Defect-phonon interactions in solid-state devices are crucial for improving our current knowledge of quantum platforms. Quantum systems are naturally coupled to external reservoirs, limiting applications and offering new engineering challenges. Many-body quantum systems with phonon reservoirs can be valuable for studying non-Markovianity, topological effects at the nanoscale, and out-of-equilibrium dynamics at non-zero temperature.

Projects

• Quantifying phonon-induced non-Markovianity in color centers in diamond, Phys. Rev. A 101, 022110 (2020)

• Effect of phonons on the electron spin resonance absorption spectrum, New J. Phys. 22 073068 (2020)

Collaborators

Peter Rabl, TU-Wien

Jerónimo Maze, Pontificia Universidad Católica de Chile

Hossein Dinani, Universidad Mayor

Open quantum systems is an approach to model the interaction between a quantum-mechanical system and some environment. This approach applies to many systems ranging from cavity QED, coupled atoms, spin lattices, and solid-state devices. Depending on the nature of the environment, the dynamics can be described as Markovian or non-Markovian. Moreover, an open quantum system can be driven by external fields that lead to novel phenomena such as dynamical hysteresis, quantum phase transition, and other many-body effects.

Projects

• From the open generalized Heisenberg model to the Landau-Lifshitz equation, New J. Phys. 22 103029 (2020)

• Quantifying phonon-induced non-Markovianity in color centers in diamond, Phys. Rev. A 101, 022110 (2020)

Collaborators

Guillermo Romero, Universidad Santiago de Chile

Time-dependent Hamiltonians can control quantum systems, a powerful technique for searching for new technological applications. In general, it is possible to induce interesting dynamical behaviors by applying time-dependent modulations for light-matter and field-atom interactions. This quantum control allows the creation of quantum memristors and controlled scenarios for dynamical quantum phase transition.

Projects

• Polariton-based quantum memristors, Phys. Rev. Applied 17, 024056 (2022)

• Dynamical quantum phase transition in diamond: Applications in quantum metrology,  Phys. Rev. B 106, 014313 (2022)

Collaborators

Guillermo Romero, Universidad Santiago de Chile

Francisco Albarrán-Arriagada, Universidad Santiago de Chile

Felipe Torres, Universidad de Chile

Machine learning for quantum mechanics

Machine learning (ML) techniques can be useful for modeling, testing, classifying, and predicting the quantum properties of several systems. Using ML it is possible to classify the degree of non-Markovianity in open quantum systems as well as to find unknown optimal control fields to drive complex dynamics. Also, the use of physics-informed neural networks can be relevant to find optimal control Hamiltonians for optical, molecular, and atomical systems.

Projects

• Physics-informed neural network for quantum control, Phys. Rev. Lett. 132, 010801 (2024).

• Quantum kernels for classifying dynamical singularities in a multiqubit system, Quantum Sci. Technol. 9. 035046 (2024)

• Estimating the degree of non-Markovianity using machine learning, Phys. Rev. A 103, 022425 (2021)

Collaborators

Marios Matheakis, Harvard University

Felipe Fanchini, Sao Paulo State University (Unesp)