Skip to content
aiqbench takeQuantum Computing

aiqbench analysis

This research addresses fundamental limits in quantum data compression, demonstrating how adaptive queries can achieve optimal space efficiency up to logarithmic factors while providing near-optimal non-adaptive schemes. The work advances the theoretical understanding of information retrieval within quantum query models, offering new benchmarks for future quantum memory architectures.
Source coveringarXiv
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Optimal compression with quantum retrieval

arXiv2 min read

arXiv:2610.06702v1 Announce Type: new Abstract: We consider the following data compression problem. Given a string $x \in \{0,1\}^m$ of Hamming weight at most $n$, compress it into a shorter string $y \in \{0,1\}^s$ so that any bit $x_i$ of $x$ can be retrieved without any error using at most $t$ quantum queries to the standard oracle encoding of $y$. If queries are allowed to be adaptive we show how optimal compression up to a logarithmic factor can be achieved. If the queries are required to be made non-adaptively, we show schemes whose space is optimal in its dependence on $m$ except for a logarithmic factor, and is at most quadratically worse when compared to the optimum in its dependence on $n$.

Abstract:We consider the following data compression problem. Given a string $x \in \{0,1\}^m$ of Hamming weight at most $n$, compress it into a shorter string $y \in \{0,1\}^s$ so that any bit $x_i$ of $x$ can be retrieved without any error using at most $t$ quantum queries to the standard oracle encoding of $y$. If queries are allowed to be adaptive we show how optimal compression up to a logarithmic factor can be achieved. If the queries are required to be made non-adaptively, we show schemes whose space is optimal in its dependence on $m$ except for a logarithmic factor, and is at most quadratically worse when compared to the optimum in its dependence on $n$.

Quantum Physics (quant-ph); Data Structures and Algorithms (cs.DS); Information Retrieval (cs.IR); Information Theory (cs.IT)

arXiv:2610.06702 [quant-ph]

(or arXiv:2610.06702v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.06702 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Full text under the publisher's open licence · 221 words

Related coverage