An Optimal Design for Universal Multiport Interferometers

By William R. Clements, Peter Conway Humphreys, B. Metcalf, W. Kolthammer, Ian A. Walsmley. 2016

1,084 citations recorded by the source corpus.

Universal multiport interferometers, which can be programmed to implement any linear transformation between multiple channels, are emerging as a powerful tool for both classical and quantum photonics. These interferometers are typically composed of a regular mesh of beam splitters and phase shifters, allowing for straightforward fabrication using integrated photonic architectures and ready scalability. The current, standard design for universal multiport interferometers is based on work by Reck et al (Phys. Rev. Lett. 73, 58, 1994). We demonstrate a new design for universal multiport interferometers based on an alternative arrangement of beam splitters and phase shifters, which outperforms that by Reck et al. Our design occupies half the physical footprint of the Reck design and is significantly more robust to optical losses.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/381c820cb4403a7416a18aeea469b9f4337d8e7b

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Citations in the corpus

Photonic quantum information processing: A concise review (2019, methodology): “A way to realize an arbitrary n -dimensional unitary transformation on the mode space, with linear optics, has been outlined by Reck et al. 35 quite some time ago, with recent improvements 36 and expansions.”
Strawberry Fields: A Software Platform for Photonic Quantum Computing (2018, methodology): “In order to allow for arbitrary linear unitaries on m imput modes, we must have a minimum of m+ 1 columns in the beamsplitter array [63].”
Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs (2016, background): “For example (Clements et al., 2016), a unitary matrix can be decomposed as WN = D ( R (1) 1,2R (1) 3,4 . . .”
Large-Scale Optical Neural Networks based on Photoelectric Multiplication (2018, background): “tal). The (optical) energy consumption of nanophotonic systems [23, 24] also scales linearly for the same operation; however, the circuit is much more complex, requiring O(NN0) tunable phase shifters [38, 39] or ring resonators [24], which becomes very challenging to scale beyond several hundred cha”
Quantum circuits with many photons on a programmable nanophotonic chip (2021, methodology): “The interferometer is composed of a network of MMIs and phase shifters in a rectangular configuration [47].”
Classical boson sampling algorithms with superior performance to near-term experiments (2017, background): “Since path-dependent loss is usually small in experiments, and can be mitigated by interferometer design [14], the matrix K/||K||2 ≈ U .”
Experimentally realized in situ backpropagation for deep learning in photonic neural networks (2022, background): “Examples of universal matrix units are triangular [8, 12], rectangular [50], cascaded binary tree [37], and cosine-sine decomposition [51] (which is more useful for quantum applications of this scheme, but can be represented classically).”