2015
DOI: 10.1007/s00041-015-9395-0
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Factoring Matrices into the Product of Circulant and Diagonal Matrices

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Cited by 19 publications
(24 citation statements)
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“…Nevertheless, a sequence of alternating pulse shapers and EOMs is sufficient to reproduce any matrix transformation. As demonstrated by a recent constructive proof [46], any N × N complex matrix can be factored exactly as the product of no more than 2N − 1 circulant and diagonal matrices-or equivalently of 2N − 1 diagonal matrices spaced by DFT matrices. As described in the following section, such a decomposition is precisely that provided by EOMs and pulse shapers when discretized for numerical simulation, thereby implying that the number of components needed to implement an arbitrary unitary transformation on N spectral modes scales like O(N ).…”
Section: Protocol Componentsmentioning
confidence: 99%
“…Nevertheless, a sequence of alternating pulse shapers and EOMs is sufficient to reproduce any matrix transformation. As demonstrated by a recent constructive proof [46], any N × N complex matrix can be factored exactly as the product of no more than 2N − 1 circulant and diagonal matrices-or equivalently of 2N − 1 diagonal matrices spaced by DFT matrices. As described in the following section, such a decomposition is precisely that provided by EOMs and pulse shapers when discretized for numerical simulation, thereby implying that the number of components needed to implement an arbitrary unitary transformation on N spectral modes scales like O(N ).…”
Section: Protocol Componentsmentioning
confidence: 99%
“…Since an arbitrary unitary matrix of size (2 N sb + 1) × (2 N sb + 1) has real degrees of freedom whereas N f ≤2 N sb , we conclude that a single modulated ring is insufficient to approximate an arbitrary unitary matrix to a high degree of accuracy, even if all modulation tones up to 2 N sb Ω R are used. To overcome this problem, notice that products of unitary transformations are also unitary 37 . Therefore, as shown in Fig.…”
Section: Resultsmentioning
confidence: 99%
“…While difficult to extrapolate with such a limited number of data points, we observe that all matrices fall under the scaling cap Q = 2N + 1; in other words, for a given N , no operation requires more than 2N + 1 elements to be realized with F, P ≥ 0.99. Should this cap hold for larger dimensions as well, it would show that-at least for these particular matrices-the linear scaling Q ∝ N expected for arbitrary temporal/spectral AFP patterns [12,20] can hold even for the significantly restricted class of sinewave-only temporal modulation. In order to examine scaling in our AFP approach in greater detail, though, it is important not only consider the circuit depth Q, but also the effective bandwidth.…”
Section: B Sinewave Modulationmentioning
confidence: 99%