Exponential speedup in measurement property learning with post-measurement states
Zhenhuan Liu, Qi Ye, Zhenyu Cai, Jens Eisert

TL;DR
This paper demonstrates that access to post-measurement states enables exponential speedup in learning measurement properties, highlighting a new resource that surpasses traditional methods used in quantum state and channel learning.
Contribution
It introduces a measurement learning task where post-measurement states provide an exponential advantage, contrasting with the limitations of classical outcomes and existing quantum resources.
Findings
Classical measurement outcomes require exponential queries for the task.
Access to post-measurement states allows constant-query solutions.
Post-measurement states are a new, crucial resource for measurement learning.
Abstract
Learning properties of quantum states and channels is known to benefit from resources such as entangled operations, auxiliary qubits, and adaptivity, whereas the resource structure of measurement learning, namely, learning properties of quantum measurement operators, remains poorly understood. In this work, we identify a measurement learning task for which access limited to classical measurement outcomes leads to an exponential lower bound on the query complexity, established via a distinguishing task between a genuine quantum projective measurement and a purely classical random number generator. Remarkably, this hardness persists even when arbitrary entangled operations, auxiliary systems, and fully adaptive strategies are allowed, indicating that conventional resources for state and channel learning are ineffective in this task. In contrast, when access to the post-measurement quantum…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Quantum Mechanics and Applications
