Distributed Quantum Inner Product Estimation with Structured Random Circuits
Congcong Zheng, Kun Wang, Xutao Yu, Ping Xu, Zaichen Zhang

TL;DR
This paper investigates distributed quantum inner product estimation using structured random circuits, demonstrating that certain ensembles achieve near-optimal sample complexities and are feasible for near-term quantum devices.
Contribution
It introduces analysis of DIPE with structured random circuits, including brickwork and local ensembles, showing their sample complexities and practical feasibility.
Findings
Brickwork ensemble achieves complexity $ ilde{O}( oot 2.18 n)$
Global Clifford ensemble matches $ ilde{O}( oot 2^n n)$ complexity
Nonstabilizerness enhances efficiency for local and global Clifford ensembles
Abstract
Distributed inner product estimation (DIPE) is a fundamental task in quantum information, aiming to estimate the inner product between two unknown quantum states prepared on distributed quantum platforms. Existing rigorous sample complexity analyses are limited to unitary -designs, which pose significant practical challenges for near-term quantum devices. This work addresses this challenge by exploring DIPE with structured random circuits. We first establish that DIPE with an arbitrary unitary -design ensemble achieves an average sample complexity of , where is the number of qubits. We then analyze ensembles below unitary -designs -- specifically, the brickwork and local unitary -design ensembles -- demonstrating average sample complexities of and , respectively. Furthermore, we analyze 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
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Quantum many-body systems
