The Quantum Reform of the Modern Metric System: Mass becomes quantum, and electrical measurements become honest
JQI Fellow William D. Phillips will deliver the 2024 Paint Branch Distinguished Lecture in Applied Physics, a lectureship sponsored by the Institute for Research in Electronics & Applied Physics.
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.
Long-range entangled quantum matter from measurement and feedback
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.
Quantum Application, Parallel Operation and Noise Characterization in a Trapped-Ion Quantum Computer
Dissertation Committee Chair: Steven Rolston
Committee: Norbert M. Linke, Avik Dutt, Yu Liu, Christopher Jarzynski
An automata-based approach for quantum circuit/program verification
We present a new method for analyzing and identifying errors in quantum circuits. In our approach, we define the problem using a triple {P}C{Q}, where the task is to determine whether a given set P of quantum states at the input of a circuit C produces a set of quantum states at the output that is equal to, or included in, a set Q. We propose a technique that utilizes tree automata to represent sets of quantum states efficiently, and we develop algorithms to apply the operations of quantum gates within this representation.
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
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.
Optimization by Decoded Quantum Interferometry
In this talk I will describe Decoded Quantum Interferometry (DQI), a quantum algorithm for reducing classical optimization problems to classical decoding problems by exploiting structure in the Fourier spectrum of the objective function. (See: https://arxiv.org/abs/2408.08292.) For a regression problem called optimal polynomial intersection, which has been previously studied in the contexts of coding theory and cryptanalysis, we believe DQI achieves an exponential quantum speedup.
Quantum Sensing, with Applications to Fundamental Physics
Quantum sensing leverages the principles of quantum mechanics to provide ``quantum-enhanced'' measurement sensitivity, thereby amplifying our ability to observe interesting physical phenomena. It employs a rich arsenal of techniques, including squeezing, photon counting, entanglement assistance, and distributed quantum sensing to achieve unprecedented sensitivity.