Bottleneck and greedy largest-weight variants of Birkhoff-von Neumann decomposition reduce permutation count to O(N log(1/ε)) or ~2N for dense matrices, lowering LCU ancilla width and enabling α=1 normalization.
Exploiting all ancilla outcomes in linear combinations of unitaries: low-rank recovery and quantum trapdoor functions
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
The linear combination of unitaries (LCU) is a fundamental quantum algorithm primitive that embeds non-unitary operators via post-selection on an ancilla register. In standard LCU, only the $|0\dots0\rangle$ ancilla outcome is retained; the remaining "junk" outcomes are discarded. We study these discarded parts by introducing an alternative LCU circuit which simplifies the coefficient preparation unitary with Hadamard gates and a single rotation qubit. Every computational basis measurement of the ancilla projects the system onto a different linear combination of the target unitaries. Collecting these outcome states and reshaping them into a $2K\times N$ matrix reveals a factorization $\Phi = C X$, where $C$ encodes the coefficients and $X$ contains the action of each unitary on the input; this immediately shows $\operatorname{rank}(\Phi)\le K$. This structure enables two complementary applications: (i) classical low-rank matrix completion can reconstruct the full output (including the target) from a fraction of its entries, turning every shot into useful information; (ii) treating $C$ as a secret key hides the input state, leading to a candidate quantum trapdoor function and symmetric encryption. The scheme thus turns the "junk" ancilla outcomes into a structured resource, possibly opening paths for further applications.
fields
quant-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
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Lowering LCU Circuit Width through Maximum-Weight Birkhoff-von Neumann Decomposition
Bottleneck and greedy largest-weight variants of Birkhoff-von Neumann decomposition reduce permutation count to O(N log(1/ε)) or ~2N for dense matrices, lowering LCU ancilla width and enabling α=1 normalization.