High-dimensional multi-fidelity Bayesian optimization for quantum control

Lazin, Marjuka F; Shelton, Christian R; Sandhofer, Simon N; Wong, Bryan M

doi:10.1088/2632-2153/ad0100

Cited by 10 publications

(2 citation statements)

References 70 publications

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“…Further methodological improvements may also be investigated such as improved MCMC sampling and active learning methods. The recent extension of sparse axis-aligned subspace Bayesian Optimisation to allow for multiple computational fidelities [75] could also be a valuable tool.…”

Section: Discussionmentioning

confidence: 99%

Bayesian parameter estimation for characterising mobile ion vacancies in perovskite solar cells

McCallum,

Nicholls,

Jensen

et al. 2023

J. Phys. Energy

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To overcome the challenges associated with poor temporal stability of perovskite solar cells, methods are required that allow for fast iteration of fabrication and characterisation, such that optimal device performance and stability may be actively pursued. Currently, establishing the causes of underperformance is both complex and time-consuming, and optimisation of device fabrication is thus inherently slow. Here, we present a means of computational device characterisation of mobile halide ion parameters from room temperature current–voltage ( J − V ) measurements only, requiring ∼2 h of computation on basic computing resources. With our approach, the physical parameters of the device may be reverse-modelled from experimental J − V measurements. In a drift-diffusion (DD) model, the set of coupled DD partial differential equations cannot be inverted explicitly, so a method for inverting the DD simulation is required. We show how Bayesian Parameter Estimation coupled with a DD perovskite solar cell model can determine the extent to which device parameters affect performance measured by J − V characteristics. Our method is demonstrated by investigating the extent to which device performance is influenced by mobile halide ions for a specific fabricated device. The ion vacancy density N 0 and diffusion coefficient DI were found to be precisely characterised for both simulated and fabricated devices. This result opens up the possibility of pinpointing origins of degradation by finding which parameters most influence device J − V curves as the cell degrades.

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Section: Discussionmentioning

confidence: 99%

Bayesian parameter estimation for characterising mobile ion vacancies in perovskite solar cells

McCallum,

Nicholls,

Jensen

et al. 2023

J. Phys. Energy

View full text Add to dashboard Cite

show abstract

“…The control of quantum dynamical systems at the electronic level continues to garner increasing attention in applications such as optically-induced chemical reactions [1,2,3], magnetic resonance devices [4,5,6,7], quantum computing [8,9], quantum simulation [10,11], and enhanced sensing modalities [12,13]. Recent advances in both theory and experiment [14,15,16,17,18] have demonstrated the power of quantum optimal control (QOC) [19] for constructing tailored control fields to enable desired quantum processes in these systems. QOC provides a set of rigorous algorithms and mathematical techniques to formalize a quantum control problem, approach the controllability of the system, and ultimately solve the optimal problem [20,21].…”

Section: Introductionmentioning

confidence: 99%

MISTER-T: An Open-Source Software Package for Quantum Optimal Control of Multi-Electron Systems on Arbitrary Geometries

Chen,

Okyay,

Wong

2024

Preprint

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We present an open-source software package, MISTER-T (Manipulating an Interacting System of Total Electrons in Real-Time), for the quantum optimal control of interacting electrons within a time-dependent Kohn-Sham formalism. In contrast to other implementations restricted to simple models on rectangular domains, our method enables quantum optimal control calculations for multi-electron systems (in the effective mass formulation) on nonuniform meshes with arbitrary two-dimensional cross-sectional geometries. Our approach is enabled by forward and backward propagator integration methods to evolve the Kohn-Sham equations with a pseudoskeleton decomposition algorithm for enhanced computational efficiency. We provide several examples of the versatility and efficiency of the MISTER-T code in handling complex geometries and quantum control mechanisms. The capabilities of the MISTER-T code provide insight into the implications of varying propagation times and local control mechanisms to understand a variety of strategies for manipulating electron dynamics in these complex systems.

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Accelerating wavepacket propagation with machine learning

Singh,

Lee,

Peláez

et al. 2024

J Comput Chem

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In this work, we discuss the use of a recently introduced machine learning (ML) technique known as Fourier neural operators (FNO) as an efficient alternative to the traditional solution of the time‐dependent Schrödinger equation (TDSE). FNOs are ML models which are employed in the approximated solution of partial differential equations. For a wavepacket propagating in an anharmonic potential and for a tunneling system, we show that the FNO approach can accurately and faithfully model wavepacket propagation via the density. Additionally, we demonstrate that FNOs can be a suitable replacement for traditional TDSE solvers in cases where the results of the quantum dynamical simulation are required repeatedly such as in the case of parameter optimization problems (e.g., control). The speed‐up from the FNO method allows for its combination with the Markov‐chain Monte Carlo approach in applications that involve solving inverse problems such as optimal and coherent laser control of the outcome of dynamical processes.

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High-dimensional multi-fidelity Bayesian optimization for quantum control

Cited by 10 publications

References 70 publications

Bayesian parameter estimation for characterising mobile ion vacancies in perovskite solar cells

Bayesian parameter estimation for characterising mobile ion vacancies in perovskite solar cells

MISTER-T: An Open-Source Software Package for Quantum Optimal Control of Multi-Electron Systems on Arbitrary Geometries

Accelerating wavepacket propagation with machine learning

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