Hardware Archives - IEEE Transactions on Quantum Engineering

Quantum-Based Resilient Routing in Networks: Minimizing Latency Under Dual-Link Failures

June 11, 2026Maher Harb, Nader Foroughi, Matt Stehman, Bob Lutz, Nati Erez, Erik GarcellEarly Access, Open AccessAlgorithms, Costing, Costs, Hardware, Joining processes, Modeling, Optimization, Probability, Qubit, Timing

Abstract: Network optimization problems represent large combinatorial search spaces that grow exponentially with network size, making them computationally intensive to solve. This paper addresses the latency-resilient Layer 3 routing optimization problem in telecommunications networks with predefined Layer 1 optical links. We formulate this problem as a graph-based optimization problem with the objective of minimizing latency, […]

Quantum Circuit-Based Adaptation for Credit Risk Analysis

May 7, 2026Halima Giovanna Ahmad, Alessandro Sarno, Mehdi El Bakraoui, Carlo Cosenza, Clément Bésoin, Francesca Cibrario, Valeria Zaffaroni, Giacomo Ranieri, Roberto Bertilone, Viviana Stasino, Pasquale Mastrovito, Francesco Tafuri, Davide Massarotti, Leonardo Chabbra, Davide CorbellettoEarly Access, Open AccessAlgorithms, Circuits, Gaussian distribution, Hardware, Modeling, Optimization, Probability, Quantum circuit, Qubit, Registers

Abstract: Noisy and Intermediate-Scale Quantum, or NISQ, processors are sensitive to noise, prone to quantum decoherence, and are not yet capable of continuous quantum error correction for fault-tolerant quantum computation. Hence, quantum algorithms designed in the pre-fault-tolerant era cannot neglect the noisy nature of the hardware, and investigating the relationship between quantum hardware performance and […]

InterQnet: A Heterogeneous Full-Stack Approach to Co-designing Scalable Quantum Networks

March 18, 2026Joaquin Chung, Daniel Dilley, Ely Eastman, Alvin Gonzales, Kara Hokenstad, Md Shariful Islam, Varun Jorapur, Joseph Petrullo, Andy C. Y. Li, Bikun Li, Vasileios Niaouris, Anirudh Ramesh, Ansh Singal, Zeyu Ye, Caitao Zhan, Michael Bishof, Eric Chitambar, Jacob P. Covey, Alan Dibos, Xu Han, Liang Jiang, Prem Kumar, Jeffrey Larson, Zain H. Saleem, Rajkumar KettimuthuEarly Access, Quantum InternetHardware, Optical fiber devices, Optical fiber networks, Optical fiber polarization, Optical fiber theory, Protocols, Quantum entanglement, Quantum repeaters, Repeaters, Scalability

Abstract: Quantum communications have progressed significantly, moving from a theoretical concept to small-scale experiments to recent metropolitan-scale demonstrations. As the technology matures, it is expected to revolutionize quantum computing in much the same way that classical networks revolutionized classical computing. Quantum communications will also enable breakthroughs in quantum sensing, metrology, and other areas. However, scalability […]

Beyond asymptotic scaling: Comparing functional quantum linear solvers

March 16, 2026Andreea-Iulia Lefterovici, Michael Perk, Debora Ramacciotti, Antonio F. Rotundo, S. E. Skelton, Martin SteinbachOpen Access, Quantum ComputingAccuracy, Approximation algorithms, Benchmark testing, Complexity theory, Costs, Hardware, Linear systems, Mathematical models, Quantum algorithm, Standards

Abstract: Solving systems of linear equations is a key subroutine in many quantum algorithms. In the last 15 years, many quantum linear solvers (QLS) have been developed, competing to achieve the best asymptotic worst-case complexity. Most QLS assume fault-tolerant quantum computers, so they cannot yet be benchmarked on real hardware. Because an algorithm with better […]

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 […]

Measurement-Informed Safe Reinforcement Learning for Quantum Battery Charging via Harmonic-Syndrome Diagnostics and BMS Constraints

March 3, 2026Sangkeum Lee, Beomdo Park, Junseong Park, Hyeonseok Jang, Hoon Jeong, Taewook HeoQuantum ComputingBatteries, Battery charge measurement, Couplings, Feature extraction, Hardware, Harmonic analysis, Pipelines, Protocols, Reinforcement learning, Safety

Abstract: Quantum batteries promise ultrafast energy storage but are highly sensitive to noise, drift, and hardware constraints, making safe high-performance charging a central challenge for noisy intermediate-scale quantum devices. We propose a measurement-informed safe control framework that couples harmonic-spectrum-based syndrome diagnostics—H2/H1, H3/H1, and frequency drift—with a battery management system (BMS)-constrained curriculum reinforcement learning (RL) policy. […]

Orthogonal Frequency-Division Multiplexing Continuous-Variable Terahertz QKD for Large-Scale Wireless Quantum Communication

March 2, 2026Mingqi Zhang, Kaveh DelfanazariQuantum InternetFast Fourier transforms, Frequency modulation, Hardware, Noise, OFDM, Protocols, Security, Terahertz communications, Terahertz radiation, Wireless communication

Abstract: In this article, we introduce a continuous-variable quantum key distribution (CVQKD) protocol that combines orthogonal frequency-division multiplexing with terahertz (THz) carriers to deliver high-throughput and hardware-compatible quantum communication. By distributing quantum states across multiple subcarriers, our approach achieves a noticeable increase in spectral efficiency while mitigating dispersion and atmospheric losses that limit the performance […]

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 […]

Accelerating the Max-Cut problems via distributed Ising machine solvers

February 23, 2026Xing-yu Wu, Yi-Xin Zhang, Chen-rui Fan, Chuan WangOpen AccessApproximation algorithms, Computational modeling, Distributed computing, Hardware, Optimization, Partitioning algorithms, Quantum computing, Qubit, Scalability, Stationary state

Abstract: The Ising machine, as a quantum-inspired computing system, can be used to efficiently solve combinatorial optimization problems. Ongoing studies have positioned it to potentially surpass the performance limitations of traditional computers. However, such Ising machines also suffer from scalability as the solution quality becomes sub-optimal when the problem size increases. In this work, we […]

Quantum Annealing for Robust Principal Component Analysis

February 6, 2026Ian Tomeo, Panagiotis Markopoulos, Andreas SavakisQuantum ComputingAnnealing, Computers, Feature extraction, Hardware, Matrix decomposition, Optimization, Principal component analysis, Quantum annealing, Qubit, Robustness

Abstract: Principal component analysis is commonly used for dimensionality reduction, feature extraction, denoising, and visualization. The most commonly used principal component analysis method is based upon optimization of the L2-norm; however, the L2-norm is known to exaggerate the contribution of errors and outliers. When optimizing over the L1-norm, the components generated are known to exhibit […]