arXiv subjects

Lan Nguyen Tran

Publications and source records attributed to Lan Nguyen Tran.

At least 19 recordsLinked to original sources

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

Self-consistent double-hybrid density functional theory via one-body second-order Møller-Plesset perturbation theory and projection-based embedding

We present the development of self-consistent one-body double-hybrid (OBDH) density functional theory (DFT). In this approach, the one-body second-order Møller-Plesset (OBMP2) perturbation potential is embedded directly in the generalized Kohn-Sham framework, allowing orbitals to be optimized in the presence of MP2-level dynamic correlation. Unlike existing orbital-optimized double hybrids, OBDH requires neither the optimized effective potential nor perturbative orbital relaxation corrections. The energy functional combines a semilocal exchange-correlation functional, exact exchange, and OBMP2 correlation, from which the effective Hamiltonian and self-consistent-field equations are systematically derived. To reduce computational cost, projector-based embedding with concentric localization truncation is applied to the OBMP2 component, termed sub-OBDH. OBDH and sub-OBDH are benchmarked on diatomic potential energy curves, self-interaction errors, dihedral torsions of organic molecules, and interaction energies in non-covalent charged systems. Across all systems, OBDH consistently outperforms standard DFT, demonstrating its potential for accurate and practical electronic structure calculations.

physics.chem-ph↗

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

Reducing quantum resources for ADAPT-VQE via plateau-operator elimination and correlated mean-field downfolding

Adaptive Derivative-Assembled Problem-Tailored variational quantum eigensolvers (ADAPT-VQE) represent one of the most promising approaches for quantum chemistry on near-term quantum devices. However, their optimization is slow and may stall due to vanishing parameters and redundant operators in the ansatz. In this work, we propose a simple strategy of operator elimination that removes non-contributing operators from the pool once they are detected, enabling the optimization to continue progressing toward convergence. We examine two variants, with and without pool restoration after elimination, and find that the former converges more smoothly and faster than the latter and the standard ADAPT-VQE. To capture dynamical correlations between the active space and its environment, we combine ADAPT-VQE with our recently developed downfolding approach, the one-body downfolding framework (OBDF). In OBDF, the bare molecular Hamiltonian in the active space is replaced by a correlated effective Hamiltonian that incorporates dynamical correlation effects outside the active space. We benchmark our implementation on a linear \ce{H_6} chain, an \ce{H_6} lattice, an \ce{H_6} ring, and the \ce{N_2} molecule using the OpenFermion simulator. Our results show that operator elimination significantly reduces circuit depth and iteration count, and that OBDF-ADAPT-VQE yields energies closer to the full configuration interaction (FCI) reference than the standard approach within the same active space.

quant-ph↗

arXiv2026

Quantum resource reduction for quantum-centric supercomputing via correlated mean-field downfolding framework

We present OBDF-SQD, a hybrid quantum-classical method that combines one-body downfolding~(OBDF) based on one-body Møller--Plesset second-order perturbation theory (OBMP2) with sample-based quantum diagonalization~(SQD) for use in quantum-centric supercomputing~(QCS). In this approach, OBMP2 is executed classically to fold dynamical correlation from external orbitals into a renormalized one-body operator, yielding an effective active-space Hamiltonian that retains the same operator structure as the bare Hamiltonian and therefore requires no additional quantum circuit resources. SQD is then applied to this effective Hamiltonian, where, in this work, the quantum sampling is performed via the Qiskit Aer simulator rather than actual quantum hardware. We benchmark OBDF-SQD on dissociation curves of \ce{H6} chain, ring, and lattice systems and the \ce{N2} molecule in the cc-pVDZ basis, comparing against standard methods and active-space SQD (CAS-SQD). We observed that OBDF-SQD consistently improves upon CAS-SQD with the same active space. The simplicity of the one-body downfolding correction also makes the approach straightforwardly extensible to periodic solids within existing quantum embedding frameworks

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

Note on a rigorous derivation of self-consistent double-hybrid functional theory via generalized Kohn-Sham theory and cumulant approximation

In this short note, we present a rigorous derivation of the one-body double-hybrid density functional (OBDHF) theory, a self-consistent double-hybrid density functional framework that unifies the generalized Kohn-Sham (GKS) formalism with one-body Møller-Plesset second-order perturbation (OBMP2) theory. Conventional double-hybrid density functionals suffer from a fundamental theoretical inconsistency arising from the non-self-consistent treatment of the perturbative MP2 correlation, in which the orbitals entering the correlation energy expression are not variationally optimized with respect to the full double-hybrid energy functional. To address this deficiency, we construct a model energy functional as a linear combination of semilocal density functional approximation XC, a fraction $α_x$ of exact Hartree-Fock (HF) exchange, and a fraction $α_c$ of OBMP2 correlation. By virtue of the one-body operator structure of OBMP2, the perturbative correlation contribution is embedded directly and self-consistently into the GKS effective Hamiltonian, without recourse to the optimized effective potential (OEP) construction or perturbative orbital relaxation corrections. Through functional differentiation of the total OBDHF energy with respect to the orbitals, we derive the OBDHF effective Hamiltonian and the associated self-consistent field equations in a rigorous and transparent manner. This formulation provides a theoretically well-founded and practically tractable pathway to fully self-consistent double-hybrid density functional theory calculations within the GKS framework, resolving the self-consistency problem inherent in conventional double-hybrid functionals.

physics.chem-ph↗

arXiv2026

Accurate prediction of inverted singlet-triplet excited states using self-consistent spin-opposite perturbation theory

The violation of Hund's rule, resulting in an inverted singlet-triplet (INVEST) gap, represents a paradigm shift in photophysics with major implications for OLED technology. INVEST molecules facilitate barrierless reverse intersystem crossing, theoretically permitting 100\% internal quantum efficiency without thermal activation. However, accurately predicting negative singlet-triplet energy gaps typically demands prohibitive computational costs. In this study, we evaluate the efficacy of our recently developed one-body Møller-Plesset perturbation theory (OBMP2) and its spin-opposite variant (O2BMP2) as efficient alternatives. Benchmarking against 30 INVEST molecules reveals that O2BMP2, with appropriate spin-opposite scaling, achieves the accuracy of ADC(3) and EOM-CCSD. Furthermore, with the possibility of reducing computational complexity to $N^4$, O2BMP2 provides a robust balance of accuracy and efficiency, making it suitable for the high-throughput screening of next-generation INVEST materials.

physics.chem-ph↗

arXiv2026

Accurate prediction of K-edge excitation energies using state-specific self-consistent perturbation theory

We present the application of the recently developed one-body Møller--Plesset perturbation theory (OBMP2) to the prediction of K-edge excited states. OBMP2 is a self-consistent perturbation theory in which a canonical transformation followed by a cumulant approximation yields an effective one-body Hamiltonian. This resulting operator augments the standard Fock operator with a one-body correlation potential containing double-excitation MP2 amplitudes, allowing molecular orbitals and orbital energies to be optimized in the presence of correlation. This self-consistent framework mitigates convergence and accuracy issues often encountered in standard non-iterative MP2 for open-shell systems and bond-stretching regimes. In this work, we evaluate the performance of an OBMP2-based approach for the calculation of K-edge excitations. Utilizing benchmark test sets of both closed-shell and open-shell molecules, we demonstrate that our method outperforms established standard techniques, including $Δ$DFT, EOM-CCSD, and USTEOM-CCSD. Our findings establish the OBMP2-based $Δ$SCF protocol as a robust and accurate new computational method for the treatment of K-edge excited states.

physics.chem-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

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↗

arXiv2024

Multi-target quantum compilation algorithm

Quantum compilation is the process of converting a target unitary operation into a trainable unitary represented by a quantum circuit. It has a wide range of applications, including gate optimization, quantum-assisted compiling, quantum state preparation, and quantum dynamic simulation. Traditional quantum compilation usually optimizes circuits for a single target. However, many quantum systems require simultaneous optimization of multiple targets, such as thermal state preparation, time-dependent dynamic simulation, and others. To address this, we develop a multi-target quantum compilation algorithm to improve the performance and flexibility of simulating multiple quantum systems. Our benchmarks and case studies demonstrate the effectiveness of the algorithm, highlighting the importance of multi-target optimization in advancing quantum computing. This work lays the groundwork for further development and evaluation of multi-target quantum compilation algorithms.

quant-ph↗

arXiv2024

Attaining high accuracy for charge-transfer excitations in non-covalent complexes at second-order perturbation cost: the importance of state-specific self-consistency

Intermolecular charge-transfer (xCT) excited states important for various practical applications are challenging for many standard computational methods. It is highly desirable to have an affordable method that can treat xCT states accurately. In the present work, we extend our self-consistent perturbation methods, named one-body second-order Møller-Plesset (OBMP2) and its spin-opposite scaling variant, for excited states without additional costs to the ground state. We then assessed their performance for the prediction of xCT excitation energies. Thanks to self-consistency, our methods yield small errors relative to high-level coupled cluster methods and outperform other same scaling ($N^5$) methods like CC2 and ADC(2). In particular, the spin-opposite scaling variant (O2BMP2), whose scaling can be reduced to $N^4$, can even reach the accuracy of CC3 ($N^7$) with errors less than 0.1 eV. This method is thus highly promising for treating xCT states in large compounds vital for applications.

physics.chem-ph↗

arXiv2023

Reaching high accuracy for energetic properties at second-order perturbation cost by merging self-consistency and spin-opposite scaling

Quantum chemical methods dealing with challenging systems while retaining low computational costs have attracted attention. In particular, many efforts have been devoted to developing new methods based on the second-order perturbation that may be the simplest correlated method beyond Hartree-Fock. We have recently developed a self-consistent perturbation theory named one-body Møller-Plesset second-order perturbation theory (OBMP2) and shown that it can resolve issues caused by the non-iterative nature of standard perturbation theory. In the present work, we extend the method by introducing the spin-opposite scaling to the double-excitation amplitudes, resulting in the O2BMP2 method. We assess the O2BMP2 performance on the triple-bond N2 dissociation, singlet-triplet gaps, and ionization potentials. O2BMP2 performs much better than standard MP2 and reaches the accuracy of coupled-cluster methods in all cases considered in this work.

physics.chem-ph↗

arXiv2023

Exploring Ligand-to-Metal Charge-transfer States in the Photo-Ferrioxalate System using Excited-State Specific Optimization

The photo-ferrioxalate system (PFS), [Fe(III)(C$_2$O$_4$)]$^{3-}$, more than an exact chemical actinometer, has been extensively applied in wastewater and environment treatment. Despite many experimental efforts to improve clarity, important aspects of the mechanism of ferrioxalate photolysis are still under debate. In this paper, we employ the recently developed W$Γ$-CASSCF to investigate the ligand-to-metal charge-transfer states key to the ferrioxalate photolysis. This investigation provides a qualitative picture of these states and key potential energy surface features related to the photolysis. Our theoretical results are consistent with the prompt charge transfer picture seen in recent experiments and clarify some features that are not visible in experiments. Two ligand-to-metal charge-transfer states contribute to the photolysis of ferrioxalate, and the avoided crossing barrier between them is low compared to the initial photoexcitation energy. Our data also clarify that one Fe-O bond cleaves first, followed by the C-C bond and the other Fe-O bond.

physics.chem-ph↗

arXiv2023

Correlated reference-assisted variational quantum eigensolver

We propose an active-space approximation to reduce the quantum resources required for variational quantum eigensolver (VQE). Starting from the double exponential unitary coupled-cluster ansatz and employing the downfolding technique, we arrive at an effective Hamiltonian for active space composed of the bare Hamiltonian and a correlated potential caused by the internal-external interaction. The correlated potential is obtained from the one-body second-order Møller-Plesset perturbation theory (OBMP2), which is derived using the canonical transformation and cumulant approximation. Considering different systems with singlet and doublet ground states, we examine the accuracy in predicting both energy and density matrix (by evaluating dipole moment). We show that our approach can dramatically outperform the active-space VQE with an uncorrelated Hartree-Fock reference.

cond-mat.str-el↗

arXiv2022

Can second-order perturbation theory accurately predict electron density of open-shell molecules? The importance of self-consistency

Electron density distribution plays an essential role in predicting molecular properties. It is also a simple observable from which machine-learning models for molecular electronic structure can be derived. In the present work, we present the performance of the one-body Møller-Plesset second-order perturbation (OBMP2) theory that we have recently developed. In OBMP2, an effective one-body Hamiltonian including dynamic correlation at the MP2 level is derived using the canonical transformation followed by the cumulant approximation. We evaluate electron density and related properties of three groups of open-shell systems: atoms and their ions, main-group radicals, and halogen dimmers. We find that OBMP2 outperforms standard MP2 and density functional theory in all cases considered here, and its accuracy is comparable to coupled-cluster singles and doubles (CCSD), a higher-level method. OBMP2 is thus believed to be an effective method for predicting the accurate electron density of open-shell molecules.

physics.chem-ph↗

arXiv2022

Qsun: an open-source platform towards practical quantum machine learning applications

Currently, quantum hardware is restrained by noises and qubit numbers. Thus, a quantum virtual machine that simulates operations of a quantum computer on classical computers is a vital tool for developing and testing quantum algorithms before deploying them on real quantum computers. Various variational quantum algorithms have been proposed and tested on quantum virtual machines to surpass the limitations of quantum hardware. Our goal is to exploit further the variational quantum algorithms towards practical applications of quantum machine learning using state-of-the-art quantum computers. This paper first introduces our quantum virtual machine named Qsun, whose operation is underlined by quantum state wave-functions. The platform provides native tools supporting variational quantum algorithms. Especially using the parameter-shift rule, we implement quantum differentiable programming essential for gradient-based optimization. We then report two tests representative of quantum machine learning: quantum linear regression and quantum neural network.

quant-ph↗