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quant-ph2026

The Expressive Power of Constrained QAOA: What You Might Have MISsed

Boris Tsvelikhovskiy, Bao Bach, Ilya Safro

Feasibility-preserving mixer Hamiltonians offer an attractive alternative to penalty encodings for constrained quantum optimization, yet how enforcing constraints alters the state…

quant-ph2026

Warm-Starting MaxCut Relaxation via Low-Depth Quantum Approximate Optimization Algorithm

Bao G. Bach, Ilya Safro, Filip B. Maciejewski

Quantum optimization has attracted growing interest as quantum hardware continues to improve, yet state-of-the-art classical solvers remain a formidable benchmark for practical uti…

quant-ph2026

Exponentially many initializations to avoid barren plateaus

Ankit Kulshrestha, Ricard Puig, Diego García-Martín +4

Barren plateaus are stated as an average-case phenomenon: pick an ansatz, initialize it naively, and concentration follows. This has led to the common view that a potential cure fo…

quant-ph2026

Scaling Quantum Optimization for Unit Commitment via Pauli Correlation Encoding

Kien X. Nguyen, Ilya Safro, Xiaoyuan Liu

Unit commitment is an important optimization problem in power system operations, classified as NP-hard. This paper presents a hybrid quantum-classical method for the unit commitmen…

quant-ph2026

QAP-Router: Tackling Qubit Routing as Dynamic Quadratic Assignment with Reinforcement Learning

Kien X. Nguyen, Ankit Kulshrestha, Ilya Safro +1

Qubit routing is a fundamental problem in quantum compilation, known to be NP-hard. Its dynamic nature makes local routing decisions propagate and compound over time, making global…

quant-ph2026

Quantum Hypergraph Partitioning

Cameron Ibrahim, Bao G. Bach, Jad Salem +4

Quantum optimization algorithms are inherently probabilistic, yet they are most often used to search for a single high-quality solution. In this paper, we instead study hypergraph…

quant-ph2026

Scaling Qubit Mapping and Routing With Position Graph Abstraction and Memoization

Brent Russon, Bao Bach, Ed Younis +1

Scalable qubit mapping and routing remain major bottlenecks in quantum compilation, especially for Trapped-Ion Quantum Charge-Coupled device (TI-QCCD) architectures, where qubit in…

quant-ph2026

QUACOD: Quantum Optimization via Coordinate Descent for Scalable Drone Scheduling

Van-Quang-Huy Nguyen, Hoang-Quan Nguyen, Samee U. Khan +2

Quantum computing has demonstrated its potential to solve various optimization problems, including drone scheduling, which is important not only for drone delivery but also for log…

quant-ph2026

Q3SAT-GPT: A Generative Model for Discovering Quantum Circuits for the 3-SAT Problem

Pratim Ugale, Ilya Tyagin, Karunya Shirali +2

This work introduces Q3SAT-GPT, a generative model for discovering quantum circuits for the Max-E3-SAT problem. Our method learns from high-performing QAOA-style ansätze to directl…

quant-ph2026

Graph-Conditioned Meta-Optimizer for QAOA Parameter Generation on Multiple Problem Classes

Kien X. Nguyen, Ilya Safro

We study parameter transferability for the Quantum Approximate Optimization Algorithm (QAOA) across multiple combinatorial optimization problem classes from a parameter generation…

quant-ph2026

Learning Cut Distributions with Quantum Optimization

Bao Bach, Cameron Ibrahim, Reuben Tate +3

Many combinatorial optimization problems admit a maximin fairness variant, where the aim is to find a distribution over possible solutions which maximizes an expected worst-case ou…