Long-range entangled quantum matter from measurement and feedback

Abstract: Long-range entangled states of matter encompass a variety of exotic quantum phenomena, ranging from topological orders to quantum criticality. In this talk, I will discuss recent advances in leveraging mid-circuit measurements and unitary feedback to efficiently generate these entangled many-body states. A particular focus will be a general approach that can (i) universally convert certain short-range entangled quantum phases into a corresponding long-range quantum order and (ii) generate quantum critical correlations from certain gapped phases of matter in constant depth.

Automated Distribution of Entanglement in New York City / Discussion of Startup Life

In the first half of this talk, I'll discuss Qunnect's approach to quantum networking based on warm-atomic ensembles. I'll introduce some of the devices that we build to distribute entanglement over long distances, and experiments we've performed on our GothamQ testbed in New York City. In the second half I'll talk about what it's like to work at a startup, and welcome audience questions on the topic.

Exponential Quantum Space Advantage for Approximating Maximum Directed Cut in the Streaming Model

Abstract: While the search for quantum advantage typically focuses on speedups in execution time, quantum algorithms also offer the potential for advantage in space complexity. Previous work has shown such advantages for data stream problems, in which elements arrive and must be processed sequentially without random access, but these have been restricted to specially-constructed problems [Le Gall, SPAA `06] or polynomial advantage [Kallaugher, FOCS `21]. We show an exponential quantum space advantage for the maximum directed cut problem.

Quantum complexity in many-body physics: random circuits and thermodynamics

Abstract: Quantum complexity is emerging as a key property of many-body systems, including black holes, topological materials, and early quantum computers. A state's complexity quantifies the number of computational gates required to prepare the state from a simple tensor product. I will discuss two approaches to better understand the role of quantum complexity in many-body physics. First, we'll consider random circuits, a model for chaotic dynamics. In such circuits, the quantum complexity grows linearly until it saturates at a value exponential in the system size.

Nobel Prize Celebrates Interplay of Physics and AI

On Tuesday, the Nobel Prize in physics was awarded to John Hopfield and Geoffrey E. Hinton for their foundational discoveries and inventions that have enabled artificial neural networks to be used for machine learning—a widely used form of AI. The award highlights how the field of physics is intertwined with neural networks and the field of AI. 

The State Hidden Subgroup Problem and How to Efficiently Locate Unentanglement

Abstract: We introduce the “hidden cut problem:” given as input which is product across an unknown bipartition, the goal is to learn precisely where the state is unentangled, i.e. to find the hidden cut. We give a polynomial time quantum algorithm for the hidden cut problem, which consumes O(n/ε^2) many copies of the state, and show that this asymptotic is optimal. In the special case of Haar-random states, the circuits involved are of merely constant depth, which could prove relevant to experimental implementations.