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quant-ph2026

Conjectured Bounds for 2-Local Hamiltonians via Token Graphs

Anuj Apte, Ojas Parekh, James Sud

We explain how the maximum energy of the Quantum MaxCut, XY, and EPR Hamiltonians on a graph are related to the spectral radii of the token graphs of . From numerical study,…

quant-ph2026

Quantum Approximate Optimization of Integer Graph Problems and Surpassing Semidefinite Programming for Max-k-Cut

Anuj Apte, Sami Boulebnane, Yuwei Jin +3

Quantum algorithms for binary optimization problems have been the subject of extensive study. However, the application of quantum algorithms to integer optimization problems remain…

quant-ph2026

Iterative Interpolation Schedules for Quantum Approximate Optimization Algorithm

Anuj Apte, Shree Hari Sureshbabu, Ruslan Shaydulin +5

Quantum Approximate Optimization Algorithm (QAOA) is a promising quantum heuristic with empirical evidence of speedup over classical state-of-the-art for some problems. QAOA uses a…

quant-ph2026

Regularized Warm-Started Quantum Approximate Optimization and Conditions for Surpassing Classical Solvers on the Max-Cut Problem

Zichang He, Anuj Apte, Brandon Augustino +4

Demonstrating quantum heuristics that outperform strong classical solvers on large-scale optimization remains an open challenge. Here we introduce Regularized Warm-Started QAOA (RW…

quant-ph2025

A 0.8395-approximation algorithm for the EPR problem

Anuj Apte, Eunou Lee, Kunal Marwaha +3

We give an efficient 0.8395-approximation algorithm for the EPR Hamiltonian. Our improvement comes from a new nonlinear monogamy-of-entanglement bound on star graphs and a refined…

quant-ph2025

Mechanisms for Quantum Advantage in Global Optimization of Nonconvex Functions

Dylan Herman, Guneykan Ozgul, Anuj Apte +4

We present new theoretical mechanisms for quantum speedup in the global optimization of nonconvex functions, expanding the scope of quantum advantage beyond traditional tunneling-b…