arXiv subjects

Shashank Sanjay Bhat

Publications and source records attributed to Shashank Sanjay Bhat.

3 recordsLinked to original sources

arXiv2026

Benchmarking Swarm Optimization Algorithms for Parameter Initialization in the Quantum Approximate Optimization Algorithm

The Quantum Approximate Optimization Algorithm (QAOA) is a prominent variational algorithm for solving combinatorial optimization problems such as the Max Cut problem. A key challenge in QAOA is the efficient identification of variational parameters (γ, \{beta}) that yield high-quality solutions. In this work, we investigate swarm optimization methods as robust strategies for exploring the QAOA parameter space. We evaluate Particle Swarm Optimization (PSO), Fully Informed Particle Swarm Optimization (FIPSO), Quantum Particle Swarm Optimization (QPSO), and an Adam-assisted FIPSO variant on weighted MaxCut instances across multiple system sizes, circuit depths, and noise regimes, including shot noise. Our results show that these methods achieve lower approximation gaps and more stable convergence compared to standard optimizers such as Adam, COBYLA, and SPSA. In particular, we observe that swarm methods maintain superior performance under noisy and shot limited conditions. These findings suggest that population based search is effective for navigating the complex QAOA landscape and is a promising approach for parameter optimization in near-term quantum algorithms.

quant-ph↗

arXiv2026

Encoding Matters: Benchmarking Binary and D-ary Representations for Quantum Combinatorial Optimization

Combinatorial optimization problems are typically formulated using Quadratic Unconstrained Binary Optimization (QUBO), where constraints are enforced through penalty terms that introduce auxiliary variables and rapidly increase Hamiltonian complexity, limiting scalability on near term quantum devices. In this work, we systematically study Quadratic Unconstrained D-ary Optimization (QUDO) as an alternative formulation in which decision variables are encoded directly in higher dimensional Hilbert spaces. We demonstrate that QUDO naturally captures structural constraints across a range of problem classes, including the Traveling Salesman Problem, two variants of the Vehicle Routing Problem, graph coloring, job scheduling, and Max-K-Cut, without the need for extensive penalty constructions. Using a qudit-level implementation of the Quantum Approximate Optimization Algorithm (qudit QAOA), we benchmark these formulations against their binary QUBO counterparts and exact classical solutions. Our study show consistently improved approximation ratios and substantially reduced computational overhead at comparable circuit depths, highlighting QUDO as a scalable and expressive representation for quantum combinatorial optimization.

quant-ph↗

arXiv2023

Investigation of a Machine learning methodology for the SKA pulsar search pipeline

The SKA pulsar search pipeline will be used for real time detection of pulsars. Modern radio telescopes such as SKA will be generating petabytes of data in their full scale of operation. Hence experience-based and data-driven algorithms become indispensable for applications such as candidate detection. Here we describe our findings from testing a state of the art object detection algorithm called Mask R-CNN to detect candidate signatures in the SKA pulsar search pipeline. We have trained the Mask R-CNN model to detect candidate images. A custom annotation tool was developed to mark the regions of interest in large datasets efficiently. We have successfully demonstrated this algorithm by detecting candidate signatures on a simulation dataset. The paper presents details of this work with a highlight on the future prospects.

astro-ph.IM↗