Convergence Archives - IEEE Transactions on Quantum Engineering

Fast State Stabilization Using Deep Reinforcement Learning for Measurement-Based Quantum Feedback Control

September 4, 2025Chunxiang Song Yanan Liu Daoyi Dong Hidehiro YonezawaQuantum ComputingConvergence, Current measurement, Deep Reinforcement Learning, Feedback control, Quantum computing, Quantum entanglement, Quantum state, Quantum system, Trajectory, Velocity measurement

Abstract: The stabilization of quantum states is a fundamental problem for realizing various quantum technologies. Measurement-based-feedback strategies have demonstrated powerful performance, and the construction of quantum control signals using measurement information has attracted great interest. However, the interaction between quantum systems and the environment is inevitable, especially when measurements are introduced, which leads to decoherence. […]

Explicit Quantum Circuit for Simulating the Advection–Diffusion–Reaction Dynamics

February 21, 2025Claudio Sanavio, Enea Mauri, Sauro SucciQuantum ComputingComputational fluid dynamics, Convergence, Encoding, Logic gates, Logistics, Mathematical models, Polynomials, Quantum algorithm, Quantum circuit, Qubit

We assess the convergence of the Carleman linearization of advection–diffusion–reaction (ADR) equations with a logistic nonlinearity. It is shown that five Carleman iterates provide a satisfactory approximation of the original ADR across a broad range of parameters and strength of nonlinearity. To assess the feasibility of a quantum algorithm based on this linearization, we analyze […]

Multiblock ADMM Heuristics for Mixed-Binary Optimization on Classical and Quantum Computers

October 23, 2020Claudio Gambella; Andrea SimonettoOpen Access, Quantum Computer Architecture, Quantum InformationComputational modeling, Computers, Convergence, Convex functions, Noise measurement, Optimization, Quantum computing

Solving combinatorial optimization problems on current noisy quantum devices is currently being advocated for (and restricted to) binary polynomial optimization with equality constraints via quantum heuristic approaches. This is achieved using, for example, the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA). In this article, we present a decomposition-based approach to extend […]