Too Much Data: Externalities and Inefficiencies in Data Markets
Azarakhsh Malekian (U. Toronto)
4057 - 4068 of 20126 Results
Format results
Measurement-induced phase transitions on dynamical quantum trees
Xiaozhou Feng Ohio State University
Experimental and Observational Studies in the Presence of Stochastic Networks
Alex Volfovsky (Duke)The back-reaction problem in quantum foundations and gravity
Jonathan Oppenheim University College London
Statistical Physics - Lecture 221201
PIRSA:22120006Quantum many-time physics: noise, complexity, and windows to new phenomena
Gregory White University of Melbourne
Market Power and Tax Interventions: A Principal Components Approach
Ben Golub (Northwestern)Interpretable Quantum Advantage in Neural Sequence Learning
Eric Anschuetz Massachusetts Institute of Technology (MIT)
Quantum Constraint Problems can be complete for BQP, QCMA, and BPP
Alexander Meiburg University of California System
Too Much Data: Externalities and Inefficiencies in Data Markets
Azarakhsh Malekian (U. Toronto)When a user shares her data with online platforms, she reveals information about others in her social network. In such a setting, network externalities depress the price of data because once a user's information is leaked by others, she has less reason to protect her data and privacy. These depressed prices lead to excessive data sharing. We characterize conditions under which shutting down data markets improves welfare. Platform competition does not redress the problem of excessively low data prices and too much data sharing and may further reduce welfare. We propose a scheme based on mediated data sharing that improves efficiency.Measurement-induced phase transitions on dynamical quantum trees
Xiaozhou Feng Ohio State University
Monitored many-body systems fall broadly into two dynamical phases, ``entangling'' or ``disentangling'', separated by a transition as a function of the rate at which measurements are made on the system. Producing an analytical theory of this measurement-induced transition is an outstanding challenge. Recent work made progress in the context of tree tensor networks, which can be related to all-to-all quantum circuit dynamics with forced (postselected) measurement outcomes. So far, however, there are no exact solutions for dynamics of spin-1/2 degrees of freedom (qubits) with ``real'' measurements, whose outcome probabilities are sampled according to the Born rule. Here we define dynamical processes for qubits, with real measurements, that have a tree-like spacetime interaction graph, either collapsing or expanding the system as a function of time. The former case yields an exactly solvable measurement transition. We explore these processes analytically and numerically, exploiting the recursive structure of the tree. We compare the case of ``real'' measurements with the case of ``forced'' measurements. Both cases show a transition at a nontrivial value of the measurement strength, with the real measurement case exhibiting a smaller entangling phase. Both exhibit exponential scaling of the entanglement near the transition, but they differ in the value of a critical exponent. An intriguing difference between the two cases is that the real measurement case lies at the boundary between two distinct types of critical scaling. On the basis of our results we propose a protocol for realizing a measurement phase transition experimentally via an expansion process.
Experimental and Observational Studies in the Presence of Stochastic Networks
Alex Volfovsky (Duke)Dynamic network data have become ubiquitous in social network analysis, with new information becoming available that captures when friendships form, when corporate transactions happen and when countries interact with each other. Moreover, data are available about individual actors in the network, including information about the spread of viral (disease or otherwise) processes between individuals in the network. We argue that the dynamics of these processes should be coupled with those of the network evolution in order to improve downstream inference and develop experimental and observational studies --- we do so by studying a class of stochastic epidemic models that are represented by a continuous-time Markov chain such that disease transmission is constrained by the contact network structure, and network evolution is in turn influenced by individual disease statuses. When aiming at estimating causal effect we couple this dynamic modeling with a study of the violation of classical no-interference assumptions, meaning that the treatment of one individuals might affect the outcomes of another. To make interference tractable, we consider a known network that describes how interference may travel. We discuss two settings: (1) design of experiments under known network interference and (2) an observational setting where the radius (and intensity) of the interference experienced by a unit is unknown and can depend on different sub-networks of those treated and untreated that are connected to this unit. In the former we propose an efficient design that leads to the naive difference in means estimator being consistent while in the second we show that under mild regularity conditions, an inverse weighted estimator is consistent, asymptotically normal and unbiased for the average treatment effect on the treated.The back-reaction problem in quantum foundations and gravity
Jonathan Oppenheim University College London
We consider two interacting systems when one is treated classically while the other remains quantum. Despite several famous no-go arguments, consistent dynamics of this coupling exist, and its most general form can be derived. We discuss the application of these dynamics to the foundations of quantum theory, and to the problem of understanding gravity when space-time is treated classically while matter has a quantum nature.
The talk will be informal and I'll review and follow on from joint work with Isaac Layton, Andrea Russo, Carlo Sparaciari, Barbara Šoda & Zachary Weller-Davies
https://arxiv.org/abs/2208.11722
https://arxiv.org/abs/2203.01982
https://arxiv.org/abs/1811.03116Zoom link: https://pitp.zoom.us/j/92520708199?pwd=WUowdnd4Z0k3dlU2YjVmVlAva3Q0UT09
Spin-liquid states on the pyrochlore lattice and Rydberg atoms simulator
Nikita Astrakhantsev University of Zurich
The XXZ model on the three-dimensional frustrated pyrochlore lattice describes a family of rare-earth materials showing signatures of fractionalization and no sign of ordering in the neutron-scattering experiments. The phase diagram of such XXZ model is believed to host several spin-liquid states with fascinating properties, such as emergent U(1) electrodynamics with emergent photon and possible confinement-deconfinement transition. Unfortunately, numerical studies of such lattice are hindered by three-dimensional geometry and absence of obvious small parameters.
In this talk, I will present my work [Phys. Rev. X 11, 041021] on the variational study of the pyrochlore XXZ model using the RVB-inspired and Neural-Network-inspired ansätze. They yield energies better than known results of DMRG at finite bond dimension. With these wave functions, we study the properties of frustrated phase at the Heisenberg point, and observe signatures of long-range dimer correlations.Lastly, I will sketch the prospects of using the Programmable Rydberg Simulator platform for the study of these spin-liquid states. I will construct two possible embeddings of the pyrochlore XXZ model onto the Rydberg atoms simulator, employing the notion of spin ice and perturbative hexagon flip processes.
Zoom link: https://pitp.zoom.us/j/99480889764?pwd=cnY2RHBjeDZvRkM2K3FlYU9OWjgxUT09
Statistical Physics - Lecture 221201
PIRSA:22120006Persuasion in Networks: Public Signals and Cores
Ozan Candogan (U Chicago)We consider a setting where agents in a social network take binary actions that exhibit local strategic complementarities. Their payoffs are affine and increasing in an underlying real-valued state of the world. An information designer commits to a signaling mechanism that publicly reveals a signal that is potentially informative about the state. She wants to maximize the expected number of agents who take action 1. We study the structure and design of optimal public signaling mechanisms. The designer’s payoff is an increasing step function of the posterior mean (of the state) induced by the realization of her signal. We provide a convex optimization formulation and an algorithm that obtain an optimal public signaling mechanism whenever the designer’s payoff admits this structure. This structure is prevalent, making our formulation and results useful well beyond persuasion in networks. In our problem, the step function is characterized in terms of the cores of the underlying network. The optimal mechanism is based on a “double-interval partition†of the set of states: it associates up to two subintervals of the set of states with each core, and when the state realization belongs to the interval(s) associated with a core, the mechanism publicly reveals this fact. In turn, this induces the agents in the relevant core to take action 1. We also provide a framework for obtaining asymptotically optimal public signaling mechanisms for a class of random networks. Our approach uses only the limiting degree distribution information, thereby making it useful even when the network structure is not fully known. Finally, we explore which networks are more amenable to persuasion, and show that more assortative connection structures lead to larger payoffs for the designer. On the other hand, the dependence of the designer’s payoff on the agents’ degrees can be quite counterintuitive. In particular, we focus on networks sampled uniformly at random from the set of all networks consistent with a degree sequence, and illustrate that when the degrees of some nodes increase, this can reduce the designer’s expected payoff, despite an increase in the extent of (positive) network externalities.Quantum many-time physics: noise, complexity, and windows to new phenomena
Gregory White University of Melbourne
Quantum theory has a temporal composition, which is expressed under many different operational frameworks. Here, points in time are imbued with a Hilbert space structure, and quantum states are passed between times through a series of experimental interventions. A multi-time quantum process, therefore, carries the same complex properties as a many-body quantum state. This invites the question: to what extent can temporal correlations be as interesting as spatial ones, and how can we access them? One particular avenue through which this structure manifests is in open quantum systems. System-environment dynamics can precipitate non-Markovian processes by which correlations persist between different times. Recently, the advent of high-fidelity quantum devices has made it possible to probe coherent quantum systems. In this talk, I will discuss my recent work in which we show how this serves as a novel test bed to capture many-time physics. We build frameworks to extract generic spatiotemporal properties of quantum stochastic processes, show how process complexity may be manipulated, and elevate user-control into the theory to make it self-consistent. Remarkably, many of these complex features are already present in naturally occurring noise, and hence the results have direct application to the development of fault-tolerant quantum devices. I will also briefly discuss some of my future research goals: the existence of exotic temporal phenomena and how emergent spatiotemporal features can be captured through renormalisation group approaches; the learnability of spacetime quantum correlations and avenues here to quantum advantage; and the taming of correlated noise in quantum devices through bespoke error suppression and error correction.
Market Power and Tax Interventions: A Principal Components Approach
Ben Golub (Northwestern)Suppliers of differentiated goods make simultaneous pricing decisions, which are strategically linked because the goods are substitutes or complements in consumption. We study how changes in producers' costs pass through to two key outcomes: prices and welfare. We consider the positive question of which cost changes (e.g., shocks to commodity prices) are most amplified by strategic behavior. We also investigate the policy question of which marginal taxes and subsidies are best for welfare. A key tool is a certain basis for the goods space, determined by the network of interactions among suppliers. It consists of principal components in the goods space, independent in the sense that a cost change incident on any component passes through to the price only of that component. Pass-through coefficients are determined by associated eigenvalues of a demand matrix and yield an ordering of principal components. The ordered basis permits a simple cutoff characterization of optimal tax-and-subsidy interventions, which subsidizes principal components, with high pass-through, and taxes ones with low pass-through. The gain in welfare achievable by an optimal tax scheme is increasing in a suitable measure of eigenvalue dispersion. The results permit us to leverage the theory of spectral approximation to design optimal interventions even when the demand system is observed with a lot of noise.Interpretable Quantum Advantage in Neural Sequence Learning
Eric Anschuetz Massachusetts Institute of Technology (MIT)
Quantum neural networks have been widely studied in recent years, given their potential practical utility and recent results regarding their ability to efficiently express certain classical data. However, analytic results to date rely on assumptions and arguments from complexity theory. Due to this, there is little intuition as to the source of the expressive power of quantum neural networks or for which classes of classical data any advantage can be reasonably expected to hold. In this talk, I will discuss my recent results (arXiv:2209.14353) studying the relative expressive power between a broad class of neural network sequence models and a class of recurrent models based on Gaussian operations with non-Gaussian measurements. We explicitly show that quantum contextuality is the source of an unconditional memory separation in the expressivity of the two model classes. Additionally, we use this intuition to study the relative performance of our introduced model on a standard translation data set exhibiting linguistic contextuality and show that the quantum model outperforms state-of-the-art classical models even in practice. I will also briefly discuss connections to my previous work studying the trainability of variational quantum algorithms (arXiv:2109.06957, arXiv:2205.05786).
Quantum Constraint Problems can be complete for BQP, QCMA, and BPP
Alexander Meiburg University of California System
Constraint satisfaction problems are known to always be "easy" or "hard", in the sense of being either solvable in P or being NP-complete, with no intermediate difficulty levels. The quantum analog of constraint problems, frustration-free Hamiltonians, are known to exhibit at least two more levels of complexity: QMA (for arbitrary local Hamiltonians) and MA (for stoquastic Hamiltonians). Wondering if other complexity classes can occur, we answer in the affirmative: there are interactions which can be freely arranged on qubits in any arrangement, such that the resulting frustration problem is BQP-complete, and captures exactly the difficulty of quantum computation. Simple modifications of this construction show that quantum constraint problems can be complete for QCMA and BPP as well. Based on https://arxiv.org/abs/2101.08381