Quantum computational advantage via high-dimensional Gaussian boson sampling

Deshpande, Abhinav; Mehta, Arthur; Vincent, Trevor; Quesada, Nicolás; Hinsche, Marcel; Ioannou, Marios; Madsen, Lars S.; Lavoie, Jonathan; Qi, Haoyu; Eisert, Jens; Hangleiter, Dominik; Fefferman, Bill; Dhand, Ish

doi:10.1126/sciadv.abi7894

Cited by 78 publications

(57 citation statements)

References 63 publications

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“…Here we report quantum computational advantage using Borealis, a photonic processor offering dynamic programmability on all gates implemented. We carry out Gaussian boson sampling 4 (GBS) on 216 squeezed modes entangled with three-dimensional connectivity 5 , using a time-multiplexed and photon-number-resolving architecture. On average, it would take more than 9,000 years for the best available algorithms and supercomputers to produce, using exact methods, a single sample from the programmed distribution, whereas Borealis requires only 36 μs.…”

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confidence: 99%

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Quantum computational advantage with a programmable photonic processor

Madsen

Laudenbach

Askarani

et al. 2022

Nature

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562

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A quantum computer attains computational advantage when outperforming the best classical computers running the best-known algorithms on well-defined tasks. No photonic machine offering programmability over all its quantum gates has demonstrated quantum computational advantage: previous machines1,2 were largely restricted to static gate sequences. Earlier photonic demonstrations were also vulnerable to spoofing3, in which classical heuristics produce samples, without direct simulation, lying closer to the ideal distribution than do samples from the quantum hardware. Here we report quantum computational advantage using Borealis, a photonic processor offering dynamic programmability on all gates implemented. We carry out Gaussian boson sampling4 (GBS) on 216 squeezed modes entangled with three-dimensional connectivity5, using a time-multiplexed and photon-number-resolving architecture. On average, it would take more than 9,000 years for the best available algorithms and supercomputers to produce, using exact methods, a single sample from the programmed distribution, whereas Borealis requires only 36 μs. This runtime advantage is over 50 million times as extreme as that reported from earlier photonic machines. Ours constitutes a very large GBS experiment, registering events with up to 219 photons and a mean photon number of 125. This work is a critical milestone on the path to a practical quantum computer, validating key technological features of photonics as a platform for this goal.

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confidence: 99%

“… Gaussian boson sampling is performed on 216 squeezed modes entangled with three-dimensional connectivity 5 , using Borealis, registering events with up to 219 photons and a mean photon number of 125. …”

mentioning

confidence: 99%

Quantum computational advantage with a programmable photonic processor

Madsen

Laudenbach

Askarani

et al. 2022

Nature

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562

253

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“…Gaussian boson sampling is one of the sampling problems that are proved hard for classical computers under some plausible assumptions [6,11]. To implement Gaussian boson sampling, one prepares the product of M single-mode Gaussian states, |ψ in , and the state into an M -mode linear-optical circuit Ûlin , composed of beam splitters, and then measures the output state |ψ out = Ûlin |ψ in by photon-number detectors.…”

Section: B Gaussian Boson Sampling For Molecular Vibronic Spectra Gen...mentioning

confidence: 99%

“…Recently, it has been proposed to employ a quantum simulator, the so-called Gaussian boson sampler [6], to efficiently * changhun@uchicago.edu generate the spectra [8]. Gaussian boson sampling is a task that is believed to be hard for classical computers under plausible computational complexity assumptions [6,10,11]. Therefore, molecular vibronic spectra has been a candidate for applications of quantum simulators [12].…”

Section: Introductionmentioning

confidence: 99%

Quantum-inspired classical algorithm for molecular vibronic spectra

Oh¹,

Lim²,

Wong³

et al. 2022

Preprint

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Molecular vibronic spectra is an essential property of molecules, which is an important tool to analyze chemical components and study molecular structures. Computing molecular vibronic spectra has been a challenging task, and its best-known classical algorithm scales combinatorially in the size of the system. Recently, a quantum simulator, specifically Gaussian boson sampling, has been proposed to generate the spectra efficiently. Thus, molecular vibronic spectra is a candidate of tasks for which a quantum device provides a computational advantage. In this work, we propose a quantum-inspired classical algorithm for molecular vibronic spectra. We find an exact solution of the Fourier components of molecular vibronic spectra at zero temperature using the positive Prepresentation method. Thus, we can obtain the molecular vibronic spectra exactly by the inverse Fourier transformation for the solution for a given resolution. We also show that the same method applies for a finite temperature case by introducing auxiliary modes. Therefore, computing molecular vibronic spectra, which can be mapped to a Gaussian boson sampling, does not require a quantum device. We then generalize the method beyond the Gaussian framework and find that the Fourier components are written as a loop hafnian of a matrix, the exact computation of which is known to be #P-hard. It presents an interesting theoretical separation between non-Gaussian boson sampling and Gaussian boson sampling. Meanwhile, since running an actual boson sampling circuit would incur an 1/poly(N ) additive error for N samples, we also study the possibility of achieving the same accuracy using a classical algorithm. Interestingly, when there is no squeezing in the process, one may use Gurvits's algorithm to compute the Fourier components within an additive error. We finally provide other possible approaches to approximate the Fourier components and discuss their limitations.

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“…The GBS variant replaces the quantum resource of the BS, i.e the Fock state, with single-mode squeezed vacuum states (SMSV). This change to the original problem enhances the samples generation rate with respect to BS performed with probabilistic sources, and preserves the hardness of sampling from a quantum state [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Certification of Gaussian Boson Sampling via graph theory

Giordani¹,

Mannucci²,

Spagnolo³

et al. 2022

Preprint

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Gaussian Boson Sampling is a non-universal model for quantum computing inspired by the original formulation of the Boson Sampling problem. Nowadays, it represents a paradigmatic quantum platform to reach the quantum advantage regime in a specific computational model. Indeed, thanks to the implementation in photonics-based processors, the latest Gaussian Boson Sampling experiments have reached a level of complexity where the quantum apparatus has solved the task faster than currently up-to-date classical strategies. In addition, recent studies have identified possible applications beyond the inherent sampling task. In particular, a direct connection between photon counting of a genuine Gaussian Boson Sampling device and the number of perfect matchings in a graph has been established. In this work, we propose to exploit such a connection to benchmark Gaussian Boson Sampling experiments. We interpret the properties of the feature vectors of the graph encoded in the device as a signature of correct sampling from the true input state. Within this framework, two approaches that exploit the distributions of graph feature vectors and graph kernels are presented. Our results provide a novel approach to the actual need for tailored algorithms to benchmark large-scale Gaussian Boson Samplers.

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Quantum computational advantage via high-dimensional Gaussian boson sampling

Cited by 78 publications

References 63 publications

Quantum computational advantage with a programmable photonic processor

Quantum computational advantage with a programmable photonic processor

Quantum-inspired classical algorithm for molecular vibronic spectra

Certification of Gaussian Boson Sampling via graph theory

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