no code implementations • 23 Aug 2023 • Jordan Cotler, Semon Rezchikov
We explain how to use diffusion models to learn inverse renormalization group flows of statistical and quantum field theories.
no code implementations • 27 Feb 2023 • Jordan Cotler, Kai Sheng Tai, Felipe Hernández, Blake Elias, David Sussillo
The specific model to be emulated is determined by a model embedding vector that the meta-model takes as input; these model embedding vectors constitute a manifold corresponding to the given population of models.
1 code implementation • 12 Dec 2022 • Katherine Van Kirk, Jordan Cotler, Hsin-Yuan Huang, Mikhail D. Lukin
Efficient characterization of highly entangled multi-particle systems is an outstanding challenge in quantum science.
no code implementations • 13 Oct 2022 • Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
The recent proliferation of NISQ devices has made it imperative to understand their computational power.
no code implementations • 1 Dec 2021 • Jordan Cotler, Hsin-Yuan Huang, Jarrod R. McClean
In this note, we prove that classical algorithms with SQ access can accomplish some learning tasks exponentially faster than quantum algorithms with quantum state inputs.
1 code implementation • 1 Dec 2021 • Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, Jarrod R. McClean
Quantum technology has the potential to revolutionize how we acquire and process experimental data to learn about the physical world.
no code implementations • 10 Nov 2021 • Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
We study the power of quantum memory for learning properties of quantum systems and dynamics, which is of great importance in physics and chemistry.
no code implementations • 10 Nov 2021 • Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
We prove that given the ability to make entangled measurements on at most $k$ replicas of an $n$-qubit state $\rho$ simultaneously, there is a property of $\rho$ which requires at least order $2^n$ measurements to learn.
no code implementations • 12 Jan 2021 • Dorit Aharonov, Jordan Cotler, Xiao-Liang Qi
We initiate the systematic study of experimental quantum physics from the perspective of computational complexity.
Quantum Physics Strongly Correlated Electrons
no code implementations • 5 Oct 2020 • Jordan Cotler, Kristan Jensen
We find constrained instantons in Einstein gravity with and without a cosmological constant.
High Energy Physics - Theory General Relativity and Quantum Cosmology
no code implementations • 5 Nov 2019 • Jordan Cotler, Nicholas Hunter-Jones
We argue that in a large class of disordered quantum many-body systems, the late time dynamics of time-dependent correlation functions is captured by random matrix theory, specifically the energy eigenvalue statistics of the corresponding ensemble of disordered Hamiltonians.
High Energy Physics - Theory Statistical Mechanics Strongly Correlated Electrons Quantum Physics
2 code implementations • 7 Aug 2019 • Jordan Cotler, Frank Wilczek
By leveraging (i) that single-qubit measurements can be made in parallel, and (ii) the theory of perfect hash families, we show that all $k$-qubit reduced density matrices of an $n$ qubit state can be determined with at most $e^{\mathcal{O}(k)} \log^2(n)$ rounds of parallel measurements.
Quantum Physics Quantum Gases Strongly Correlated Electrons