Talks

The elementary CuO2 plane sustaining cuprate high temperature superconductivity occurs typically at the base of a periodic array of edge-sharing CuO5 pyramids. Virtual transitions of electrons between adjacent planar Cu and O atoms, occurring at a rate t/ℏ and across the charge-transfer energy gap E, generate ‘superexchange’ spin-spin interactions of energy J≈4t4/E3 in an antiferromagnetic correlated-insulator state. However, hole doping this CuO2 plane converts this into a very high temperature superconducting state whose electron-pairing is exceptional. A leading proposal for the mechanism of this intense electron-pairing is that, while hole doping destroys magnetic order it preserves pair-forming superexchange interactions governed by the charge-transfer energy scale E.

To explore this hypothesis directly at atomic-scale, we developed high-voltage single-electron and electron-pair (Josephson) scanning tunneling microscopy, to visualize the interplay of E and the electron-pair density nP in Bi2Sr2CaCu2O8+x. Changing the distance δ between each pyramid’s apical O atom and the CuO2 plane below, should alter the energy levels of the planar Cu and O orbitals and thus vary E.  Hence, the responses of both E and nP to alterations in δ that occur naturally in Bi2Sr2CaCu2O8+x were visualized. These data revealed, directly at atomic scale, the crux of strongly correlated superconductivity in CuO2: the response of the electron-pair condensate to varying the charge transfer energy. Strong concurrence between these observations and recent three-band Hubbard model DMFT predictions for superconductivity in hole-doped Bi2Sr2CaCu2O8+x (PNAS 118, e2106476118 (2021)) indicate that charge-transfer superexchange is the electron-pairing mechanism (PNAS 119, 2207449119 (2022)).

Zoom link:  https://pitp.zoom.us/j/95592484157?pwd=YU56Wno3WnBIUTlyaC9VSHJ3cGxZUT09

Online allocation problems with resource constraints are central problems in revenue management and online advertising. In these problems, requests arrive sequentially during a finite horizon and, for each request, a decision maker needs to choose an action that consumes a certain amount of resources and generates reward. The objective is to maximize cumulative rewards subject to a constraint on the total consumption of resources. In this talk, we consider a data-driven setting in which the reward and resource consumption of each request are generated using an input model that is unknown to the decision maker. We design a general class of algorithms that attain good performance in various input models without knowing which type of input they are facing. In particular, our algorithms are asymptotically optimal under independent and identically distributed inputs as well as various non-stationary stochastic input models, and they attain an asymptotically optimal fixed competitive ratio when the input is adversarial. Our algorithms operate in the Lagrangian dual space: they maintain a dual multiplier for each resource that is updated using online algorithms, such as, online mirror descent and PI controller. Our proposed algorithms are simple, fast, robust to estimation errors, and do not require convexity in the revenue function, consumption function and action space, in contrast to existing methods for online allocation problems. The major motivation of this line of research is the applications to budget pacing in online advertising.

We introduce the online stochastic Convex Programming (CP) problem, a very general version of stochastic online problems which allows arbitrary concave objectives and convex feasibility constraints. Many well-studied problems like the Adwords problem, online stochastic packing and covering, online stochastic matching with concave returns, etc. form a special case of online stochastic CP. We present fast algorithms for these problems, which achieve near-optimal regret guarantees for both the i.i.d. and the random permutation models of stochastic inputs. When applied to the special case of online packing, our ideas yield a simpler and faster primal-dual algorithm for this well studied problem, which achieves the optimal competitive ratio. Our techniques make explicit the connection of primal-dual paradigm and online learning to online stochastic CP.

Tensor network provides a geometric representation of quantum many-body wave functions. Inspired by holography, we discuss a geometric realization of (one-shot) entanglement distillation for tensor networks including the multi-scale entanglement renormalization ansatz and matrix product states. We evaluate the trace distances between the ‘distilled' states and EPR states step by step and see a trend of distillation. If time permits, I will mention a possible field theoretic generalization of this geometric distillation.

Zoom link: https://pitp.zoom.us/j/98545776462?pwd=b1Z3ZENNRWVITlNOZG1GdzJaMmN1Zz09

Decision-makers often face the "many bandits" problem, where one must simultaneously learn across related but heterogeneous contextual bandit instances. For instance, a large retailer may wish to dynamically learn product demand across many stores to solve pricing or inventory problems, making it desirable to learn jointly for stores serving similar customers; alternatively, a hospital network may wish to dynamically learn patient risk across many providers to allocate personalized interventions, making it desirable to learn jointly for hospitals serving similar patient populations. Motivated by real datasets, we decompose the unknown parameter in each bandit instance into a global parameter plus a sparse instance-specific term. Then, we propose a novel two-stage estimator that exploits this structure in a sample-efficient way by using a combination of robust statistics (to learn across similar instances) and LASSO regression (to debias the results). We embed this estimator within a bandit algorithm, and prove that it improves asymptotic regret bounds in the context dimension; this improvement is exponential for data-poor instances. We further demonstrate how our results depend on the underlying network structure of bandit instances. Finally, we illustrate the value of our approach on synthetic and real datasets. Joint work with Kan Xu. Paper: https://arxiv.org/abs/2112.14233

Oftentimes, decisions involve multiple, possible conflicting rewards, or costs. For example, solving a problem faster may incur extra cost, or sacrifice safety. In cases like this, one possibility is to aim for decisions that maximize the value obtained from one of the reward functions, while keeping the value obtained from the other reward functions above some prespecified target values. Up to logarithmic factors, we resolve the optimal words-case sample complexity of finding solutions to such problems in the discounted MDP setting when a generative model of the MDP is available. While this is clearly an oversimplified problem, our analysis reveals an interesting gap between the sample complexity of this problem and the sample complexity of solving MDPs when the solver needs to return a solution which, with a prescribed probability, cannot violate the constraints. In the talk, I will explain the background of the problem, the origin of the gap, the algorithm that we know to achieve the near-optimal sample complexity, closing with some open questions. This is joint work with Sharan Vaswani and Lin F. Yang

The modular commutator is a recently discovered multipartite entanglement measure that quantifies the chirality of the underlying many-body quantum state. In this Letter, we derive a universal expression for the modular commutator in conformal field theories in 1+1 dimensions and discuss its salient features. We show that the modular commutator depends only on the chiral central charge and the conformal cross ratio. We test this formula for a gapped (2+1)-dimensional system with a chiral edge, i.e., the quantum Hall state, and observe excellent agreement with numerical simulations. Furthermore, we propose a geometric dual for the modular commutator in certain preferred states of the AdS/CFT correspondence. For these states, we argue that the modular commutator can be obtained from a set of crossing angles between intersecting Ryu-Takayanagi surfaces.

Zoom link:  https://pitp.zoom.us/j/94069836709?pwd=RlA2ZUsxdXlPTlh2TStObHFDNUY0Zz09

 

Can degrees of freedom in the interior of black holes be responsible for the entropy-area law? If yes, what spacetime appears? In this talk, I answer these questions at the semi-classical level. Specifically, a black hole is considered as a bound state consisting of many semi-classical degrees of freedom which exist uniformly inside and have maximum gravity. The distribution of their information determines the interior metric through the semi-classical Einstein equation. Then, the interior is a continuous stacking of AdS_2 times S^2 without horizon or singularity and behaves like a local thermal state. Evaluating the entropy density from thermodynamic relations and integrating it over the interior volume, the area law is obtained with the factor 1/4 for any interior degrees of freedom. Here, the dynamics of gravity plays an essential role in changing the entropy from the volume law to the area law. This should help us clarify the holographic property of black-hole entropy. [arXiv: 2207.14274]

Zoom link: https://pitp.zoom.us/j/99386433635?pwd=VzlLV2U4T1ZOYmRVbG9YVlFIemVVZz09

The AdS/CFT correspondence is one of the most important breakthroughs of the last decades in theoretical physics. A recently proposed way to get insights on various features of this duality is achieved by discretizing the Anti-de Sitter spacetime. Within this program, we consider the Poincaré disk and we discretize it by introducing a regular hyperbolic tiling on it. The features of this discretization are expected to be identified in the quantum theory living on the boundary of the hyperbolic tiling. In this talk, we discuss how a class of boundary Hamiltonians can be naturally obtained in this discrete geometry via an inflation rule that allows constructing the tiling using concentric layers of tiles. The models in this class are aperiodic spin chains. Using strong-disorder renormalization group techniques, we study the entanglement entropy of these boundary theories, identifying a logarithmic growth in the subsystem size, with a coefficient depending on the bulk discretization parameters.

Zoom link:  https://pitp.zoom.us/j/95849965965?pwd=eEx5Q0gxR2orR0dzS2pQbG8rR09oUT09

Offline reinforcement learning (RL) is a paradigm for designing agents that can learn from existing datasets. Because offline RL can learn policies without collecting new data or expensive expert demonstrations, it offers great potentials for solving real-world problems. However, offline RL faces a fundamental challenge: oftentimes data in real world can only be collected by policies meeting certain criteria (e.g., on performance, safety, or ethics). As a result, existing data, though being large, could lack diversity and have limited usefulness. In this talk, I will introduce a generic game-theoretic approach to offline RL. It frames offline RL as a two-player game where a learning agent competes with an adversary that simulates the uncertain decision outcomes due to missing data coverage. By this game analogy, I will present a systematic and provably correct framework to design offline RL algorithms that can learn good policies with state-of-the-art empirical performance. In addition, I will show that this framework reveals a natural connection between offline RL and imitation learning, which ensures the learned policies to be always no worse than the data collection policies regardless of hyperparameter choices.