aiqbench analysis
This research paper presents nearly optimal tradeoffs for the circuit size, depth, and ancilla count required to implement quantum oracles for various Boolean functions. By establishing asymptotically tight bounds up to logarithmic factors, it provides a theoretical foundation crucial for optimizing resource usage in complex quantum algorithms like QROM implementations.
Nearly optimal quantum circuits for Boolean oracles
arXiv:2607.28402v1 Announce Type: new Abstract: Quantum oracle of Boolean functions is one of the central bridges between classical and quantum algorithms, but the study focusing at quantum circuit optimization of such oracle is yet closed. In this paper, we propose nearly optimal tradeoffs among circuit size, circuit depth and ancilla count, for quantum oracles of three kinds of Boolean functions: general total Boolean functions with output size $b$: with $1\le m\le\Theta\left(\frac{2^n}{n}\right)$ ancilla, size $\mathcal{O}\left(\frac{b2^n}{\log(n+m)}\right)$, depth $\mathcal{O}\left(\frac{b2^n}{n+m}\right)$; partial Boolean functions of effective support size $d$ and output size $b$: with $\Theta\left(\log d\right)\le m\le \Theta\left(d\right)$ ancilla, size $\mathcal{O}\left(n\log d+bd\right)$, depth $\mathcal{O}\left(\frac{n\log n\log d}{n+m}+\log n+\frac{d(\log d+b\log m)}{m}\right)$; sparse Boolean functions of true input size $d$: with $\Theta\left(\log n+\log d\right)\le m\le\Theta\left(\frac{nd}{\log d}\right)$ ancilla, size $\mathcal{O}\left(n^2\log d+\frac{nd}{\log(\log d+m/n)}\right)$, depth $\mathcal{O}\left(\frac{n^2\log n\log d}{n+m}+\log n+\frac{nd}{m}\right)$. All the size and depth bounds are asymptotically optimal up to logarithmic factors in the corresponding ancilla count regions. We hope these results find applications in scenarios where classical procedures are needed to be embedded into quantum circuits, such as QROM implementation and quantum algorithm design.
Abstract:Quantum oracle of Boolean functions is one of the central bridges between classical and quantum algorithms, but the study focusing at quantum circuit optimization of such oracle is yet closed. In this paper, we propose nearly optimal tradeoffs among circuit size, circuit depth and ancilla count, for quantum oracles of three kinds of Boolean functions: general total Boolean functions with output size $b$: with $1\le m\le\Theta\left(\frac{2^n}{n}\right)$ ancilla, size $\mathcal{O}\left(\frac{b2^n}{\log(n+m)}\right)$, depth $\mathcal{O}\left(\frac{b2^n}{n+m}\right)$; partial Boolean functions of effective support size $d$ and output size $b$: with $\Theta\left(\log d\right)\le m\le \Theta\left(d\right)$ ancilla, size $\mathcal{O}\left(n\log d+bd\right)$, depth $\mathcal{O}\left(\frac{n\log n\log d}{n+m}+\log n+\frac{d(\log d+b\log m)}{m}\right)$; sparse Boolean functions of true input size $d$: with $\Theta\left(\log n+\log d\right)\le m\le\Theta\left(\frac{nd}{\log d}\right)$ ancilla, size $\mathcal{O}\left(n^2\log d+\frac{nd}{\log(\log d+m/n)}\right)$, depth $\mathcal{O}\left(\frac{n^2\log n\log d}{n+m}+\log n+\frac{nd}{m}\right)$. All the size and depth bounds are asymptotically optimal up to logarithmic factors in the corresponding ancilla count regions. We hope these results find applications in scenarios where classical procedures are needed to be embedded into quantum circuits, such as QROM implementation and quantum algorithm design.
arXiv:2607.28402 [quant-ph]
(or arXiv:2607.28402v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2607.28402 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 260 words
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