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This research demonstrates how general qubit connectivity graphs can eliminate the depth slowdown typically associated with generating matchgate designs, offering significant speedups for fermionic tomography algorithms on all-to-all connected quantum hardware. By leveraging random walks on the matchgate group without ancillae, the authors achieve optimal-depth circuits that outperform strictly one-dimensional counterparts. These findings advance our understanding of resource efficiency in near-term quantum processors and their ability to simulate complex fermionic systems.
Strong matchgate designs in nearly optimal depth
arXiv:2609.26677v1 Announce Type: new Abstract: Understanding the resources required to generate approximately random unitaries over various groups is a natural goal of quantum information theory. With respect to one notion of approximation, that of a design, it is known that the full unitary group can be approximated in logarithmic depth by one-dimensional circuits of nearest-neighbour 2-local gates. On the other hand, remarkably, circuits with this connectivity cannot form designs over the matchgate group in sublinear depth. Here we show that this dramatic slowdown can disappear when using a general qubit connectivity graph $\mathsf{G}$ of routing number ${\rm rt}(\mathsf{G})$. Indeed, in this setting one can obtain (strong) $\varepsilon$-approximate relative error matchgate $k$-designs in depth $\mathcal{O}(k^2{\rm rt}(\mathsf{G})\log n\log(n/\varepsilon))$. For all-to-all connectivity, ${\rm rt}(\mathsf{G})=2$. Our construction is conceptually simple, involving a random walk on the matchgate group, and no ancillae. As a technical byproduct, we improve upon the state of the art for fermionic routing, obtaining an $\mathcal{O}({\rm rt}(\mathsf{G})\log n)$ depth router. Additionally, for $k=3$, we obtain an exact strong matchgate design in $\mathcal{O}({\rm rt}(\mathsf{G})\log n)$ depth, again without ancillae. Under all-to-all connectivity, our fermionic router and 3-designs are optimal. Notably, our results imply that quantum algorithms for fermionic tomography which require drawing from a matchgate 3-design may be exponentially sped up on quantum computers with all-to-all connectivity, relative to their strictly one-dimensional counterparts.
Abstract:Understanding the resources required to generate approximately random unitaries over various groups is a natural goal of quantum information theory. With respect to one notion of approximation, that of a design, it is known that the full unitary group can be approximated in logarithmic depth by one-dimensional circuits of nearest-neighbour 2-local gates. On the other hand, remarkably, circuits with this connectivity cannot form designs over the matchgate group in sublinear depth. Here we show that this dramatic slowdown can disappear when using a general qubit connectivity graph $\mathsf{G}$ of routing number ${\rm rt}(\mathsf{G})$. Indeed, in this setting one can obtain (strong) $\varepsilon$-approximate relative error matchgate $k$-designs in depth $\mathcal{O}(k^2{\rm rt}(\mathsf{G})\log n\log(n/\varepsilon))$. For all-to-all connectivity, ${\rm rt}(\mathsf{G})=2$. Our construction is conceptually simple, involving a random walk on the matchgate group, and no ancillae. As a technical byproduct, we improve upon the state of the art for fermionic routing, obtaining an $\mathcal{O}({\rm rt}(\mathsf{G})\log n)$ depth router. Additionally, for $k=3$, we obtain an exact strong matchgate design in $\mathcal{O}({\rm rt}(\mathsf{G})\log n)$ depth, again without ancillae. Under all-to-all connectivity, our fermionic router and 3-designs are optimal. Notably, our results imply that quantum algorithms for fermionic tomography which require drawing from a matchgate 3-design may be exponentially sped up on quantum computers with all-to-all connectivity, relative to their strictly one-dimensional counterparts.
arXiv:2609.26677 [quant-ph]
(or arXiv:2609.26677v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2609.26677 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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