 lattice. We then introduce an imaginary-time-enhanced FALQON (ITE-FALQON) scheme, which inserts short imaginary-time evolution steps into the feedback loop. The hybrid method suppresses excited-state components, escapes degenerate subspaces, and restores monotonic energy descent. The ITE-FALQON achieves a reliable ground-state convergence across all fillings, providing a practical route to scalable ground-state preparation in strongly correlated quantum systems.

quant-ph↗

arXiv2025

Flexible Genetic Algorithm for Quantum Support Vector Machines

Quantum Support Vector Machines (QSVM) is one of the most promising frameworks in quantum machine learning, yet their performance depends on the design of the feature map. Conventional approaches rely on fixed quantum circuits, which often fail to generalize across datasets. To address this limitation, we propose GA-QSVM, a hybrid framework that employs Genetic Algorithms (GA) to automatically optimize feature maps. The proposed method introduces a configurable framework that flexibly defines the evolutionary parameters, enabling the construction of adaptive circuits. Experimental evaluation of datasets, including Digits, Fashion, Wine, and Breast Cancer, demonstrates that GA-QSVMs achieve a comparable accuracy compared to classical SVMs and standard QSVMs. Furthermore, transfer learning results indicate that GA-QSVM's circuits generalize effectively across datasets. These findings highlight the potential of evolutionary strategies to automate and enhance kernel design for future quantum machine learning applications.

quant-ph↗

arXiv2025

Optimized quantum sensor networks for ultralight dark matter detection

Dark matter (DM) remains one of the most compelling unresolved problems in fundamental physics, motivating the search for new detection approaches. We propose a network-based quantum sensor architecture to enhance sensitivity to ultralight DM fields. Each node in the network is a superconducting qubit, interconnected via controlled-Z gates in symmetric topologies such as line, ring, star, and fully connected graphs. We investigate four- and nine-qubit systems, optimizing both state preparation and measurement using a variational quantum metrology framework. This approach minimizes the quantum and classical Cramér-Rao bounds to identify optimal configurations. Bayesian inference is employed to extract the DM-induced phase shift from measurement outcomes. Our results show that optimized network configurations significantly outperform conventional GHZ-based protocols while maintaining shallow circuit depths compatible with noisy intermediate-scale quantum hardware. Sensitivity remains robust under local dephasing noise. These findings highlight the importance of network structure in quantum sensing and point toward scalable strategies for quantum-enhanced DM detection.

quant-ph↗

arXiv2025

Statistical analysis of barren plateaus in variational quantum algorithms

We investigate the barren plateau (BP) phenomenon in variational quantum algorithms using a statistical approach. Using Gaussian function models, we identify three distinct types of BPs. The first type, which we called localized-dip BPs, occurs in landscapes that are mostly flat but contain a dip point where the gradient is large in a small region around the minimum. The second type, called localized-gorge BPs, which are somewhat similar to the localized-dip BPs but contain a gorge line. The third type, called everywhere-flat BPs, appears when the entire landscape is uniformly flat with almost vanishing gradients, making optimization significantly more difficult. After illustrating these behaviors in the Gaussian function models, we extend the analysis to the variational quantum eigensolver (VQE). We consider two types of ansätze: the hardware-efficient ansatz and the random Pauli ansatz. For both ansätze, we only observe the everywhere-flat BPs. Using our statistical approach, we searched for localized-dip and localized-gorge BPs but found no evidence of such features in the examples studied, suggesting that everywhere-flat BPs dominate in these ansätze. Our method effectively probes landscape features by capturing the gradient scaling across parameter space, making it a useful tool for diagnosing BPs in variational algorithms. To mitigate BPs in the VQE, we employ a genetic algorithm (GA) to optimize the random gates generated in the ansätze, thereby reshaping the cost function landscape to enhance the optimization efficiency. A comparison with an unoptimized ansatz shows how the ansatz design can improve the scalability and reliability of variational quantum algorithms.

quant-ph↗