Generalized Non-local R^2-like inflation and its Universal predictions
Sravan Kumar Tokyo Institute of Technology
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Unbiasing Fermionic Quantum Monte Carlo with a Quantum Computer
William Huggins Alphabet (United States)
Goldilocks modes in celestial CFT
Andrea Puhm University of Amsterdam
Null Surface Thermodynamics
Mohammad M Sheikh-Jabbari Institute for Research in Fundamental Sciences (IPM)
Ultralocality and the robustness of slow contraction to cosmic initial conditions
Anna Ijjas Max Planck Institute for Gravitational Physics - Albert Einstein Institute (AEI)
Panel Discussion
Arnab Bhattacharyya (National University of Singapore, Singapore), Christopher Harshaw (Yale University), and Liam Solus (KTH)Balancing Covariates In Randomized Experiments: The Gram--Schmidt Walk Design
Christopher Harshaw (Yale University)Learning And Testing Causal Models: A Property Testing Viewpoint
Arnab Bhattacharyya (National University of Singapore, Singapore)Panel Discussion
Chiara Sabatti (Stanford University), Jennifer Listgarten (UC Berkeley), and Peng Ding (UC Berkeley)
Generalized Non-local R^2-like inflation and its Universal predictions
Sravan Kumar Tokyo Institute of Technology
Cosmic inflation is an important paradigm of the early Universe which is so far developed in two equivalent ways, either by geometrical modification of Einstein's general relativity (GR) or by introducing new forms of matter beyond the standard model of particle physics. Starobinsky's R+R^2 inflation based on a geometric modification of GR is one of the most observationally favorable models of cosmic inflation based on a geometric modification of GR. In this talk, I will discuss in detail the fundamental motivations for Starobinsky inflation and present how certain logical steps in the view of its UV completion lead to the emergence of a gravity theory that is non-local in nature. Then I will establish how one can perform studies of the early Universe in the context of non-local gravity and what are the observational consequences in the scope of future CMB and gravitational waves. I will discuss in detail how non-local R^2-like inflation can be observationally distinguishable from the local effective field theories of inflation.
Unbiasing Fermionic Quantum Monte Carlo with a Quantum Computer
William Huggins Alphabet (United States)
Many-electron problems pose some of the greatest challenges in computational science, with important applications across many fields of modern science. Fermionic quantum Monte Carlo (QMC) methods are among the most powerful approaches to these problems. However, they can be severely biased when controlling the fermionic sign problem using constraints, as is necessary for scalability. Here we propose an approach that combines constrained QMC with quantum computing tools to reduce such biases. We experimentally implement our scheme using up to 16 qubits in order to unbias constrained QMC calculations performed on chemical systems with as many as 120 orbitals. These experiments represent the largest chemistry simulations performed on quantum computers (more than doubling the size of prior electron correlation calculations), while obtaining accuracy competitive with state-of-the-art classical methods. Our results demonstrate a new paradigm of hybrid quantum-classical algorithm, surpassing the popular variational quantum eigensolver in terms of potential towards the first practical quantum advantage in ground state many-electron calculations.
Goldilocks modes in celestial CFT
Andrea Puhm University of Amsterdam
In this talk I will consider massless scattering from the point of view of the position, momentum, and celestial bases with a view to advancing a holographic principle for asymptotically flat spacetimes. Within the soft sector, these different languages highlight distinct aspects of the 'infrared triangle': quantum field theory soft theorems arise in the limit of vanishing energy, memory effects are described via shifts of fields along retarded time, and celestial symmetry algebras are realized via currents that appear at special values of the conformal dimension. The latter are determined by the global conformal multiplets in celestial CFT referred to as 'celestial diamonds'. These diamonds degenerate beyond the leading universal soft modes and the standard interpretation of the infrared triangle breaks down: we have neither an obvious asymptotic symmetry nor a Goldstone mode but we do have a soft theorem, and hence a version of a memory effect. I will discuss various aspects of celestial CFT surrounding these Goldstone-like, or Goldilocks, modes and their canonically paired memory modes. They play an important role in constraining celestial OPEs and for understanding the interpretation and implications of the (semi-)infinite tower of tree-level symmetry currents which may pose powerful constraints on consistent low energy effective field theories.
Zoom Link: https://pitp.zoom.us/j/95785822777?pwd=cVJWYVJBS1E3aDJPT1ZSTmlZbzVQQT09
Axion echos from supernovae remnants
JiJi Fan Brown University
Stimulated decays of axion dark matter, triggered by a source in the sky, could produce a photon flux along the continuation of the line of sight, pointing backward to the source. The strength of this so-called axion “echo” signal depends on the entire history of the source and could still be strong from sources that are dim today but had a large flux density in the past, such as supernova remnants (SNRs). This echo signal turns out to be most observable in the radio band. I will present the sensitivity of radio telescopes such as the Square Kilometer Array (SKA) to echo signals generated by SNRs that have already been observed. In addition, I will show projections of the detection reach for signals from newly born supernovae that could be detected in the future. Intriguingly, an observable echo signal could come from old “ghost” SNRs which were very bright in the past but are now so dim that they haven’t been observed.
Zoom Link: https://pitp.zoom.us/j/91076203387?pwd=UzNva3N4Zi9mV3BkMlJvUnhtRXRZdz09
Getting the most out of your measurements: neural networks and active learning
Annabelle Bohrdt Harvard University
Recent advances in quantum simulation experiments have paved the way for a new perspective on strongly correlated quantum many-body systems. Digital as well as analog quantum simulation platforms are capable of preparing desired quantum states, and various experiments are starting to explore non-equilibrium many-body dynamics in previously inaccessible regimes in terms of system sizes and time scales. State-of-the art quantum simulators provide single-site resolved quantum projective measurements of the state. Depending on the platform, measurements in different local bases are possible. The question emerges which observables are best suited to study such quantum many-body systems.
In this talk, I will cover two different approaches to make the most use of these possibilities. In the first part, I will discuss the use of machine learning techniques to study the thermalization behavior of an interacting quantum system. A neural network is trained to distinguish non-equilibrium from thermal equilibrium data, and the network performance serves as a probe for the thermalization behavior of the system. We apply this method to numerically simulated data, as well experimental snapshots of ultracold atoms taken with a quantum gas microscope.
In the second part of this talk, I will present a scheme to perform adaptive quantum state tomography using active learning. Based on an initial, small set of measurements, the active learning algorithm iteratively proposes the basis configurations which will yield the maximum information gain. We apply this scheme to GHZ states of a few qubits as well as ground states of one-dimensional lattice gauge theories and show an improvement in accuracy over random basis configurations.
Null Surface Thermodynamics
Mohammad M Sheikh-Jabbari Institute for Research in Fundamental Sciences (IPM)
We study D dimensional pure Einstein gravity theory in a region of spacetime bounded by a generic null boundary. We show besides the graviton modes propagating in the bulk, the system is described by boundary degrees of freedom labeled by D surface charges associated with nontrivial diffeomorphisms at the boundary. We establish that the system admits a natural thermodynamical description. Using standard surface charge analysis and covariant phase space method, we formulate laws of null surface thermodynamics which are local equations over an arbitrary null surface. This thermodynamical system is generally an open system and can be closed only when there is no flux of gravitons through the null surface. Our analysis extends the usual black hole thermodynamics to a universal feature of any area element on a generic null surface in a generic diffeomorphism invariant theory of gravity.
Zoom Link: https://pitp.zoom.us/j/91590041045?pwd=UXpWY3JEd0QwK2hXanBzSkdPRC94UT09
Ultralocality and the robustness of slow contraction to cosmic initial conditions
Anna Ijjas Max Planck Institute for Gravitational Physics - Albert Einstein Institute (AEI)
I will discuss the detailed process by which slow contraction smooths and flattens the universe using an improved numerical relativity code that accepts initial conditions with non-perturbative deviations from homogeneity and isotropy along two independent spatial directions. Contrary to common descriptions of the early universe, I will show that the geometry first rapidly converges to an inhomogeneous, spatially-curved, and anisotropic ultralocal state in which all spatial gradient contributions to the equations of motion decrease as an exponential in time to negligible values. This is followed by a second stage in which the geometry converges to a homogeneous, spatially flat, and isotropic spacetime. In particular, the decay appears to follow the same history whether the entire spacetime or only parts of it are smoothed by the end of slow contraction.
Zoom Link: https://pitp.zoom.us/j/95441238892?pwd=TUh4Mjh1MHJ6TDNCL0V1NUk5WWFZQT09
Panel Discussion
Arnab Bhattacharyya (National University of Singapore, Singapore), Christopher Harshaw (Yale University), and Liam Solus (KTH)No abstract available.Balancing Covariates In Randomized Experiments: The Gram--Schmidt Walk Design
Christopher Harshaw (Yale University)The design of experiments involves an inescapable compromise between covariate balance and robustness. In this talk, we describe a formalization of this trade-off and introduce a new style of experimental design that allows experimenters to navigate it. The design is specified by a robustness parameter that bounds the worst-case mean squared error of an estimator of the average treatment effect. Subject to the experimenter’s desired level of robustness, the design aims to simultaneously balance all linear functions of potentially many covariates. The achieved level of balance is better than previously known possible, considerably better than what a fully random assignment would produce, and close to optimal given the desired level of robustness. We show that the mean squared error of the estimator is bounded by the minimum of the loss function of an implicit ridge regression of the potential outcomes on the covariates. The estimator does not itself conduct covariate adjustment, so one can interpret the approach as regression adjustment by design. Finally, we provide non-asymptotic tail bounds for the estimator, which facilitate the construction of conservative confidence intervals.Some Staged Tree Models For Learning From Interventions
Liam Solus (KTH)A well-known limitation of modeling causal systems via DAGs is their inability to encode context-specific information. Among the several proposed representations for context-specific causal information are the staged tree models, which are colored probability trees capable of expressing highly diverse context-specific information. The expressive power of staged trees comes at the cost of easy interpretability and the admittance of desirable properties useful in the development of causal discovery algorithms. In this talk, we consider a subfamily of staged trees, which we call CStrees, that admit an alternative representation via a sequence of DAGs. This alternate representation allows us to prove a Verma-Pearl-type characterization of model equivalence for CStrees which extends to the interventional setting, providing a graphical characterization of interventional CStree model equivalence. We will discuss these results and their potential applications to causal discovery algorithms for context-specific models based on interventional and observational data.Learning And Testing Causal Models: A Property Testing Viewpoint
Arnab Bhattacharyya (National University of Singapore, Singapore)We consider testing and learning problems on causal Bayesian networks where the variables take values from a bounded domain. We address two problems: (i) Given access to observations and experiments on two unknown environments X and Y, test whether X=Y or X is far from Y. Here, two environments are equal if no intervention can distinguish between them. (ii) Given access to observations and experiments on an unknown environment X, learn a DAG that admits a causal model M such that X is close to M. For problem (i), we show that under natural sparsity assumptions on the underlying DAG, only O(log n) interventions and O~(n) samples/intervention is sufficient. This is joint work with Jayadev Acharya, Constantinos Daskalakis and Saravanan Kandasamy. For problem (ii), we consider the setting where there are two variables, and the goal is to learn whether X causes Y, Y causes X, or there is a hidden variable confounding the two. Under natural assumptions, we obtain a nearly tight characterization of the sample complexity that is sublinear in k. Moreover, there is a tradeoff between the number of observational samples and interventional samples. This is joint work with Jayadev Acharya, Sourbh Bhadane, Saravanan Kandasamy, and Ziteng Sun.Panel Discussion
Chiara Sabatti (Stanford University), Jennifer Listgarten (UC Berkeley), and Peng Ding (UC Berkeley)