arXiv subjects

Kushagra Agarwal

Publications and source records attributed to Kushagra Agarwal.

3 recordsLinked to original sources

arXiv2026

DF-SQD: Deterministic Fields for Sampling-Based Quantum Diagonalization

Sampling-based quantum diagonalization method exploits Quantum-centric supercomputing platforms to sample bitstrings for Hamiltonian projection on a quantum computer, and then classically diagonalize the Hamiltonian to estimate the eigenvalues and eigenvectors. In current quantum devices an algorithm is useful when shallow quantum circuits with error mitigation support can discover better results while having either a proof of convergence or some method to explain trust in experiment. In this paper, we introduce DF-SQD, a hybrid algorithm that derives deterministic auxiliary-field circuits from selected double-factorization leaves of the two-electron tensor. The circuits propose occupation-number configurations, while selected configuration interaction evaluates the original active-space Hamiltonian and can recentre subsequent proposal rounds. On N2 (32 qubits; 6-31G basis) and a 40-qubit [Fe2S2(SCH3)4]2- active-space Hamiltonian, we show that DF-SQD improves the energy obtained from sampled determinant spaces while using shallow number-preserving circuits in both simulator and hardware runs. For N2, DF-SQD is 45x more accurate with a 11.23\% smaller subspace, and due to its ability to sample better bitstrings at lesser shots it is 2.93x faster than SQD in quantum devices. For the iron-sulfur cluster, DF-SQD generated a subspace dimension of 221M with 400K shots, while SQD needed 1.5M shots to generate a 238M subspace, thus we have better subspace recovery evident from the hardware at 3.75x reduced shots. At a matched 50M subspace dimension, DF-SQD is 1.32x more accurate (achieves a 24.5\% relative error reduction over standard SQD). So overall, our method is able to discover better results with shallower circuits, is sample efficient, uses configuration recovery (so has targeted error mitigation) and we have empirical convergence observation.

quant-ph↗

arXiv2026

Optimization of photonic waveguide bends for low index contrast material platforms

Compact and low loss waveguide bends are vital for enhancing the integration density of on chip photonic devices. The bend loss becomes extremely critical, particularly for compact bends in low index contrast platforms such as Indium Phosphide. To address this issue, we introduce an elliptical width modulated bend, which exhibits low loss. For example, at 6 micrometers radius bend loss for elliptical width modulated bend is 0.22 decibels per 90 degrees, resulting in about 40 percent and 27 per cent reduction compared to conventional circular bends and circular width modulated bends, respectively with only a 15 per cent increase in footprint providing a better loss area trade off. Additionally, conventional effective index based analytical models do not account for the impact of width modulation. In this work, we develop an enhanced analytical bend loss formulation that incorporates these effects and validates its accuracy through close agreement with full three dimensional Finite Difference Time Domain simulations.

physics.optics↗

arXiv2024

Link Prediction for Social Networks using Representation Learning and Heuristic-based Features

The exponential growth in scale and relevance of social networks enable them to provide expansive insights. Predicting missing links in social networks efficiently can help in various modern-day business applications ranging from generating recommendations to influence analysis. Several categories of solutions exist for the same. Here, we explore various feature extraction techniques to generate representations of nodes and edges in a social network that allow us to predict missing links. We compare the results of using ten feature extraction techniques categorized across Structural embeddings, Neighborhood-based embeddings, Graph Neural Networks, and Graph Heuristics, followed by modeling with ensemble classifiers and custom Neural Networks. Further, we propose combining heuristic-based features and learned representations that demonstrate improved performance for the link prediction task on social network datasets. Using this method to generate accurate recommendations for many applications is a matter of further study that appears very promising. The code for all the experiments has been made public.

cs.SI↗