QuICS seminar

The recent advances in qubit manufacturing and coherent control of synthetic quantum matter are leading to a new generation of intermediate scale quantum hardware, with promising progress towards simulation of quantum matter and materials. In order to enhance the capabilities of this class of quantum devices, some of the more arduous experimental tasks can be off-loaded to classical algorithms running on conventional computers.

The quantum adiabatic theorem governs the evolution of a wavefunction under a slowly time-varying Hamiltonian.

Communication networks have multiple users, each sending and receiving messages. A multiple access channel (MAC) models multiple senders transmitting to a single receiver, such as the uplink from many mobile phones to a single base station. The optimal performance of a MAC is quantified by a capacity region of simultaneously achievable communication rates. We study the two-sender classical MAC, the simplest and best-understood network, and find a surprising richness in both a classical and quantum context.

Quantum circuits are believed to be hard to simulate by classical computers. In realistic situations, there is always noise, which makes the quantum gates imperfect. In this talk, I consider classical simulation of several different ensembles of quantum circuits without fault-tolerance, such that the strength of the noise is regarded as a constant (not scaling with the system size). The noise model we consider is mixture of Pauli errors, which includes depolarizing noise as a special case.

This talk is about certifying high-dimensional entanglement in the setting of non-local games. In a non-local game, two or more non-communicating, but entangled, players cooperatively try to win a game consisting of a one-round interaction with a classical referee. In this talk, I will describe a strikingly simple two-player non-local game with the property that an epsilon-close to optimal strategy requires the two players to share an entangled state of dimension 2^{1/poly(epsilon)}.

This talk will discuss techniques that were used in the paper "How to factor 2048 bit RSA integers in 8 hours using 20 million noisy qubits" to reduce the estimated spacetime cost of factoring integers using a quantum computer by over two orders of magnitude. Most of savings come from classical circuit optimization techniques that have been adjusted to work around limitations imposed by the quantum domain.

Machine learning techniques have been recently proposed as a way to efficiently represent certain quantum states with applications in state tomography and ground state estimation. In this talk, we present a practically usable deep architecture for representing and sampling from probability distributions of quantum states. Our representation is based on variational autoencoders, a type of generative model in the form of a neural network.

This presentation will overview several results concerning tasks, some computational and some information theoretic, at which quantum hardware can outperform classical hardware.  A common mathematical theme in the design of each of these tasks is a quantum mechanical phenomenon called non-locality.  The first result is a modification of a breakthrough work of Bravyi, Gosset, and Koenig, wherein we achieve an improved soundness scaling for the circuit separation established in that work, and also develop an application of their framework to a cryptographic task known as

In the area of quantum state learning, one is given a small number of "samples" of a quantum state, and the goal is use them to determine a feature of the state.  Examples include learning the entire state ("quantum state tomography"), determining whether it equals a target state ("quantum state certification"), or estimating its von Neumann entropy.  These are problems which are not only of theoretical interest, but are also commonly used in current-day implementation and verification of quantum technologies.