SearcharxivSearch

arXiv subjects

Ayush Nadiger

Publications and source records attributed to Ayush Nadiger.

3 recordsLinked to original sources

Geometry-controlled correlated electric-field noise in enclosed ion traps from billiard return spectra

We ask how passive conducting geometry determines the spatial structure of electric-field noise seen by trapped ions. From a boundary-potential covariance and the Dirichlet Green function we construct the $N$-ion electric-field cross-spectral matrix and its blockwise motional Kossakowski generator. In a parallel slab, billiard unfolding turns the electrostatic response into a return-depth measure and yields an exact return-pair functional for arbitrary stationary surface spectra. For every finite equal-height ion array, the passive cover increases the normal-field covariance matrix in the positive-semidefinite ordering and decreases the tangential-field covariance matrix in the same ordering: all collective normal-field coordinates acquire more absolute noise, while all collective tangential coordinates acquire less. At $h=2d$ the local-noise single-ion ratios are exactly $\zeta(3)$ and $\eta(3)$. Diagonalizing the equal-frequency covariance identifies collective environmental noise eigenchannels and a geometry-dependent noise rank; within a degenerate frequency block these become the Lindblad jump channels. In a ten-ion example, closing the cover to $h=2d$ lowers the participation rank from 5.61 to 5.11 while increasing the leading channel's share from 23.7% to 28.6%; a primitive M{\o}lmer-S{\o}rensen calculation shows how the projected covariance sets the weak-heating gate exposure. Beyond parallel walls, specular paths emerge as large-$q_z$ saddles of the screened boundary operator. Across 16 curved covers, the fitted electrostatic decay exponent correlates at 0.9991 with the independently computed shortest specular excess length, while separate tests resolve focusing and competing saddles.

quant-ph

Potential Energy Savings from Quantum Computing-Based Route Optimization

We investigate the potential of the Quantum Approximate Optimization Algorithm (QAOA) for reducing energy consumption in route planning, a key challenge in logistics due to the NP-hard nature of the Traveling Salesman and Vehicle Routing Problems. By encoding route optimization as a Quadratic Unconstrained Binary Optimization (QUBO) problem and implementing QAOA circuits at depth p = 3-5 alongside classical baselines of Simulated Annealing (SA) and Genetic Algorithms (GA), we perform systematic benchmarks on Euclidean graphs of sizes N = 5, 10, and 20. Our results demonstrate that QAOA attains higher solution quality with approximation ratios of 0.953 (N = 5), 0.921 (N = 10), and 0.903 (N = 20), outperforming SA and GA by 2.7-4.4%. Wall-clock runtimes for QAOA are 2-3x faster than SA across all tested sizes, and energy consumption measurements reveal a three-order-of-magnitude reduction, remaining in the picojoule range versus nanojoules for classical methods. Translating these gains to real-world logistics suggests an 8.2% improvement in routing efficiency could save approximately 2.62 EJ of fuel annually in the U.S., avoiding nearly 1.94 x 10^8 tonnes of CO2 emissions. These findings highlight QAOA's promise as a fast, energy-efficient optimizer for sustainable logistics applications and underscore its potential role in next-generation fleet-management systems.

cs.ET

Randomized-Accelerated FEAST: A Hybrid Approach for Large-Scale Eigenvalue Problems

We present Randomized-Accelerated FEAST (RA-FEAST), a hybrid algorithm that combines contour-integration-based eigensolvers with randomized numerical linear algebra techniques for efficiently computing partial eigendecompositions of large-scale matrices arising in statistical applications. By incorporating randomized subspace initialization to enable aggressive quadrature reduction and truncated refinement iterations, our method achieves significant computational speedups (up to 38x on sparse graph Laplacian benchmarks at n = 8000) while maintaining high-accuracy approximations to the target eigenspace. We provide a probabilistic error bound for the randomized warmstart, a stability result for inexact FEAST iterations under general perturbations, and a simple complexity model characterizing the trade-off between initialization cost and solver speedup. Empirically, we demonstrate that RA-FEAST can be more than an order of magnitude faster than standard FEAST while preserving accuracy on sparse Laplacian problems representative of modern spectral methods in statistics.

math.NA