Computational modeling Archives - IEEE Transactions on Quantum Engineering

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 […]

Q-Gen: A Parameterized Quantum Circuit Generator

May 20, 2025Yikai Mao; Shaswot Shresthamali; Masaaki KondoQuantum Software, UncategorizedComplexity theory, Computational modeling, Generators, Integrated circuit modeling, Logic gates, Quantum algorithm, Quantum circuit, Quantum computing, Quantum mechanics, Qubit

Abstract: Unlike most classical algorithms that take an input and give the solution directly as an output, quantum algorithms produce a quantum circuit that works as an indirect solution to computationally hard problems. In the full quantum computing workflow, most data processing remains in the classical domain except for running the quantum circuit in the […]

Analysis of Parameterized Quantum Circuits: On the Connection Between Expressibility and Types of Quantum Gates

May 19, 2025Yu Liu; Kazuya Kaneko; Kentaro Baba; Jumpei Koyama; Koichi Kimura; Naoyuki TakedaQuantum Computing, UncategorizedAccuracy, Additives, Computational modeling, Integrated circuit modeling, Kernel, Logic gates, Predictive models, Quantum circuit, Qubit, Topology

Abstract: Expressibility is a crucial factor of a parameterized quantum circuit (PQC). In the context of variational-quantum-algorithm-based quantum machine learning (QML), a QML model composed of a highly expressible PQC and a sufficient number of qubits is theoretically capable of approximating any arbitrary continuous function. While much research has explored the relationship between expressibility and […]

Modeling and Performance Evaluation of Hybrid Classical–Quantum Serverless Computing Platforms

May 6, 2025Claudio CicconettiQuantum ComputingComputational modeling, Containers, Delays, Logic gates, Mathematical models, Numerical models, Optimization, Quantum circuit, Quantum computing, Serverless computing

Abstract: While quantum computing technologies are evolving toward achieving full maturity, hybrid algorithms, such as variational quantum computing, are already emerging as valid candidates to solve practical problems in fields, such as chemistry and operations research. This situation calls for a tighter and better integration of classical and quantum computing infrastructures to improve efficiency and […]

A Comprehensive Cross-Model Framework for Benchmarking the Performance of Quantum Hamiltonian Simulations

April 4, 2025Avimita Chatterjee; Sonny Rappaport; Anish Giri; Sonika Johri; Timothy Proctor; David E. Bernal NeiraQuantum ComputingBenchmark testing, Computational modeling, Hardware, Integrated circuit modeling, Legged locomotion, Noise, Quantum algorithm, Quantum computing, Qubit, Software algorithms

Abstract: Quantum Hamiltonian simulation is one of the most promising applications of quantum computing and forms the basis for many quantum algorithms. Benchmarking them is an important gauge of progress in quantum computing technology. We present a methodology and software framework to evaluate various facets of the performance of gate-based quantum computers on Trotterized quantum […]

Engineering Quantum Error Correction Codes Using Evolutionary Algorithms

February 5, 2025Mark A. Webster, Dan E. BrowneQuantum ComputingCodes, Computational modeling, Computers, Error analysis, Error correction codes, Evolutionary computation, Generators, Qubit, Standards, Vectors

Quantum error correction and the use of quantum error correction codes are likely to be essential for the realization of practical quantum computing. Because the error models of quantum devices vary widely, quantum codes that are tailored for a particular error model may have much better performance. For more about this article see link below. […]

Simulation of Charge Stability Diagrams for Automated Tuning Solutions (SimCATS)

August 20, 2024Fabian Hader, Sarah Fleitmann, Jan Vogelbruch, Lotte Geck, Stefan van WaasenEnabling TechnologiesComputational modeling, Electrons, Logic gates, Quantum computing, Quantum dots, Quantum mechanics, Tuning

Quantum dots (QDs) must be tuned precisely to provide a suitable basis for quantum computation. A scalable platform for quantum computing can only be achieved by fully automating the tuning process. One crucial step is to trap the appropriate number of electrons in the QDs, typically accomplished by analyzing charge stability diagrams (CSDs). Training and […]

Fault-Tolerant One-Way Noiseless Amplification for Microwave Bosonic Quantum Information Processing

August 7, 2024Hany Khalifa, Riku Jäntti, Gheorghe Sorin ParaoanuQuantum InternetComputational modeling, Logic gates, Microwave amplifiers, Photonics, Propagation losses, Quantum entanglement, Qubit

Microwave quantum information networks require reliable transmission of single-photon propagating modes over lossy channels. In this article, we propose a microwave noiseless linear amplifier (NLA) suitable to circumvent the losses incurred by a flying photon undergoing an amplitude damping channel (ADC). The proposed model is constructed by engineering a simple 1-D four-node cluster state. Contrary […]

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 […]

Postprocessing Variationally Scheduled Quantum Algorithm for Constrained Combinatorial Optimization Problems

March 13, 2024Tatsuhiko Shirai, Nozomu TogawaQuantum ComputingAnnealing, Computational modeling, Logic gates, Quantum algorithm, Quantum annealing, Quantum computing, Schedules

In this article, we propose a postprocessing variationally scheduled quantum algorithm (pVSQA) for solving constrained combinatorial optimization problems (COPs). COPs are typically transformed into ground-state search problems of the Ising model on a quantum annealer or gate-based quantum device. Variational methods are used to find an optimal schedule function that leads to high-quality solutions in […]