Quantum circuits with many photons on a programmable nanophotonic chip

Arrazola, Juan Miguel; Bergholm, Ville; Brádler, Kamil; Bromley, Thomas R.; Collins, Matthew J.; Dhand, Ish; Fumagalli, Andrea; Gerrits, Thomas; Goussev, Arseni; Helt, L. G.; Hundal, Jasbir S.; Isacsson, Theodor; Israel, Robert B.; Izaac, Josh; Jahangiri, Soran; Janik, R.; Killoran, Nathan; Kumar, Shreya P.; Lavoie, Jonathan; Lita, Adriana E.; Mahler, Dylan H.; Menotti, M.; Morrison, Blair; Nam, Sae Woo; Neuhaus, Leonhard; Qi, Haoyu; Quesada, Nicolás; Repingon, A.; Sabapathy, Krishna Kumar; Schuld, Maria; Su, Daiqin; Swinarton, Jeremy; Száva, Antal; Tan, Kuan Yen; Tan, Phoumra; Vaidya, Varun; Vernon, Z.; Zabaneh, Zeid; Zhang, Y.

doi:10.1038/s41586-021-03202-1

Cited by 458 publications

(319 citation statements)

References 53 publications

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“…Such robustness on the multiple cases of mode mismatch is made possible because of the fully degenerate nature of the self-imaging OPO, which cannot be accomplished by using a confocal OPO 18 or two single-mode OPOs 16 . Our work of mitigating the mode mismatching loss will have broad applications to quantum technologies based on squeezed light, e.g., quantum-enhanced gravitational-wave detection 11 , 12 , deterministic quantum teleportation 7 , measurement-based quantum computing 1 – 4 , and Gaussian boson sampling 5 , 6 .…”

Section: Discussionmentioning

confidence: 99%

Robust squeezed light against mode mismatch using a self imaging optical parametric oscillator

Roh

Gwak

2021

Sci Rep

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We present squeezed light that is robust against spatial mode mismatch (beam displacement, tilt, and beam-size difference), which is generated from a self-imaging optical parametric oscillator below the threshold. We investigate the quantum properties of the generated light when the oscillator is detuned from the ideal self-imaging condition for stable operation. We find that the generated light is more robust to mode mismatch than single-mode squeezed light having the same squeezing level, and it even outperforms the single-mode infinitely squeezed light as the strength of mode mismatch increases.

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Section: Discussionmentioning

confidence: 99%

Robust squeezed light against mode mismatch using a self imaging optical parametric oscillator

Roh

Gwak

2021

Sci Rep

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“…This problem corresponds to the renowned Gaussian Boson sampling (Hamilton et al 2017;Quesada et al 2018), which recently demonstrated the quantum advantage at an impressing scale (Zhong et al 2020), following earlier realizations (Tillmann et al 2012;Broome et al 2013;Spring et al 2013;Spagnolo et al 2014;Carolan et al 2014;Wang et al 2019) of the original proposal by Aharanson and Arkhipov (Aaronson and Arkhipov 2013). The theory of Gaussian Boson sampling (GBS) heavily relies on phase-space methods (Kruse et al 2019), making it an exciting NN test-bed supported by recently reported trainable hardware (Arrazola et al 2021;Hoch et al 2021;Zhong et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Training Gaussian boson sampling by quantum machine learning

Conti

2021

Quantum Mach. Intell.

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We use neural networks to represent the characteristic function of many-body Gaussian states in the quantum phase space. By a pullback mechanism, we model transformations due to unitary operators as linear layers that can be cascaded to simulate complex multi-particle processes. We use the layered neural networks for non-classical light propagation in random interferometers, and compute boson pattern probabilities by automatic differentiation. This is a viable strategy for training Gaussian boson sampling. We demonstrate that multi-particle events in Gaussian boson sampling can be optimized by a proper design and training of the neural network weights. The results are potentially useful to the creation of new sources and complex circuits for quantum technologies.

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“…In this paper, we present two photonic integrated reconfigurable linear processors based on 2 × 2 interferometric elements [5]. These devices are versatile, being exploited over the years for optical processing and filtering [6], programmable true-time optical delay lines [7], and quantum information processing [8,9]. They are also suited for realizing the linear operations (i.e., matrix-vector multiplications) required in each DNN layer.…”

Section: Introductionmentioning

confidence: 99%

Photonic Integrated Reconfigurable Linear Processors as Neural Network Accelerators

et al. 2021

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Reconfigurable linear optical processors can be used to perform linear transformations and are instrumental in effectively computing matrix–vector multiplications required in each neural network layer. In this paper, we characterize and compare two thermally tuned photonic integrated processors realized in silicon-on-insulator and silicon nitride platforms suited for extracting feature maps in convolutional neural networks. The reduction in bit resolution when crossing the processor is mainly due to optical losses, in the range 2.3–3.3 for the silicon-on-insulator chip and in the range 1.3–2.4 for the silicon nitride chip. However, the lower extinction ratio of Mach–Zehnder elements in the latter platform limits their expressivity (i.e., the capacity to implement any transformation) to 75%, compared to 97% of the former. Finally, the silicon-on-insulator processor outperforms the silicon nitride one in terms of footprint and energy efficiency.

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Quantum circuits with many photons on a programmable nanophotonic chip

Cited by 458 publications

References 53 publications

Robust squeezed light against mode mismatch using a self imaging optical parametric oscillator

Robust squeezed light against mode mismatch using a self imaging optical parametric oscillator

Training Gaussian boson sampling by quantum machine learning

Photonic Integrated Reconfigurable Linear Processors as Neural Network Accelerators

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