aiqbench analysis
This research establishes asymptotically optimal T-count bounds for sparse quantum read-only memory (QROM), demonstrating square-root scaling relative to support size and message length via a multilevel hashing scheme. By proving matching lower bounds even under mid-circuit measurements, the work provides critical efficiency limits for state preparation and block encoding in fault-tolerant algorithms. These results are essential for minimizing resource overhead when integrating classical data access into large-scale quantum computations.
Optimal T Counts under Sparsity: from QROM to State Preparation and Block Encoding
arXiv:2607.28260v1 Announce Type: new Abstract: Many quantum algorithms require coherent access to classical data, often modeled by quantum read-only memory (QROM). We initiate the study of the $T$ count of sparse QROM, in which only $s$ of the $2^n$ addresses store nonzero data. We prove asymptotically optimal $T$-count bounds $\Theta(\sqrt{sm} + \sqrt{sn})$ with square-root dependence on the support size $s$ and message length $m$. Our upper bounds use a multilevel hashing scheme, while our lower bounds reduce sparse QROM to state preparation and use counting arguments for adaptive Clifford+$T$ circuits. The lower bounds thus hold even when mid-circuit measurements and classically controlled operations are allowed. As applications, we obtain matching $T$-count bounds $\Theta(\sqrt{sn} + \sqrt{s\log(1/\varepsilon)} + \log(1/\varepsilon))$ for $s$-sparse state preparation and $\Theta( \sqrt{2^n sn} + \sqrt{2^n s\log(s/\varepsilon_{\mathrm{BE}})} + \log(s/\varepsilon_{\mathrm{BE}}))$ for block encoding of $s$-sparse matrices, where $\varepsilon$ and $\varepsilon_{\mathrm{BE}}$ are the precision of state preparation and block encoding, respectively.
Abstract:Many quantum algorithms require coherent access to classical data, often modeled by quantum read-only memory (QROM). We initiate the study of the $T$ count of sparse QROM, in which only $s$ of the $2^n$ addresses store nonzero data. We prove asymptotically optimal $T$-count bounds $\Theta(\sqrt{sm} + \sqrt{sn})$ with square-root dependence on the support size $s$ and message length $m$. Our upper bounds use a multilevel hashing scheme, while our lower bounds reduce sparse QROM to state preparation and use counting arguments for adaptive Clifford+$T$ circuits. The lower bounds thus hold even when mid-circuit measurements and classically controlled operations are allowed. As applications, we obtain matching $T$-count bounds $\Theta(\sqrt{sn} + \sqrt{s\log(1/\varepsilon)} + \log(1/\varepsilon))$ for $s$-sparse state preparation and $\Theta( \sqrt{2^n sn} + \sqrt{2^n s\log(s/\varepsilon_{\mathrm{BE}})} + \log(s/\varepsilon_{\mathrm{BE}}))$ for block encoding of $s$-sparse matrices, where $\varepsilon$ and $\varepsilon_{\mathrm{BE}}$ are the precision of state preparation and block encoding, respectively.
Quantum Physics (quant-ph); Computational Complexity (cs.CC); Data Structures and Algorithms (cs.DS)
arXiv:2607.28260 [quant-ph]
(or arXiv:2607.28260v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2607.28260 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 · 247 words
Related coverage

Sparse quantum state preparation with improved Toffoli cost
Quantum Journal · Sep 10, 2026

University of Wisconsin team releases open-source quantum encoding tool
Quantum Zeitgeist · Sep 1, 2026
Quantum phase estimation with optimal confidence interval using three control qubits
Quantum Journal · Aug 19, 2026

Run High-Performance Core Math at Scale with NVIDIA nvmath-python
NVIDIA · Jul 30, 2026