## Andrew Guo - November 9, 2022

Dissertation Title:** Many-body entanglement dynamics and computation in quantum systems with power-law interactions**

Date and Time:** Wednesday, November 9, 2:00 pm**

Location:** ATL 3100A and Zoom**

Dissertation Committee Chair:** Victor Galitski**

Committee:

Alexey Gorshkov (co-advisor)

Brian Swingle (co-advisor)

Andrew Childs

Xiaodi Wu

Abstract:

Quantum many-body systems with long-range interactionsâ€”such as those that decay as a power-law in the distance between particlesâ€”are promising candidates for quantum information processors. Due to their high degree of connectivity, they are potentially capable of generating entanglement more quickly than systems limited to local interactions, which may lead to faster computational speeds. The questions of the nature of the speed-ups they can achieveâ€”as well as how to program these long-range systems to achieve such speed-upsâ€”are, therefore, of prime theoretical interest.

To understand the nature of the speed-ups achievable, it is natural to consider the dual question, which is *what are the fundamental speed limits in quantum many-body systems?* Given that most systems relevant to quantum computation operate in the non-relativistic regimeâ€”where information typically propagates at velocities far below the threshold set by the speed of lightâ€”the absence of an absolute speed limit seems to allow for unbounded rates of information transfer. However, in 1972, Lieb and Robinson restored a notion of locality in systems with local interactions by proving a bound that led to light-cone-like regions outside which information propagation is exponentially suppressed. The question of whether similar bounds could be proven for long-range systems has remained openâ€”until recently.

In this thesis, we will describe results related to the now-fuller picture of the fundamental rates of information propagation in power-law-interacting systems. First, we consider the regime of ``strongly long-range'' interactions, for which velocities can grow unboundedly with system size. We will present Lieb-Robinson-type bounds for these systems and also outline a protocol that can transfer quantum states as fast as these bounds will allow. we will also discuss the implications of these bounds for quantum information scrambling.

The second part of the thesis will study how protocols for transferring quantum states quickly can be used to perform multi-qubit gates. In particular, we will demonstrate how the power of long-range interactions allows one to implement the unbounded fanout gate asymptotically faster than systems with local interactions. This result also implies the hardness of simulating the dynamics of long-range systems evolving for superlogarithmic times, and demonstrates the potential for insights from quantum many-body physics to lead to a more powerful toolbox for quantum computation.

Finally, we will address the question of fundamental speed limits in quantum systems that are open to the environment. A priori, it may seem surprising that such speed limits may exist, since non-unitary processes may break locality constraints. However, we show that under certain assumptions such as linearity and Markovianity of the bath, one can restore a notion of locality using Lieb-Robinson-type bounds. We use the resulting bounds to constrain the entanglement structure of the steady states of open long-range systems, a first step towards proving the area law for such systems.

## Amitava Banerjee - November 4, 2022

Dissertation Title:** **Developing Machine Learning Techniques for Network Connectivity Inference From Time-series Data

Date and Time:** Friday, November 4, 2:00 pm**

Location:** ERF 1207**

Dissertation Committee Chair:** Professor Edward Ott**

Committee:

Professor Rajarshi Roy

Professor Michelle Girvan

Professor Yanne K. Chembo

Professor Pratyush Tiwary

Abstract:

Inference of the connectivity structure of a network from the observed dynamics of the states of its nodes is a key issue in science, with wide-ranging applications such as determination of the synapses in nervous systems, mapping of interactions between genes and proteins in biochemical networks, distinguishing ecological relationships between different species in their habitats etc. In this thesis, we show that machine learning models, trained for the forecasting of experimental and synthetic time-series data from complex systems, can automatically learn the causal networks underlying such complex systems. Based on this observation, we develop new machine learning techniques for inference of causal interaction network connectivity structures underlying large, networked, noisy, complex dynamical systems, solely from the time-series of their nodal states.

In particular, our approach is to first train a type of machine learning architecture, known as the 'reservoir computer', to mimic the measured dynamics of an unknown network. We then use the trained reservoir computer system as an *in silico* model of the unknown network to estimate how small changes in nodal states propagate in time across that network. Since small perturbations of network nodal states are expected to spread along the links of the network, the estimated propagation of nodal state perturbations reveal the connections of the unknown network.

We shall discuss how we can further apply this methodology to infer causal network structures underlying different time-series datasets and compare the inferred network with the ground truth whenever available. We shall demonstrate three practical applications of this network inference procedure in (1) inference of network link strengths from time-series data of coupled, noisy Lorenz oscillators, (2) inference of time-delayed feedback couplings in opto-electronic oscillator circuit networks designed the laboratory, and, (3) inference of the synaptic network from publicly-available calcium fluorescence time-series data of *C. elegans* neurons. In all examples, we shall also explain how experimental factors like noise level, sampling time, and measurement duration systematically affect causal inference from experimental data.

Given the general applicability of our machine learning methodology in time-series prediction and network inference, we anticipate that such techniques can be used for better model-building, forecast, and control of complex systems in nature and in the lab.