This paper develops a resource-bounded framework for evaluating practical controllability in open quantum systems using a trained graybox response model. Rather than treating controllability as a binary property of an idealised Hamiltonian model, the proposed approach evaluates the best-achievable process fidelity over a finite, hardware-realisable pulse family under explicit control constraints. The graybox model retains the known coherent dynamics while learning control-dependent open-system distortions from pulse-response data. The resulting surrogate predictions are used to reconstruct the implemented processes and compare them with Haar-random target gates. Practical controllability is then characterised through the distribution of best-achievable infidelities and an area-based summary metric. The framework is demonstrated for a driven qubit under closed-system, classical-noise, and combined quantum-plus-classical-noise dynamics, with pulse amplitude and inverse Gaussian width used as the control-resource coordinates. The results show how finite control resources and open-system noise jointly constrain the gate performance attainable by the chosen pulse family.
Indirect quantum control aims to manipulate a target system through interventions on an auxiliary controller with which it interacts. We formulate finite-step indirect-control protocols using the language of higher-order quantum operations, representing admissible controller manipulations as deterministic superinstrume...
Šimon Vedl, Varun Srivastava, Riddhi Ghosh et al.· 0 citations
Quantum systems in near-term devices operate in noisy, open-system regimes where decoherence and control
imperfections are unavoidable. This study presents a physics-informed neural-network framework for modelling, noiseaware simulation, and residual-based control of open quantum systems, validated against a closed-for...
Salako Najeem Abiodun, Olatinwo Adenike Sola, Olubudo Paul Ajibola· International Journal of Inn...· 0 citations
A quantitative resource-theoretic framework for delineating the boundary between classical simulation and learning, motivate resource measures linking quantum resources to learnability, and guide the design of learning-based algorithms for scalable quantum systems beyond the reach of direct classical simulation are est...
Xin-Biao Wang, Yuxuan Du, Dacheng Tao· 0 citations
Numerical results show that the learned architectures recover known benchmark strategies, adapt to dephasing noise, and outperform fixed hardware-efficient ans\"atze while using fewer entangling gates, establishing AutoQSense as a resource-aware approach to adaptive and hardware-compatible quantum sensing.
This review develops a control-theoretic perspective on measurement-based feedback for continuously monitored open quantum systems, with the main analysis focused on finite-dimensional systems governed by diffusive stochastic master equations. We introduce the relevant state-space, invariant-subspace, and quantum non-d...
Wei-Chao Liang, Jing Zhang, Gao-Yue Guo et al.· 0 citations
Non-Markovian quantum-state diffusion (QSD) provides a microscopic trajectory description of open-system dynamics, but trajectories associated with a chosen initial state are not directly reusable as quantum channels. We develop a constructive framework that converts the QSD propagator into an input-independent Kraus c...
Yu-Sui Chen· 0 citations
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