Experimental Demonstration of Gaussian Boson Sampling with Displacement

Thekkadath, G. S.; Sempere-Llagostera, S.; Bell, Bryn; Patel, Raj B.; Kim, M. S.

doi:10.1103/prxquantum.3.020336

Cited by 9 publications

(6 citation statements)

References 60 publications

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“…For this reason, stochastic algorithms (Lee et al, 2010) for the densest k-subgraph problem that make use of uniform randomness can be enhanced by having access to samples drawn from the output distribution of Gaussian boson sampling. In a proof-of-principle experiment using time-bin encoded GBS, this has been demonstrated recently (Sempere-Llagostera et al, 2022). follow a similar line of thought by introducing an NP-hard problem referred to as Max-Haf and showing that access to samples from the Gaussian boson sampling distribution defined by the probabilities P Gbs,U (S) of obtaining the output pattern S can enhance classical stochastic algorithms for this problem.…”

Section: Exploiting Structurementioning

confidence: 84%

“…A variant of the original scheme of Gaussian boson sampling that allows for shifts of the input squeezed states in phase space has been implemented by Thekkadath et al (2022), making use of an interferometer involving n = 15 modes. Such displacements are useful when anticipating applications of Gaussian boson sampling as sketched in Section VIII.…”

Section: Gaussian Boson Samplingmentioning

confidence: 99%

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Computational advantage of quantum random sampling

Hangleiter¹,

Eisert²

2022

Preprint

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Quantum random sampling is the leading proposal for demonstrating a computational advantage of quantum computers over classical computers. Recently, first large-scale implementations of quantum random sampling have arguably surpassed the boundary of what can be simulated on existing classical hardware. In this article, we comprehensively review the theoretical underpinning of quantum random sampling in terms of computational complexity and verifiability, as well as the practical aspects of its experimental implementation using superconducting and photonic devices and its classical simulation. We discuss in detail open questions in the field and provide perspectives for the road ahead, including potential applications of quantum random sampling.

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Section: Exploiting Structurementioning

confidence: 84%

Section: Gaussian Boson Samplingmentioning

confidence: 99%

Computational advantage of quantum random sampling

Hangleiter¹,

Eisert²

2022

Preprint

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“…Gaussian boson sampling (GBS) is a variant of boson sampling (BS) that was originally proposed to demonstrate the quantum advantage [1][2][3][4] . In recent years, great progress has been made in experiments on GBS [5][6][7][8][9][10][11] . Both the total number of optical modes and detected photons in GBS experiments have surpassed several hundred 7,8 .…”

Section: Gaussian Boson Sampling (Gbs) Plays a Crucially Important Ro...mentioning

confidence: 99%

“…This classical algorithm is helpful on clarifying how limited connectivity affects the computational complexity of GBS and tightening the boundary for achieving quantum advantage in the GBS problem.Gaussian boson sampling (GBS) is a variant of boson sampling (BS) that was originally proposed to demonstrate the quantum advantage [1][2][3][4] . In recent years, great progress has been made in experiments on GBS [5][6][7][8][9][10][11] . Both the total number of optical modes and detected photons in GBS experiments have surpassed several hundred 7,8 .…”

mentioning

confidence: 99%

Speeding up the classical simulation of Gaussian boson sampling with limited connectivity

Yang,

Wang

2024

Sci Rep

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Gaussian boson sampling (GBS) plays a crucially important role in demonstrating quantum advantage. As a major imperfection, the limited connectivity of the linear optical network weakens the quantum advantage result in recent experiments. In this work, we introduce an enhanced classical algorithm for simulating GBS processes with limited connectivity. It computes the loop Hafnian of an $$n \times n$$ n × n symmetric matrix with bandwidth w in $$O(nw2^w)$$ O ( n w 2 w ) time. It is better than the previous fastest algorithm which runs in $$O(nw^2 2^w)$$ O ( n w 2 2 w ) time. This classical algorithm is helpful on clarifying how limited connectivity affects the computational complexity of GBS and tightening the boundary for achieving quantum advantage in the GBS problem.

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“…In quantum information theory with continuous variables [9], these systems may be used for Gaussian boson sampling [10] or quantum error correction using squeezed Schrödinger cat states [11]. They may be used for the transport, separation, and merging of trapped ions in quantum computing architectures using sequences of time-varying potentials that produce squeezing and displacement of their center of mass motion state [12].…”

Section: Introductionmentioning

confidence: 99%

Continuous-time quantum harmonic oscillator state engineering

García Herrera,

Torres-Leal,

Rodríguez-Lara

2023

New J. Phys.

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The center of mass motion of trapped ions and neutral atoms is suitable for approximation by a time-dependent driven quantum harmonic oscillator whose frequency and driving strength may be controlled with high precision. We show the time evolution for these systems with continuous differentiable time-dependent parameters in terms of the three basic operations provided by its underlying symmetry, rotation, displacement, and squeezing, using a Lie algebraic approach. Our factorization of the dynamics allows for the intuitive construction of protocols for state engineering, for example, creating and removing displacement and squeezing, as well as their combinations, optimizing squeezing, or more complex protocols that work for slow and fast rates of change in the oscillator parameters.

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Experimental Demonstration of Gaussian Boson Sampling with Displacement

Cited by 9 publications

References 60 publications

Computational advantage of quantum random sampling

Computational advantage of quantum random sampling

Speeding up the classical simulation of Gaussian boson sampling with limited connectivity

Continuous-time quantum harmonic oscillator state engineering

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