arXiv subjects

Sk Mujaffar Hossain

Publications and source records attributed to Sk Mujaffar Hossain.

6 recordsLinked to original sources

arXiv2026

Circuit Depth Compression via Spectral Gap Amplification in Quantum Phase Estimation

We show that quantum phase estimation (QPE) circuits can be significantly compressed in depth by preprocessing the input operator with a sigmoid spectral filter before estimation. For systems with small spectral gaps Delta_lambda, standard QPE requires m = ceil(log2(1/Delta_lambda)) precision qubits and depth Theta(2^m). Applying a soft-step transformation f(lambda; tau,w) amplifies the effective gap to Delta_f > Delta_lambda (for w < 1/4), reducing the required precision to m_f = ceil(log2(1/Delta_f)) and compressing circuit depth by 2^(alpha Delta_m), where alpha = 1 for the LMR density-matrix exponentiation framework and alpha is in [0.11,0.42] for controlled-phase-gate circuits. We prove that this compression is exact, bounded above by log2(1/(4w Delta_lambda)) + 1, and impossible for exactly degenerate spectra. We further show that the threshold parameter tau requires only O(w) accuracy, so classical preprocessing such as covariance diagonalisation or CASSCF avoids circularity. A net resource advantage occurs when 4w^2(2^Delta_m - 1) > Delta_lambda log(1/epsilon). Validation on LiH and BeH2 bond-stretch calculations, classical covariance datasets, and synthetic near-degenerate cases demonstrates depth reductions of up to 27x and CX-gate reductions of up to 21x. For LiH, QPE output fidelity improves from 0.66 to 0.98 at a 1% hardware error rate. The method preserves the principal subspace to machine precision, requires no modification of QPE, and can be combined with readout-stage and state-preparation filtering. Negative-control tests establish the benefit condition: m_raw >= 2 and Delta_lambda > 0.

quant-ph↗

arXiv2026

Filtered Spectral Projection for Quantum Principal Component Analysis

Quantum principal component analysis (qPCA) is commonly formulated as the extraction of eigenvalues and eigenvectors of a covariance-encoded density operator. Yet in many qPCA settings the practical goal is simpler: projection onto the dominant spectral subspace. Here we introduce a projection-first framework, the Filtered Spectral Projection Algorithm (FSPA), which bypasses explicit eigenvalue estimation while preserving the relevant spectral structure. FSPA amplifies any nonzero warm-start overlap with the leading subspace and remains robust in small-gap and near-degenerate regimes, without artificial symmetry breaking in the absence of bias. We show that FSPA achieves an oracle complexity $\mathcal{O}((\log(1/ε)+\log(1/|a_1|^2))/\log(λ_1/λ_2))$,which is tight by a matching lower bound, establishing it as an\emph{optimal} projection primitive. We derive a convergence rate for degenerate spectra, give a circuit resource analysis with $n+\mathcal{O}(1)$ qubit overhead independent of system dimension, and extend the method to threshold spectral projection, Threshold-FSPA, which converges in $\mathcal{O}(\log(1/ε))$ calls when the threshold lies between eigenvalues. In the density matrix exponentiation access model, FSPA gives an exponential copy-complexity advantage over classical methods. For classical datasets, we show that for amplitude-encoded centered data the ensemble density matrix $ρ=\sum_i p_i|ψ_i\rangle\langleψ_i|$ equals the covariance matrix. Numerical tests on chemistry density matrices, noisy circuit outputs, Breast Cancer Wisconsin, handwritten Digits, and 1--4-qubit scalability confirm the theory. A minimal Qiskit implementation validates magnitude invariance, signal amplification, and no spurious symmetry breaking. These results establish FSPA as an optimal and deployable quantum spectral projection primitive.

stat.ML↗

arXiv2025

Quantum Simulations of Battery Electrolytes with VQE-qEOM and SQD: Active-Space Design, Dissociation, and Excited States of LiPF$_6$, NaPF$_6$, and FSI Salts

Accurate prediction of excited states in battery electrolytes is central to understanding photostability, oxidative stability, and degradation. We employ hybrid quantum-classical algorithms -- the Variational Quantum Eigensolver (VQE) for ground states combined with the quantum equation of motion (qEOM) for vertical singlet excitations -- to study LiPF$_6$, NaPF$_6$, LiFSI, and NaFSI. Compact active spaces were constructed from frontier orbitals, mapped to qubits, and reduced via symmetry tapering and commuting-group measurements to lower sampling cost. Within $\sim$10-qubit models, VQE-qEOM agrees closely with exact diagonalization of the same Hamiltonians, while sample-based quantum diagonalization (SQD) in larger active spaces recovers near-exact (subspace-FCI) energies. The spectra display clear anion and cation trends: PF$_6$ salts exhibit higher first-excitation energies (e.g., LiPF$_6$ $\approx$13.2 eV) and a compact three-state cluster at 12-13 eV, whereas FSI salts show substantially lower onsets ($\approx$8-9 eV) with a near-degenerate (S$_1$,S$_2$) followed by S$_3$ $\sim$1.3 eV higher. Substituting Li$^+$ with Na$^+$ narrows the gap by $\sim$0.4-0.8 eV within each anion family. Converting S$_1$ to wavelengths places the onsets in the deep-UV (LiPF$_6$ $\sim$94 nm; NaPF$_6$ $\sim$100 nm; LiFSI $\sim$141 nm; NaFSI $\sim$148 nm). All results pertain to isolated species or embedded clusters appropriate to the NISQ regime; solvent shifts can be incorporated a posteriori via classical $Δ$-solvation or static embedding. These results demonstrate that current quantum algorithms can deliver chemically meaningful excitation and binding trends for realistic electrolyte motifs and provide quantitative baselines to guide electrolyte screening and design.

cond-mat.mtrl-sci↗

arXiv2025

Integrating Density Functional Theory with Deep Neural Networks for Accurate Voltage Prediction in Alkali-Metal-Ion Battery Materials

Accurate prediction of the voltage of battery materials plays a pivotal role in the advancement of energy storage technologies and the rational design of high-performance cathode materials. In this work, we present a deep neural network (DNN) model, built using PyTorch, to estimate the average voltage of cathode materials across Li-ion, Na-ion, and other alkali-metal-ion batteries. The model is trained on an extensive dataset from the Materials Project, incorporating a wide range of specific structural, physical, chemical, electronic, thermodynamic, and battery descriptors, ensuring a comprehensive representation of material properties. Our model exhibits strong predictive performance, as corroborated by first-principles density functional theory (DFT) calculations. The close alignment between the DNN predictions and the DFT outcomes highlights the robustness and accuracy of our machine learning framework to effectively select and identify viable battery materials. Using this validated model, we successfully proposed novel Na-ion battery compositions, with their predicted behavior confirmed by rigorous computational assessment. By seamlessly integrating data-driven prediction with first-principles validation, this study presents an effective framework that significantly accelerates the discovery and optimization of advanced battery materials, contributing to the development of more reliable and efficient energy storage technologies.

cond-mat.mtrl-sci↗

arXiv2025

Tunable Thermal Expansion in Functionalized 2D Boron Nitride: A First-Principles Investigation

This study investigates the thermal expansion coefficient of two-dimensional (2D) functionalized boron nitride (f-BN) materials using first-principles density functional theory (DFT). Two-dimensional materials, particularly hexagonal boron nitride (h-BN), have attracted significant attention due to their exceptional mechanical, thermal, and electronic properties. However, the influence of functionalization on the thermal expansion behavior remains largely unexplored. In this work, DFT calculations are employed to analyze how different functionalized forms of h-BN impact the thermal expansion of BN sheets. Density functional perturbation theory (DFPT) and the quasiharmonic approximation (QAH) are utilized to determine the thermal expansion coefficient over a range of temperatures. The results reveal that functionalization induces notable modifications in the in-plane thermal expansion of BN, affecting material stability and suggesting potential applications in nanoelectronics and thermal management. This investigation provides critical insights into the tunability of the thermal properties of 2D BN, underscoring its suitability for next-generation flexible and high-performance devices.

cond-mat.mtrl-sci↗

arXiv2020

Growth, Properties, and Applications of Pulsed Laser Deposited Nanolaminate Ti3AlC2 Thin Films

Recently, nanolaminated ternary carbides have attracted immense interest due to the concomitant presence of both ceramic and metallic properties. Here, we grow nanolaminate Ti3AlC2 thin films by pulsed laser deposition on c-axis-oriented sapphire substrates and, surprisingly, the films are found to be highly oriented along the (103) axis normal to the film plane, rather than the (000l) orientation. Multiple characterization techniques are employed to explore the structural and chemical quality of these films, the electrical and optical properties, and the device functionalities. The 80-nm thick Ti3AlC2 film is highly conducting at room temperature (resistivity of 50 micro ohm-cm), and a very-low-temperature coefficient of resistivity. The ultrathin (2 nm) Ti3AlC2 film has fairly good optical transparency and high conductivity at room temperature (sheet resistance of 735 ohm). Scanning tunneling microscopy reveals the metallic characteristics (with finite density of states at the Fermi level) at room temperature. The metal-semiconductor junction of the p-type Ti3AlC2 film and n-Si show the expected rectification (diode) characteristics, in contrast to the ohmic contact behavior in the case of Ti3AlC2 on p-Si. A triboelectric-nanogenerator-based touch-sensing device, comprising of the Ti3AlC2 film, shows a very impressive peak-to-peak open-circuit output voltage of 80 V. These observations reveal that pulsed laser deposited Ti3AlC2 thin films have excellent potential for applications in multiple domains, such as bottom electrodes, resistors for high-precision measurements, Schottky diodes, ohmic contacts, fairly transparent ultrathin conductors, and next-generation biomechanical touch sensors for energy harvesting.

cond-mat.mtrl-sci↗