Characterization of multimode linear optical networks

Hoch, Francesco; Giordani, Taira; Spagnolo, Nicolò; Crespi, Andrea; Osellame, Roberto; Sciarrino, Fabio

doi:10.1117/1.apn.2.1.016007

Cited by 4 publications

(3 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Interesting topics for future work include developing more efficient acceleration methods. While lightweight networks 61 with higher fitting ability are desirable, optical neural networks may be a possible way for such a fast reconstruction task 62 , 63 …”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Deep image prior plus sparsity prior: toward single-shot full-Stokes spectropolarimetric imaging with a multiple-order retarder

Han

et al. 2023

Adv. Photon. Nexus

View full text Add to dashboard Cite

Compressive full-Stokes spectropolarimetric imaging (SPI), integrating passive polarization modulator (PM) into general imaging spectrometer, is powerful enough to capture high-dimensional information via incomplete measurement; a reconstruction algorithm is needed to recover 3D data cube (x , y , and λ) for each Stokes parameter. However, existing PMs usually consist of complex elements and enslave to accurate polarization calibration, current algorithms suffer from poor imaging quality and are subject to noise perturbation. In this work, we present a single multiple-order retarder followed a polarizer to implement passive spectropolarimetric modulation. After building a unified forward imaging model for SPI, we propose a deep image prior plus sparsity prior algorithm for high-quality reconstruction. The method based on untrained network does not need training data or accurate polarization calibration and can simultaneously reconstruct the 3D data cube and achieve self-calibration. Furthermore, we integrate the simplest PM into our miniature snapshot imaging spectrometer to form a single-shot SPI prototype. Both simulations and experiments verify the feasibility and outperformance of our SPI scheme. It provides a paradigm that allows general spectral imaging systems to become passive full-Stokes SPI systems by integrating the simplest PM without changing their intrinsic mechanism.

show abstract

Section: Discussionmentioning

confidence: 99%

“…While lightweight networks 61 with higher fitting ability are desirable, optical neural networks may be a possible way for such a fast reconstruction task. 62,63…”

Section: Acceleration Strategymentioning

confidence: 99%

Deep image prior plus sparsity prior: toward single-shot full-Stokes spectropolarimetric imaging with a multiple-order retarder

Han

et al. 2023

Adv. Photon. Nexus

View full text Add to dashboard Cite

show abstract

“…However, compiling a linear optical unitary over natural ansatze such as the triangular decomposition (also known as Reck-Zeilinger decomposition) [13] or rectangular decomposition [14] can indeed be considered as a type of CV quantum process tomography [15][16][17][18][19][20][21] because each matrix element of a linear optical circuit corresponds to a physical parameter (namely, a complex transmissivity coupling two modes) which can be computed efficiently from the beamsplitter transmissivities and phase shifts of the ansatz. Specific methods for CV quantum process tomography have been introduced in the special case of characterizing linear optical circuits [22][23][24][25][26][27][28][29][30][31][32]. These methods have not previously made use of hybrid quantum-classical algorithms.…”

Section: Introductionmentioning

confidence: 99%

Learning linear optical circuits with coherent states

Volkoff,

Sornborger

2024

J. Phys. A: Math. Theor.

View full text Add to dashboard Cite

We analyze the energy and training data requirements for supervised learning of an $M$-mode linear optical circuit by minimizing an empirical risk defined solely from the action of the circuit on coherent states. When the linear optical circuit acts non-trivially only on $k<M$ unknown modes (i.e., a linear optical $k$-junta), we provide an energy-efficient, adaptive algorithm that identifies the junta set and learns the circuit. We compare two schemes for allocating a total energy, $E$, to the learning algorithm. In the first scheme, each of the $T$ random training coherent states has energy $E/T$. In the second scheme, a single random $MT$-mode coherent state with energy $E$ is partitioned into $T$ training coherent states. The latter scheme exhibits a polynomial advantage in training data size sufficient for convergence of the empirical risk to the full risk due to concentration of measure on the $(2MT-1)$-sphere. Specifically, generalization bounds for both schemes are proven, which indicate that for $\epsilon$-approximation of the full risk by the empirical risk with high probability, $O(E^{2/3}M^{2/3}/\epsilon^{2/3})$ training states are sufficient for the first scheme and $O(E^{1/3}M^{1/3}/\epsilon^{2/3})$ training states are sufficient for the second scheme.

show abstract

Experimental certification of contextuality, coherence, and dimension in a programmable universal photonic processor

Giordani,

Wagner,

Esposito

et al. 2023

Sci. Adv.

View full text Add to dashboard Cite

Quantum superposition of high-dimensional states enables both computational speed-up and security in cryptographic protocols. However, the exponential complexity of tomographic processes makes certification of these properties a challenging task. In this work, we experimentally certify coherence witnesses tailored for quantum systems of increasing dimension using pairwise overlap measurements enabled by a six-mode universal photonic processor fabricated with a femtosecond laser writing technology. In particular, we show the effectiveness of the proposed coherence and dimension witnesses for qudits of dimensions up to 5. We also demonstrate advantage in a quantum interrogation task and show it is fueled by quantum contextuality. Our experimental results testify to the efficiency of this approach for the certification of quantum properties in programmable integrated photonic platforms.

show abstract

Characterization of multimode linear optical networks

Cited by 4 publications

References 49 publications

Deep image prior plus sparsity prior: toward single-shot full-Stokes spectropolarimetric imaging with a multiple-order retarder

Deep image prior plus sparsity prior: toward single-shot full-Stokes spectropolarimetric imaging with a multiple-order retarder

Learning linear optical circuits with coherent states

Experimental certification of contextuality, coherence, and dimension in a programmable universal photonic processor

Contact Info

Product

Resources

About