On the complexity of sampling from shallow Brownian circuits
Gregory Bentsen, Bill Fefferman, Soumik Ghosh, Michael J. Gullans,, Yinchen Liu

TL;DR
This paper analyzes the output distribution of shallow Brownian quantum circuits, revealing they follow a Porter-Thomas distribution with a truncated Hilbert space, and compares their classical and quantum benchmarking scores.
Contribution
It introduces a mean-field approach to characterize shallow Brownian circuits, providing new insights into their output distribution and benchmarking performance.
Findings
Output distributions follow Porter-Thomas distribution with truncation
Quantum scores are within a constant factor of expected value
Classical spoofers exhibit exponentially larger variance
Abstract
While many statistical properties of deep random quantum circuits can be deduced, often rigorously and other times heuristically, by an approximation to global Haar-random unitaries, the statistics of constant-depth random quantum circuits are generally less well-understood due to a lack of amenable tools and techniques. We circumvent this barrier by considering a related constant-time Brownian circuit model which shares many similarities with constant-depth random quantum circuits but crucially allows for direct calculations of higher order moments of its output distribution. Using mean-field (large-n) techniques, we fully characterize the output distributions of Brownian circuits at shallow depths and show that they follow a Porter-Thomas distribution, just like in the case of deep circuits, but with a truncated Hilbert space. The access to higher order moments allows for studying the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMachine Learning and Algorithms · Neural Networks and Applications · Stochastic Gradient Optimization Techniques
