arXiv subjects

Muhammad Faizan

Publications and source records attributed to Muhammad Faizan.

5 recordsLinked to original sources

arXiv2026

Robust AI-Driven Discovery of Electronic Metal Phosphide Semiconductors

Metal phosphides have diverse bonding motifs and coordination environments, making them promising for optoelectronic and thermoelectric applications, but their chemical space remains underexplored. Here we report an AI-driven high-throughput discovery workflow that combines generative materials design, machine-learning interatomic potentials, and targeted density functional theory (DFT) calculations. ICSD-derived Wyckoff-site substitution and MatterGen-based conditional structure generation are used to expand the candidate space beyond existing phosphide databases. A domain-finetuned DPA3 machine-learning potential then enables efficient prescreening of thermodynamic and dynamical stability before DFT validation. This workflow identifies 3,574 previously unreported stable phosphide structures, including 196 semiconductors with HSE06 band gaps of 0-3.0 eV. By screening these new semiconductors together with experimentally known phosphide semiconductors, we identify 30 promising optoelectronic candidates and 26 promising thermoelectric candidates, including seven newly discovered optoelectronic materials and eight newly discovered thermoelectric materials. These results provide a candidate pool for experimental synthesis and show that combining generative AI with machine-learning interatomic potentials can accelerate the discovery of functional semiconductor materials.

cond-mat.mtrl-sci↗

arXiv2025

Analysis of the Bernstein--Vazirani Algorithm in the presence of Pauli Noise

We analytically investigate the robustness of the Bernstein--Vazirani algorithm in the presence of bit flip, phase flip, and depolarizing noise using the density matrix formalism. We derive the exact expressions for the algorithm's success probability as a function of the error probability $\boldsymbol{p}$ and number of qubits $\boldsymbol{n}$. The analysis compares the three noise models and reveals how performance degrades with increasing system size under standard Pauli noise models. Most importantly, we show that scaling up quantum systems without simultaneously improving qubit quality leads to a sharp decline in ideal quantum speedup.

quant-ph↗

arXiv2024

Quasi Inverse of Qubit Channels for Mixed States

We found the quasi inverse of qubit channels as a unitary map, $\mathcal{E}^i$, by minimizing the average trace distance between the input state to the channel and the output of the quasi inverse channel for arbitrary qubit channel $\mathcal{E}$ and for arbitrary input states. The channel $\mathcal{E}$ was assumed completely positive and trace-preserving. To find the quasi inverse for mixed states, we proposed an alternative definition of the quasi inverse based on the mean square of the trace distance (MSTD) of a channel. The definition based on the trace distance allowed easy generalization of the quasi inverse to mixed input states. The quasi inverse of the Pauli, generalized amplitude damping, mixed unitary, and tetrahedron channels calculated based on the MSTD agreed with the one computed using average fidelity in the special case of input states being pure.

quant-ph↗

arXiv2023

Simulation and analysis of quantum phase estimation algorithm in the presence of incoherent quantum noise channels

The quantum phase estimation (QPE) is one of the fundamental algorithms based on the quantum Fourier transform. It has applications in order-finding, factoring, and finding the eigenvalues of unitary operators. The major challenge in running QPE and other quantum algorithms is the noise in quantum computers. In the present work, we study the impact of incoherent noise on QPE, modeled as trace-preserving and completely positive quantum channels. Different noise models such as depolarizing, phase flip, bit flip, and bit-phase flip are taken to understand the performance of the QPE in the presence of noise. The simulation results indicate that the standard deviation of the eigenvalue of the unitary operator has strong exponential dependence upon the error probability of individual qubits. However, the standard deviation increases only linearly with the number of qubits for fixed error probability when that error probability is small.

quant-ph↗

arXiv2022

Inorganic Crystal Structure Prototype Database based on Unsupervised Learning of Local Atomic Environments

Recognition of structure prototypes from tremendous known inorganic crystal structures has been an important subject beneficial for material science research and new materials design. The existing databases of inorganic crystal structure prototypes were mostly constructed by classifying materials in terms of the crystallographic space group information. Herein, we employed a distinct strategy to construct the inorganic crystal structure prototype database, relying on the classification of materials in terms of local atomic environments (LAE) accompanied by unsupervised machine learning method. Specifically, we adopted a hierarchical clustering approach onto all experimentally known inorganic crystal structures data to identify structure prototypes. The criterion for hierarchical clustering is the LAE represented by the state-of-the-art structure fingerprints of the improved bond-orientational order parameters and the smooth overlap of atomic positions. This allows us to build up a LAE-based Inorganic Crystal Structure Prototype Database (LAE-ICSPD) containing 15,613 structure prototypes with defined stoichiometries. In addition, we have developed a Structure Prototype Generator Infrastructure (SPGI) package, which is a useful toolkit for structure prototype generation. Our developed SPGI toolkit and LAE-ICSPD are beneficial for investigating inorganic materials in a global way as well as accelerating materials discovery process in the data-driven mode.

cond-mat.mtrl-sci↗