We present the first multi-fidelity Bayesian optimization (BO) approach for solving inverse
problems in the quantum control of prototypical quantum systems. Our approach …

We develop inductive biases for the machine learning of complex physical systems based
on the port-Hamiltonian formalism. To satisfy by construction the principles of …

We present an open-source software package, TRAVOLTA (Terrific Refinements to
Accelerate, Validate, and Optimize Large Time-dependent Algorithms), for carrying out …

We present a novel, computationally efficient approach to accelerate quantum optimal
control calculations of large multi-qubit systems used in a variety of quantum computing …

Polar molecules are a promising platform for achieving scalable quantum information
processing because of their long-range electric dipole–dipole interactions. Here, we take the …

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 …

The application of time series forecasting utilizing historical data has become increasingly
essential across a variety of industries including finance, healthcare, meteorology, and …

A deep-learning-based model to automate the design of a dual-band coupled-line trans-
directional (CL-TRD) coupler can greatly improve upon the current techniques that rely on …

In this work, we developed an efficient inverse design approach for optimal intermediate
band solar cells (IBSC) device design given a target performance by using a joint drift …

In Coulomb three-body problems, configurations of close proximity of the particles are
classically unstable. In confined systems they might however exist as excited quantum …