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This paper details a specific quantum circuit construction for block encoding semiseparable matrices using only their generators, leveraging cyclic shifts and Hadamard gates to achieve polylogarithmic depth. The work advances the toolkit for efficient linear algebra operations on quantum hardware, which is essential for scaling quantum algorithms in simulation and optimization tasks.
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Block encoding of semiseparable matrices via their generators

arXiv1 min read

arXiv:2610.13228v1 Announce Type: new Abstract: We present an explicit quantum circuit that constructs a block encoding of a generator-representable semiseparable (one-pair) matrix directly from its two generators. For an $N\times N$ semiseparable matrix with $N=2^{n}$, the circuit uses $2n+2$ ancilla qubits and has $O({\rm polylog} N)$ depth, and it realizes a $(2\sqrt{N},\,2n+2,\,2 \sqrt{2}\ \varepsilon)$-block encoding, where $\varepsilon$ is controlled by the accuracy of the state-preparation routines that load the generators. The building blocks of the circuit are cyclic shifts, swaps, and Hadamard gates, each admitting an efficient implementation through the binary decomposition of the relevant indices.

Abstract:We present an explicit quantum circuit that constructs a block encoding of a generator-representable semiseparable (one-pair) matrix directly from its two generators. For an $N\times N$ semiseparable matrix with $N=2^{n}$, the circuit uses $2n+2$ ancilla qubits and has $O({\rm polylog} N)$ depth, and it realizes a $(2\sqrt{N},\,2n+2,\,2 \sqrt{2}\ \varepsilon)$-block encoding, where $\varepsilon$ is controlled by the accuracy of the state-preparation routines that load the generators. The building blocks of the circuit are cyclic shifts, swaps, and Hadamard gates, each admitting an efficient implementation through the binary decomposition of the relevant indices.

Quantum Physics (quant-ph); Numerical Analysis (math.NA)

arXiv:2610.13228 [quant-ph]

(or arXiv:2610.13228v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.13228 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 188 words

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