Sampling and the complexity of nature
Dominik Hangleiter

TL;DR
This thesis investigates the complexity and verification of quantum sampling algorithms, focusing on the quantum sign problem as a key factor in distinguishing classical and quantum computational capabilities.
Contribution
It develops measures for the quantum sign problem, analyzes their practicality, and explores the boundary between classical and quantum computing through sampling complexity.
Findings
Quantum sign problem underpins the intractability of quantum output probabilities.
Verifying quantum sampling distributions from few samples is computationally hard.
Assessing the quantum sign problem itself is an intractable computational task.
Abstract
Randomness is an intrinsic feature of quantum theory. The outcome of any quantum measurement will be random, sampled from a probability distribution that is defined by the measured quantum state. The task of sampling from a prescribed probability distribution is therefore a natural technological application of quantum devices. In the research presented in this thesis, I investigate the complexity-theoretic and physical foundations of quantum sampling algorithms. I assess the computational power of natural quantum simulators and close loopholes in the complexity-theoretic argument for the classical intractability of quantum samplers (Part I). I shed light on how and under which conditions quantum sampling devices can be tested or verified in regimes that are not simulable on classical computers (Part II). Finally, I explore the computational boundary between classical and quantum…
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.
