arXiv subjects

Vivek Dixit

Publications and source records attributed to Vivek Dixit.

9 recordsLinked to original sources

arXiv2026

Towards Natural Gas Contract Selection via Quantum-Guided Independent Set Reduction

Selecting mutually compatible natural gas transportation contracts is a practically important optimization task in which operators must choose from many candidate agreements subject to temporal, infrastructural, and flow-related constraints. As the number of candidates grows, the resulting search space becomes difficult to explore exhaustively. We study a pairwise abstraction of this task, formulated as a Maximum Clique problem on a contract-compatibility graph, or equivalently as a Maximum Independent Set (MIS) problem on the complement graph. Building on recent work, this paper studies a quantum-classical framework for solving large-scale MIS instances within the limitations of noisy quantum hardware. The approach combines iterative classical graph reduction with quantum-guided optimization to progressively simplify the search space while maintaining high solution quality. This enables large candidate spaces to be reduced to smaller subproblems that are more suitable for execution on current quantum computers. We evaluate the approach on fifteen benchmark instances from the Quantum Optimization Benchmarking Library (QOBLIB), obtaining an average approximation ratio of 0.996 and recovering optimal solutions for fourteen instances, including graphs with up to 186 vertices. We further evaluate the algorithm on six synthetic pairwise contract-compatibility graphs containing up to 900 contracts, where the proposed method achieves an average approximation ratio of 0.989 and obtains optimal solutions in four cases. These experiments demonstrate the ability of the hybrid MIS solver to reduce industrially motivated graphs. The pairwise abstraction serves as the first step of a two-stage screening procedure that narrows the candidate contracts to a smaller set of mutually compatible ones, which can then be verified against pipeline-capacity constraints.

quant-ph↗

arXiv2026

A Quantum Approach to Stochastic Optimization in Insurance Underwriting

The presence of stochastic elements in combinatorial optimization problems makes them particularly challenging, as such problems quickly become intractable for classical computers even at relatively small sizes. In this work, we propose a novel quantum-classical hybrid scheme for solving a class of stochastic optimization problems known as chance-constrained knapsack problems, in which item weights follow probability distributions and constraints may be violated within a specified risk tolerance. Our method employs knapsack-specific QAOA-based circuits to generate samples which, when combined with a new self-consistent classical recovery scheme introduced in this work, produce high-quality solutions. Experiments carried out on IBM Heron processors, using circuits with depths up to 177 and comprising 3443 gates acting on as many as 150 qubits, yield solutions that indicate performance comparable to classical optimization schemes. The proposed quantum-classical scheme paves the way to tackling such problems, with the potential to outperform approaches that rely solely on classical computation.

quant-ph↗

arXiv2024

Detecting Thermodynamic Phase Transition via Explainable Machine Learning of Photoemission Spectroscopy

Identifying thermodynamic signatures of electronic phases, such as superconductivity, is challenging in low-dimensional materials due to strong fluctuations and low probing volume. Spectroscopic methods are often used to identify new bulk phases, but their main measurable quantity -- electronic energy gaps -- is no longer an effective order parameter in low-dimensional and fluctuating systems. Combining angle-resolved photoemission with a domain-adversarial neural network, we report a data-driven method to identify thermodynamic phase transitions solely based on single-particle spectra. We demonstrate 97.6$\%$ accuracy in cuprate superconductor Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ with strong superconducting fluctuations. This model notably compensates for the scarcity of experimental data by leveraging virtually inexhaustible simulated data. Further, its explainability reveals the crucial role of in-gap spectral weight in detecting phase fluctuations and thermodynamic transitions. Our work pinpoints the spectroscopic signatures of fluctuating orders and enables using spectroscopy for machine-learning-assisted material discovery for low-dimensional and strong coupling systems.

cond-mat.supr-con↗

arXiv2022

First principle investigations of the structural, electronic, and phase stability in 2D layered ZnSb

Recently, the two dimensional (2D) materials have become a potential candidates for various technological applications in spintronics and optoelectronics. In the present study, the structural, electronic, and phase stability of 2D layered ZnSb compounds of four different phases viz. wurzite(w), tetragonal (t), hexagonal (h), and orthorhombic (o) have been tuned using the first principle calculations based on density functional theory (DFT). We invoked the Perdew-Burke-Ernzerhof (PBE) functional and the projected augmented wave (PAW) method during all the calculations. Based on our numerical results, we predicted the novel tetragonal phase as stable phase of ZnSb next to existing orthorhombic structure. We reported the pressure induced phase transition between orthorhombic to tetragonal phase at 12.48 GPa/atom. The projected density of states indicates the strong p-d hybridization between Sb-5p and Zn-3d states confirming the nature of strong covalent bonding between them. The electronic band structures suggest that t-ZnSb, w-ZnSb, and h-ZnSb are metallic in nature whereas o-ZnSb is semiconducting with narrow band gap of 0.03 eV using PBE. We predicted the possibility of extracting the two dimensional (2D) monolayer sheet in t-ZnSb and o-ZnSb according to the exfoliation energy criterion. In addition, the 2D monolayer (ML) of o-ZnSb has been predicted to be dynamically stable but that of t-ZnSb is not stable as manifested in phonon dispersion bands. Surprisingly, the semiconducting band gap nature of o-ZnSb changes from indirect and narrow to direct and sizable while going from 3D bulk to 2D ML structure. Further, we estimated the value of work functions for the surfaces of t-ZnSb and o-ZnSb as 4.61 eV and 4.04 eV respectively. Such materials can find the niche applications in next generation electronic devices utilizing 2D hetero-structures.

physics.comp-ph↗

arXiv2021

Prime Factorization Using Quantum Variational Imaginary Time Evolution

The road to computing on quantum devices has been accelerated by the promises that come from using Shor's algorithm to reduce the complexity of prime factorization. However, this promise hast not yet been realized due to noisy qubits and lack of robust error correction schemes. Here we explore a promising, alternative method for prime factorization that uses well-established techniques from variational imaginary time evolution. We create a Hamiltonian whose ground state encodes the solution to the problem and use variational techniques to evolve a state iteratively towards these prime factors. We show that the number of circuits evaluated in each iteration scales as O(n^{5}d), where n is the bit-length of the number to be factorized and $d$ is the depth of the circuit. We use a single layer of entangling gates to factorize several numbers represented using 7, 8, and 9-qubit Hamiltonians. We also verify the method's performance by implementing it on the IBMQ Lima hardware.

quant-ph↗

arXiv2021

Training a quantum annealing based restricted Boltzmann machine on cybersecurity data

We present a real-world application that uses a quantum computer. Specifically, we train a RBM using QA for cybersecurity applications. The D-Wave 2000Q has been used to implement QA. RBMs are trained on the ISCX data, which is a benchmark dataset for cybersecurity. For comparison, RBMs are also trained using CD. CD is a commonly used method for RBM training. Our analysis of the ISCX data shows that the dataset is imbalanced. We present two different schemes to balance the training dataset before feeding it to a classifier. The first scheme is based on the undersampling of benign instances. The imbalanced training dataset is divided into five sub-datasets that are trained separately. A majority voting is then performed to get the result. Our results show the majority vote increases the classification accuracy up from 90.24% to 95.68%, in the case of CD. For the case of QA, the classification accuracy increases from 74.14% to 80.04%. In the second scheme, a RBM is used to generate synthetic data to balance the training dataset. We show that both QA and CD-trained RBM can be used to generate useful synthetic data. Balanced training data is used to evaluate several classifiers. Among the classifiers investigated, K-Nearest Neighbor (KNN) and Neural Network (NN) perform better than other classifiers. They both show an accuracy of 93%. Our results show a proof-of-concept that a QA-based RBM can be trained on a 64-bit binary dataset. The illustrative example suggests the possibility to migrate many practical classification problems to QA-based techniques. Further, we show that synthetic data generated from a RBM can be used to balance the original dataset.

quant-ph↗

arXiv2020

Training and Classification using a Restricted Boltzmann Machine on the D-Wave 2000Q

Restricted Boltzmann Machine (RBM) is an energy based, undirected graphical model. It is commonly used for unsupervised and supervised machine learning. Typically, RBM is trained using contrastive divergence (CD). However, training with CD is slow and does not estimate exact gradient of log-likelihood cost function. In this work, the model expectation of gradient learning for RBM has been calculated using a quantum annealer (D-Wave 2000Q), which is much faster than Markov chain Monte Carlo (MCMC) used in CD. Training and classification results are compared with CD. The classification accuracy results indicate similar performance of both methods. Image reconstruction as well as log-likelihood calculations are used to compare the performance of quantum and classical algorithms for RBM training. It is shown that the samples obtained from quantum annealer can be used to train a RBM on a 64-bit `bars and stripes' data set with classification performance similar to a RBM trained with CD. Though training based on CD showed improved learning performance, training using a quantum annealer eliminates computationally expensive MCMC steps of CD.

cs.LG↗

arXiv2018

Correlation between site preference and magnetic properties of Zn-Sn-substituted strontium hexaferrite

The site preference and magnetic properties of Zn, Sn and Zn-Sn substituted M-type strontium hexaferrite (SrFe$_{12}$O$_{19}$) have been investigated using first-principles total energy calculations based on density functional theory. The site occupancy of substituted atoms were estimated by calculating the substitution energies of different configurations. The distribution of different configurations during the annealing process at high temperature was determined using the formation probabilities of configurations to calculate magnetic properties of substituted strontium hexaferrite. We found that the magnetization and magnetocrystalline anisotropy are closely related to the distributions of Zn-Sn ions on the five Fe sites. Our calculation show that in SrFe$_{11.5}$Zn$_{0.5}$O$_{19}$, Zn atoms prefer to occupy $4f_1$, $12k$, and $2a$ sites with occupation probability of 78%, 19% and 3%, respectively, while in SrFe$_{11.5}$SnO$_{19}$, Sn atoms occupy the $12k$ and $4f_2$ sites with occupation probability of 54% and 46%, respectively. We also found that in SrFe$_{11}$Zn$_{0.5}$Sn$_{0.5}$O$_{19}$, (Zn,Sn) atom pairs prefer to occupy the ($4f_1$, $4f_2$), ($4f_1$, $12k$) and ($12k$, $12k$) sites with occupation probability of 82%, 8% and 6%, respectively. Our calculation shows that the increase of magnetization and the reduction of magnetic anisotropy in Zn-Sn substituted M-type strontium hexaferrite as observed experimentally is due to the occupation of (Zn,Sn) pairs at the ($4f_1$, $4f_2$) sites.

cond-mat.mtrl-sci↗

arXiv2015

Site occupancy and magnetic properties of Al-substituted M-type strontium hexaferrite

We use first-principles total-energy calculations based on density functional theory to study the site occupancy and magnetic properties of Al-substituted $M$-type strontium hexaferrite SrFe$_{12-x}$Al$_{x}$O$_{19}$ with $x=0.5$ and $x=1.0$. We find that the non-magnetic Al$^{3+}$ ions preferentially replace Fe$^{3+}$ ions at two of the majority spin sites, $2a$ and $12k$, eliminating their positive contribution to the total magnetization causing the saturation magnetization $M_s$ to be reduced as Al concentration $x$ is increased. Our formation probability analysis further provides the explanation for increased magnetic anisotropy field when the fraction of Al is increased. Although Al$^{3+}$ ions preferentially occupy the $2a$ sites at a low temperature, the occupation probability of the $12k$ site increases with the rise of the temperature. At a typical annealing temperature ($> 700\,^{\circ}{\rm C}$) Al$^{3+}$ ions are much more likely to occupy the $12k$ site than the $2a$ site. Although this causes the magnetocrystalline anisotropy $K_1$ to be reduced slightly, the reduction in $M_s$ is much more significant. Their combined effect causes the anisotropy field $H_a$ to increase as the fraction of Al is increased, consistent with recent experimental measurements.

cond-mat.mtrl-sci↗