Qiskit Quantum Seminar

Currently Playing: Quantum-Enhanced Markov Chain Monte Carlo with David Layden | Qiskit Seminar Series w/ David Layden

Quantum-Enhanced Markov Chain Monte Carlo Your formal invite to weekly Qiskit videos ► https://ibm.biz/q-subscribe Speakers: David Layden Host: Olivia Lanes, PhD. Abstract: Sampling from complicated probability distributions is a hard computational problem arising in many fields, including statistical physics, optimization, and machine learning. Quantum computers have recently been used to sample from complicated distributions that are hard to sample from classically, but which seldom arise in applications. We introduce a quantum algorithm to sample from distributions that pose a bottleneck in several applications, which we implement on a superconducting quantum processor. The algorithm performs Markov chain Monte Carlo (MCMC), a popular iterative sampling technique, to sample from the Boltzmann distribution of classical Ising models. In each step, the quantum processor explores the model in superposition to propose a random move, which is then accepted or rejected by a classical computer and returned to the quantum processor, ensuring convergence to the desired Boltzmann distribution. We find that this quantum algorithm converges in fewer iterations than common classical MCMC alternatives on relevant problem instances, both in simulations and experiments. It therefore opens a new path for quantum computers to solve useful—not merely difficult—problems in the near term. Bio: David joined IBM Quantum as a Research Staff Member in 2020 after finishing his PhD at MIT, where he worked on device- and application-adapted quantum error correction. His current research is at the interface of theory and experiment, and focuses on using emerging quantum devices to their full potential. -- The Qiskit Seminar Series is a deep dive into various academic and research topics within the quantum community. It will feature community members and leaders every Friday, 12 PM EDT.