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This paper presents a significant theoretical advance in quantum circuit synthesis, establishing a method that simultaneously optimizes both circuit size and depth relative to ancilla qubit budgets. By encoding basis states into Gaussian wavefunctions, the authors achieve asymptotically optimal tradeoffs for universal quantum computation without relying on approximation errors. This work directly impacts the efficiency of compiling complex unitary transformations into practical gate sequences for near-term and fault-tolerant quantum processors.
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Exact Unitary Synthesis: Simultaneously Optimal in Size and Depth-Ancilla Tradeoff

arXiv2 min read

arXiv:2610.07010v1 Announce Type: new Abstract: Unitary synthesis underlies universal quantum computation by translating unitary transformations into circuits of elementary gates. We show that for any budget $m\geq 0$ of ancilla, every $n$-qubit unitary can be exactly implemented using a quantum circuit consisting of $O(4^n)$ CNOT and single-qubit gates in $O(n+4^n/(n+m))$ layers attaining the asymptotically optimal depth--ancilla tradeoff while simultaneously achieving optimal circuit size. The key idea is to encode computational basis states as wavefunctions sharing a Gaussian envelope, converting unitary action on amplitudes into coordinate transformations that admit shallow circuit implementations. The continuous encoding is then discretized, with approximation errors removed by exact residual correction. The depth-ancilla tradeoff follows by recursively applying the quantum Shannon decomposition, synthesizing its smaller unitary factors, and reusing ancillary qubits.

Abstract:Unitary synthesis underlies universal quantum computation by translating unitary transformations into circuits of elementary gates. We show that for any budget $m\geq 0$ of ancilla, every $n$-qubit unitary can be exactly implemented using a quantum circuit consisting of $O(4^n)$ CNOT and single-qubit gates in $O(n+4^n/(n+m))$ layers attaining the asymptotically optimal depth--ancilla tradeoff while simultaneously achieving optimal circuit size. The key idea is to encode computational basis states as wavefunctions sharing a Gaussian envelope, converting unitary action on amplitudes into coordinate transformations that admit shallow circuit implementations. The continuous encoding is then discretized, with approximation errors removed by exact residual correction. The depth-ancilla tradeoff follows by recursively applying the quantum Shannon decomposition, synthesizing its smaller unitary factors, and reusing ancillary qubits.

arXiv:2610.07010 [quant-ph]

(or arXiv:2610.07010v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.07010 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 211 words

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