arXiv subjects

Ali Rezaei

Publications and source records attributed to Ali Rezaei.

6 recordsLinked to original sources

arXiv2025

Low-Level and NUMA-Aware Optimization for High-Performance Quantum Simulation

Scalable classical simulation of quantum circuits is crucial for advancing quantum algorithm development and validating emerging hardware. This work focuses on performance enhancements through targeted low-level and NUMA-aware tuning on a single-node system, thereby not only advancing the efficiency of classical quantum simulations but also establishing a foundation for scalable, heterogeneous implementations that bridge toward noiseless quantum computing. Although few prior studies have reported similar hardware-level optimizations, such implementations have not been released as open-source software, limiting independent validation and further development. We introduce an open-source, high-performance extension to the QuEST state vector simulator that integrates state-of-the-art low-level and NUMA-aware optimizations for modern processors. Our approach emphasizes locality-aware computation and incorporates hardware-specific techniques including NUMA-aware memory allocation, thread pinning, AVX-512 vectorization, aggressive loop unrolling, and explicit memory prefetching. Experiments demonstrate substantial speedups--5.5-6.5x for single-qubit gate operations, 4.5x for two-qubit gates, 4x for Random Quantum Circuits (RQC), and 1.8x for the Quantum Fourier Transform (QFT). Algorithmic workloads further achieve 4.3-4.6x acceleration for Grover and 2.5x for Shor-like circuits. These results show that systematic, architecture-aware tuning can significantly extend the practical simulation capacity of classical quantum simulators on current hardware.

quant-ph↗

arXiv2023

Tilted Dirac Cones in Two-Dimensional Materials: Impact on Electron Transmission and Pseudospin Dynamics

This study is devoted to the profound implications of tilted Dirac cones on the quantum transport properties of two-dimensional (2D) Dirac materials. These materials, characterized by their linear conic energy dispersions in the vicinity of Dirac points, exhibit unique electronic behaviors, including the emulation of massless Dirac fermions and the manifestation of relativistic phenomena such as Klein tunneling. Expanding beyond the well-studied case of graphene, the manuscript focuses on materials with tilted Dirac cones, where the anisotropic and tilted nature of the cones introduces additional complexity and richness to their electronic properties. The investigation begins by considering a heterojunction of 2D Dirac materials, where electrons undergo quantum tunneling between regions with upright and tilted Dirac cones. The role of tilt in characterizing the transmission of electrons across these interfaces is thoroughly examined, shedding light on the influence of the tilt parameter on the transmission probability and the fate of the pseudospin of the Dirac electrons, particularly upon a sudden change in the tilting. We also investigate the probability of reflection and transmission from an intermediate slab with arbitrary subcritical tilt, focusing on the behavior of electron transmission across regions with varying Dirac cone tilts. The study demonstrates that for certain thicknesses of the middle slab, the transmission probability is equal to unity, and both reflection and transmission exhibit periodic behavior with respect to the slab thickness.

cond-mat.mes-hall↗

arXiv2023

Fully Convolutional Generative Machine Learning Method for Accelerating Non-Equilibrium Greens Function Simulations

This work describes a novel simulation approach that combines machine learning and device modelling simulations. The device simulations are based on the quantum mechanical non-equilibrium Greens function (NEGF) approach and the machine learning method is an extension to a convolutional generative network. We have named our new simulation approach ML-NEGF and we have implemented it in our in-house simulator called NESS (nano-electronics simulations software). The reported results demonstrate the improved convergence speed of the ML-NEGF method in comparison to the standard NEGF approach. The trained ML model effectively learns the underlying physics of nano-sheet transistor behaviour, resulting in faster convergence of the coupled Poisson-NEGF simulations. Quantitatively, our ML- NEGF approach achieves an average convergence acceleration of 60%, substantially reducing the computational time while maintaining the same accuracy.

cs.CE↗

arXiv2019

Phase-controlled spin and charge currents in superconductor-ferromagnet hybrids

We investigate spin-dependent quasiparticle and Cooper-pair transport through a central node interfaced with two superconductors and two ferromagnets. We demonstrate that voltage biasing of the ferromagnetic contacts induces superconducting triplet correlations on the node and reverses the supercurrent flowing between the two superconducting contacts. We further find that such triplet correlations can mediate a tunable spin current flow into the ferromagnetic contacts. Our key finding is that unequal spin-mixing conductances for the two interfaces with the ferromagnets result in equal-spin triplet correlations on the node, detectable via a net charge current between the two magnets. Our proposed device thus enables the generation, control, and detection of the typically elusive equal-spin triplet Cooper pairs.

cond-mat.supr-con↗

arXiv2018

Spin splitting induced in a superconductor by an antiferromagnetic insulator

Inspired by recent feats in exchange coupling antiferromagnets to an adjacent material, we demonstrate the possibility of employing them for inducing spin splitting in a superconductor, thereby avoiding the detrimental, parasitic effects of ferromagnets employed to this end. We derive the Gor'kov equation for the matrix Green's function in the superconducting layer, considering a microscopic model for its disordered interface with a two-sublattice magnetic insulator. We find that an antiferromagnetic insulator with effectively uncompensated interface induces a large, disorder-resistant spin splitting in the adjacent superconductor. In addition, we find contributions to the self-energy stemming from the interfacial disorder. Within our model, these mimic impurity and spin-flip scattering, while another breaks the symmetries in particle-hole and spin spaces. The latter contribution, however, drops out in the quasi-classical approximation and thus, does not significantly affect the superconducting state.

cond-mat.mes-hall↗

arXiv2018

Application of the Fast Multipole Fully Coupled Poroelastic Displacement Discontinuity Method to Hydraulic Fracturing Problems

In this study, a fast multipole method (FMM) is used to decrease the computational time of a fully-coupled poroelastic hydraulic fracture model with a controllable effect on its accuracy. The hydraulic fracture model is based on the poroelastic formulation of the displacement discontinuity method (DDM) which is a special formulation of the boundary element method (BEM). DDM is a powerful and efficient method for problems involving fractures. However, this method becomes slow as the number of temporal, or spatial elements increases, or necessary details such as poroelasticity, that makes the solution history-dependent, are added to the model. FMM is a technique to expedite matrix-vector multiplications within a controllable error without forming the matrix explicitly. Fully-coupled poroelastic formulation of DDM involves the multiplication of a dense matrix with a vector in several places. A crucial modification to DDM is suggested in two places in the algorithm to leverage the speed efficiency of FMM for carrying out these multiplications. The first modification is in the time-marching scheme, which accounts for the solution of previous time steps to compute the current time step. The second modification is in the generalized minimal residual method (GMRES) to iteratively solve for the problem unknowns. Several examples are provided to show the efficiency of the proposed approach in problems with large degrees of freedom (in time and space). Examples include hydraulic fracturing of a horizontal well and randomly distributed pressurized fractures at different orientations with respect to horizontal stresses. The results are compared to the conventional DDM in terms of computational processing time and accuracy. Accordingly, the proposed algorithm may be used for fracture propagation studies while substantially reducing the processing time with a controllable error.

math.NA↗