Speeding up state preparation with dynamic quantum circuits with Kevin Smith

Episode 161

Matrix product states (MPS) comprise a broad class of physically interesting entangled states highly relevant to condensed matter physics and quantum chemistry, and applications such as quantum machine learning. While it is well known that any MPS can be exactly prepared using a linear-depth unitary circuit, decoherence limits current NISQ-era processors to fairly shallow-depth circuits, inhibiting the preparation of MPS larger than a few sites. In this talk, I will demonstrate that by blending unitary evolution with non-unitary resources, dynamic quantum circuits can provide a significant speed-up for this task. To provide intuition, I will begin by presenting our algorithm in the context of the paradigmatic AKLT state, and will explain how the core ingredients of our algorithm — unitary gates, mid-circuit measurements, classical feedforward, and symmetries of the target state — can be blended to produce a completely deterministic, constant-depth protocol — an impossibility with unitary resources alone. I will then present experimental results collected on an IBM Quantum processor that indicate that our scheme outperforms its purely unitary counterpart. Finally, I will show how our algorithm more generally provides a unifying framework for the constant-depth preparation of a variety of short-range and long-range entangled MPS, and will highlight a number of examples ranging from useful resource states to those with exotic symmetries. Altogether, this work illustrates the immense potential for applications using dynamic circuits on near-term devices, and furthermore hints at a more general, depth-reduction strategy that leverages non-unitary resources and tensor network representations.

Kevin Smith is a postdoctoral researcher at Yale University and Brookhaven National Laboratory developing resource-efficient quantum algorithms for near‑term applications of NISQ‑era and early fault‑tolerant quantum processors. Within this scope, he is particularly interested in adaptive/dynamic quantum circuits, hybrid oscillator-qubit architectures and, more broadly, the co-design of algorithms with hardware capabilities. Kevin received his Ph.D in Physics from the University of Washington in 2021, where he developed theoretical models for solid-state cavity QED and nanophotonic platforms. Prior to that, he received a B.S. in Physics and Mathematics from the University of Massachusetts Amherst in 2015. Outside of physics, he enjoys reading, rock climbing, and playing with his cats.