Neural networks Archives - IEEE Transactions on Quantum Engineering

Parallel Variational Quantum Algorithms With Gradient-Informed Restart to Speed Up Optimization in the Presence of Barren Plateaus

February 10, 2026Daniel Mastropietro, Georgios Korpas, Vyacheslav Kungurtsev, Jakub MarecekQuantum SoftwareApproximation algorithms, Cost function, Markov processes, Neural networks, Noise, Noise measurement, Quantum algorithm, Quantum circuit, Quantum entanglement, Simulated annealing

Abstract: Inspired by the Fleming–Viot stochastic process, we propose a parallel implementation with restart of variational quantum algorithms, with the aim of reducing the time spent by the algorithm in barren plateaus where the optimization direction is unclear. In the Fleming–Viot tradition, parallel searches are called particles. In the proposed approach, the search by a […]

Integrated Encoding and Quantization to Enhance Quanvolutional Neural Networks

December 18, 2025Daniele Lizzio Bosco, Beatrice Portelli, Giuseppe SerraQuantum ComputingConvolutional neural networks, Encoding, Image coding, Integrated circuit modeling, Logic gates, Neural networks, Quantization (signal), Quantum circuit, Quantum state, Qubit

Abstract: Image processing is one of the most promising applications for quantum machine learning. Quanvolutional neural networks with nontrainable parameters are the preferred solution to run on current and near future quantum devices. The typical input preprocessing pipeline for quanvolutional layers comprises of four steps: optional input binary quantization, encoding classical data into quantum states, […]

Dual-Discriminator Hybrid Quantum Generative Adversarial Networks for Improved GAN Performance

December 9, 2025Purin Pongpanich, Tanasanee PhienthrakulQuantum ComputingComputer architecture, Generative adversarial networks, Generators, Hybrid power systems, Integrated circuit modeling, Logic gates, Neural networks, Quantum circuit, Quantum computing, Training

Abstract: This study presents an investigation of the dual-discriminator hybrid quantum generative adversarial network (DDHQ-GAN), a framework designed to enhance the performance of conventional generative adversarial networks (GANs) through the incorporation of a hybrid quantum discriminator. The proposed DDHQ-GAN architecture comprises three primary components: a generator and two discriminators. The research evaluates the efficacy of […]

Relative Entropy-Based Training of Quantum Neural Networks

December 1, 2025Subhadeep Mondal, Amit Kumar DuttaQuantum ComputingCost function, Entropy, Indexes, Machine learning, Neural networks, Noise, Quantum channels, Quantum computing, Qubit, Training

Abstract: Quantum neural networks (QNNs) are gaining attention as versatile models for quantum machine learning, but training them effectively remains a challenge. Most existing approaches, such as quantum multilayer perceptrons, use fidelity-based cost functions. While well-suited for pure states, these measures are less reliable when inputs and outputs are mixed states—a situation common in learning […]

DT-QFL: Dual-Timeline Quantum Federated Learning With Time-Symmetric Updates, Temporal Memory Kernels, and Reversed Gradient Dynamics

September 9, 2025Koffka Khan Khouler KhanQuantum Computing, UncategorizedAdaptation models, Computational modeling, Data models, Federated Learning, Kernel, Neural networks, Quantum computing, Robustness, Servers, Training

Abstract: Federated learning has emerged as a powerful paradigm for decentralized model training, ensuring privacy preservation by allowing clients to collaboratively learn a shared model without exchanging raw data. Quantum federated learning (QFL) extends this approach by leveraging quantum computing to enhance computational efficiency and security. However, existing QFL frameworks face challenges in handling temporal […]

Learning a Quantum Computer’s Capability

July 18, 2024Daniel Hothem, Kevin Young, Tommie Catanach, Timothy ProctorQuantum InformationComputational modeling, Computers, Integrated circuit modeling, Neural networks, Predictive models, Program processors, Quantum computing

Accurately predicting a quantum computer’s capability—which circuits it can run and how well it can run them—is a foundational goal of quantum characterization and benchmarking. As modern quantum computers become increasingly hard to simulate, we must develop accurate and scalable predictive capability models to help researchers and stakeholders decide which quantum computers to build and […]

Understanding Logical-Shift Error Propagation in Quanvolutional Neural Networks

March 5, 2024Marzio Vallero, Emanuele Dri, Edoardo Giusto, Bartolomeo Montrucchio, Paolo RechQuantum ComputingCircuit faults, Integrated circuit reliability, Neural networks, Quantum circuit, Quantum computing, Qubit, Reliability

Quanvolutional neural networks (QNNs) have been successful in image classification, exploiting inherent quantum capabilities to improve performance of traditional convolution. Unfortunately, the qubit’s reliability can be a significant issue for QNNs inference, since its logical state can be altered by both intrinsic noise and by the interaction with natural radiation. In this article, we aim […]

Understanding Logical-Shift Error Propagation in Quanvolutional Neural Networks

March 5, 2024Marzio ValleroQuantum ComputingCircuit faults, Integrated circuit reliability, Neural networks, Quantum circuit, Quantum computing, Qubit, Reliability

Quanvolutional Neural Networks (QNNs) have been successful in image classification, exploiting inherent quantum capabilities to improve performance of the traditional convolution. Unfortunately, the qubit’s reliability can be a significant issue for QNNs inference, since its logical state can be altered by both intrinsic noise and by the interaction with natural radiation. In this paper we […]

DQRA: Deep Quantum Routing Agent for Entanglement Routing in Quantum Networks

November 2, 2022Linh LeOpen Access, Quantum InternetNeural networks, Quantum entanglement, Quantum networks, Qubit, Repeaters, Routing, Teleportation

Quantum routing plays a key role in the development of the next-generation network system. In particular, an entangled routing path can be constructed with the help of quantum entanglement and swapping among particles (e.g., photons) associated with nodes in the network. From another side of computing, machine learning has achieved numerous breakthrough successes in various […]

DQRA: Deep Quantum Routing Agent for Entanglement Routing in Quantum Networks

February 7, 2022Linh Le; Tu N. NguyenOpen Access, Quantum InformationNeural networks, Quantum entanglement, Quantum networks, Qubit, Repeaters, Routing, Teleportation