Urban areas Archives - IEEE Transactions on Quantum Engineering

Advanced Quantum Annealing for the Bi-Objective Traveling Thief Problem: An ε-Constraint-Based Approach

March 16, 2026Nguyen Hoang Viet, Nguyen Xuan Tung, Trinh Van Chien, Won-Joo HwangOpen Access, Quantum ComputingAnnealing, Hardware, Interference, Linear programming, Optimization, Quantum annealing, Scalability, Transforms, Traveling salesman problems, Urban areas

Abstract: This paper addresses the Bi-Objective Traveling Thief Problem (BI-TTP), a challenging multi-objective optimization problem that requires the simultaneous optimization of travel cost and item profit. Conventional methods for the BI-TTP often face severe scalability issues due to the complex interdependence between routing and packing decisions, as well as the inherent complexity and large problem […]

Encoder Circuit Optimization for Nonbinary Quantum Error Correction Codes in Prime Dimensions: An Algorithmic Framework

March 2, 2026Aditya Sodhani, Keshab K. ParhiQuantum InformationCodes, Encoding, Galois fields, Hardware, Hilbert space, Logic gates, Quantum system, Qubit, Urban areas, Vectors

Abstract: Quantum computers are a revolutionary class of computational platforms with applications in combinatorial and global optimization, machine learning, and other domains involving computationally hard problems. While these machines typically operate on qubits—quantum information elements that can occupy superpositions of the basis |0⟩ and |1⟩ states—recent advances have demonstrated the practical implementation of higher dimensional […]

Two-Step Quantum Search Algorithm for Solving Traveling Salesman Problems

March 6, 2025Rei Sato, Cui Gordon, Kazuhiro Saito, Hideyuki Kawashima, Tetsuro Nikuni, Shohei WatabeQuantum ComputingComplexity theory, Costs, Encoding, Heuristic algorithms, Optimization, Quantum algorithm, Quantum circuit, Search problems, Traveling salesman problems, Urban areas

Quantum search algorithms, such as Grover’s algorithm, are anticipated to efficiently solve constrained combinatorial optimization problems. However, applying these algorithms to the traveling salesman problem (TSP) on a quantum circuit presents a significant challenge. Existing quantum search algorithms for the TSP typically assume that an initial state—an equal superposition of all feasible solutions satisfying the […]

Accelerating Grover Adaptive Search: Qubit and Gate Count Reduction Strategies With Higher Order Formulations

April 25, 2024Yuki Sano, Kosuke Mitarai, Naoki Yamamoto, Naoki Ishikawa,Quantum ComputingEncoding, Logic gates, Optimization, Quantum computing, Qubit, Urban areas, Vectors

Grover adaptive search (GAS) is a quantum exhaustive search algorithm designed to solve binary optimization problems. In this article, we propose higher order binary formulations that can simultaneously reduce the numbers of qubits and gates required for GAS. Specifically, we consider two novel strategies: one that reduces the number of gates through polynomial factorization, and […]