Papers
Topics
Authors
Recent
Search
2000 character limit reached

Random Projections for Multi-Copy Quantum Algorithms

Published 18 Jun 2026 in quant-ph | (2606.20238v1)

Abstract: Estimating nonlinear properties of quantum states is a central task in quantum information science. Multivariate traces, tr(ρ1ρK)\mathrm{tr}(ρ_1 \cdots ρ_K), and nonlinear observables such as tr(ρ<sup>K)\mathrm{tr}(ρ<sup>K), for integer KK, can be accessed through collective measurements on multiple state copies, but standard protocols based on swap tests require coherent operations on the full Hilbert space and become experimentally unfeasible for large systems. In this work, we introduce a framework for multi-copy measurements based on random projections onto lower-dimensional subspaces prior to the collective measurement, which is then performed only on the reduced Hilbert space. This procedure yields a tunable tradeoff between coherent quantum resources and statistical sampling overhead, allowing the amount of coherent processing to be matched to the capabilities of the underlying hardware. We derive explicit formulas relating the Haar-averaged projected moments to multivariate traces of the original states and analyze the sampling overhead induced by the projection procedure. Specifically, after compressing an nn-qubit state to a reduced qq-qubit subspace, estimating tr(ρ<sup>K)\mathrm{tr}(ρ<sup>K) requires approximately O(2<sup>(nq)(K1))O(2<sup>{(n-q)(K-1)}) copies of ρρ, with each qubit projected out increasing the sampling cost by a factor of 2<sup>K12<sup>{K-1}. Our results establish how coherent multi-copy operations can be traded for additional state copies, enabling multi-copy quantum protocols to be optimized for the available hardware resources.

Summary

No one has generated a summary of this paper yet.

Paper to Video (Beta)

No one has generated a video about this paper yet.

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Continue Learning

We haven't generated follow-up questions for this paper yet.