Quantum@SUN

Title of talk: Universal cooling dynamics toward a quantum critical point

Speaker: Emma King (present affiliation: University of Saarland, Saarbrücken, Germany; the work I will present was done at Stellenbosch University, Stellenbosch, South Africa).

Abstract:
Curiosity surrounding concepts such as critical phenomena, scaling laws and universality in out-of-equilibrium settings has inspired vibrant research in the field of nonequilibrium many-body systems. While experimental advances continue to pave the way for probing the dynamics of such systems, we aim to complement these developments by analysing the imprint of equilibrium phase transitions on a system’s nonequilibrium dynamics. In this talk we will focus on one aspect of this, in particular the loss of adiabaticity when cooling a many-body quantum system from an initial thermal state toward a quantum critical point. The excitation density, which quantifies the degree of adiabaticity of the dynamics, is found to obey scaling laws governed by the critical exponents of the quantum phase transition (QPT). As an example, we will demonstrate the scaling of the excitation density for a Kitaev quantum wire coupled to Markovian baths, a “minimal model” that can be solved analytically. Interestingly, we will show that quantum critical properties can be probed dynamically at finite temperature, without even varying the control parameter of the QPT. In closing, we will mention some interesting future research directions.

References: (1) Emma C. King, Johannes N. Kriel, and Michael Kastner, Phys. Rev. Lett. 130, 050401,https://link.aps.org/doi/10.1103/PhysRevLett.130.050401; (2) Emma C. King, Johannes N. Kriel, and Michael Kastner, arXiv:2204.07595, https://doi.org/10.48550/arXiv.2204.07595.

Abstract:

The Quantum Convolutional Neural Network (QCNN) is a quantum circuit model inspired by the architecture of Convolutional Neural Networks (CNNs). CNNs are successful because they do not need manual feature design and can learn high-level features from raw data. Neural Architecture Search (NAS) builds on this success by learning network architecture and achieves state-of-the-art performance. However, NAS requires the design of a search space, which is currently not possible for QCNNs as no formal framework exists to capture its design elements. In this work, we provide such a framework by using techniques from NAS to create a hierarchical representation for QCNN architectures. Using this framework, we generate a family of popular QCNNs, those resembling reverse binary trees. We then evaluate this family of models on a music genre classification dataset, GTZAN, showing that alternating architecture has a greater impact on model performance than other modelling components, such as the choice of unitary ansatz and data encoding. Our framework provides a way to improve model performance without increasing complexity and to jump around the cost landscape to avoid barren plateaus. Finally, we implement the framework as an open-source Python package to enable dynamic QCNN creation and facilitate QCNN search space design for NAS.