Quantum Physics

Neural networks (NNs) normally do not allow any insight into the reasoning behind their predictions. We demonstrate how influence functions can unravel the black box of NN when trained to predict the phases of the one-dimensional extended spinless Fermi-Hubbard model at half-filling. Results provide strong evidence that the NN correctly learns an order parameter describing the quantum transition. Moreover, we demonstrate that influence functions not only allow to check that the network, trained to recognize known quantum phases, can predict new unknown ones but even guide physicists in understanding patterns responsible for the phase transition. This method requires no a priori knowledge on the order parameter, the system itself, or even the architecture of the ML model.

Given a vector in a representation, can it be distinguished from zero by an invariant polynomial? This classical question in invariant theory relates to a diverse set of problems in mathematics and computer science. In quantum information, it captures the quantum marginal problem and recent bounds on tensor ranks. We will see that the general question can be usefully thought of as an optimization problem and discuss how this perspective leads to efficient algorithms for solving it.

Quantum Thermal Machines

https://drive.google.com/drive/folders/1L6a1YCdB9EejAUmZ52oLDAeB6oosjwhU?usp=sharing

https://www.dropbox.com/sh/sprq2u40e3leoso/AADhyov350QS9TEDdgtErekCa?dl=0

https://github.com/gsellaroli/pi-summer-school-2020

Fluctuation Theorems and Quantum Information

https://drive.google.com/drive/folders/1L6a1YCdB9EejAUmZ52oLDAeB6oosjwhU?usp=sharing

https://www.dropbox.com/sh/sprq2u40e3leoso/AADhyov350QS9TEDdgtErekCa?dl=0

https://github.com/gsellaroli/pi-summer-school-2020

Quantum Thermal Operations

https://drive.google.com/drive/folders/1L6a1YCdB9EejAUmZ52oLDAeB6oosjwhU?usp=sharing

https://www.dropbox.com/sh/sprq2u40e3leoso/AADhyov350QS9TEDdgtErekCa?dl=0

https://github.com/gsellaroli/pi-summer-school-2020

Resource Theories and Quantum Information

https://drive.google.com/drive/folders/1L6a1YCdB9EejAUmZ52oLDAeB6oosjwhU?usp=sharing

https://www.dropbox.com/sh/sprq2u40e3leoso/AADhyov350QS9TEDdgtErekCa?dl=0

https://github.com/gsellaroli/pi-summer-school-2020

Foundations of Quantum Statistical Mechanics - Entanglement

https://drive.google.com/drive/folders/1L6a1YCdB9EejAUmZ52oLDAeB6oosjwhU?usp=sharing

https://www.dropbox.com/sh/sprq2u40e3leoso/AADhyov350QS9TEDdgtErekCa?dl=0

https://github.com/gsellaroli/pi-summer-school-2020

MIP* denotes the class of problems that admit interactive proofs with quantum entangled provers. It has been an outstanding question to characterize the complexity of this class. Most notably, there was no known computable upper bound on MIP*.

We show that MIP* is equal to the class RE, the set of recursively enumerable languages. In particular, this shows that MIP* contains uncomputable problems. Through a series of known connections, this also yields a negative answer to Connes’ Embedding Problem from the theory of operator algebras. In this talk, I will explain the connection between Connes' Embedding Problem, quantum information theory, and complexity theory. I will then give an overview of our approach, which involves reducing the Halting Problem to the problem of approximating the entangled value of nonlocal games.

Joint work with Zhengfeng Ji, Anand Natarajan, Thomas Vidick, and John Wright.