Validation of a noisy Gaussian boson sampler via graph theory
arXiv:2306.12120 · doi:10.1117/1.APN.4.1.016011
Abstract
Quantum photonic processors are emerging as promising platforms to prove preliminary evidence of quantum computational advantage towards the realization of universal quantum computers. In the context of non-universal noisy intermediate quantum devices, photonic-based sampling machines solving the Gaussian Boson Sampling problem currently play a central role in the experimental demonstration of a quantum computational advantage. In particular, the recently developed photonic machine Borealis, a large-scale instance of a programmable Gaussian Boson Sampling device encoded in the temporal modes of single photons, is available online for external users. In this work, we test the performances of Borealis as a sampling machine and its possible use cases in graph theory. We focused on the validation problem of the sampling process in the presence of experimental noise, such as photon losses, that could undermine the hardness of simulating the experiment. To this end, we used a recent protocol that exploits the connection between Guassian Boson Sampling and graphs perfect match counting. Such an approach to validation also provides connections with the open question on the effective advantage in using noisy Gaussian Boson Sampling devices for graphs similarity and isomorphism problems.
12+3 pages, 7+1 figures
References in corpus (46)
- Quantum Computing in the NISQ era and beyond
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Quantum computational advantage using photons
- Strong quantum computational advantage using a superconducting quantum processor
- Quantum circuits with many photons on a programmable nanophotonic chip
- Gaussian Boson Sampling
- Phase-Programmable Gaussian Boson Sampling Using Stimulated Squeezed Light
- Strawberry Fields: A Software Platform for Photonic Quantum Computing
- Efficient experimental validation of photonic boson sampling against the uniform distribution
- Boson Sampling from Gaussian States
- Perspective: Toward large-scale fault-tolerant universal photonic quantum computing
- No imminent quantum supremacy by boson sampling
- Scalable boson sampling with a single-photon device
- Gaussian Boson Sampling with Pseudo-Photon-Number Resolving Detectors and Quantum Computational Advantage
- Sufficient Conditions for Efficient Classical Simulation of Quantum Optics
- Using Gaussian Boson Sampling to Find Dense Subgraphs
- Scalable boson-sampling with time-bin encoding using a loop-based architecture
- Applications of Near-Term Photonic Quantum Computers: Software and Algorithms
- Quantum Computational Advantage via High-Dimensional Gaussian Boson Sampling
- Efficient algorithm for boson sampling with partially distinguishable photons
- Simulating boson sampling in lossy architectures
- A quantum hardware-induced graph kernel based on Gaussian Boson Sampling
- What can quantum optics say about computational complexity theory?
- The Boundary for Quantum Advantage in Gaussian Boson Sampling
- Regimes of classical simulability for noisy Gaussian boson sampling
- A Statistical Benchmark for BosonSampling
- Experimental statistical signature of many-body quantum interference
- Classical simulation of photonic linear optics with lost particles
- Solving Graph Problems Using Gaussian Boson Sampling
- Exact simulation of Gaussian Boson Sampling in polynomial space and exponential time
- Classical algorithm for simulating experimental Gaussian boson sampling
- Graph isomorphism and Gaussian boson sampling
- A universal programmable Gaussian Boson Sampler for drug discovery
- Classical simulation of lossy boson sampling using matrix product operators
- Point Processes with Gaussian Boson Sampling
- Classical simulation of boson sampling based on graph structure
- Classical simulation of linear optics subject to nonuniform losses
- Benchmarking of Gaussian boson sampling using two-point correlators
- Simulating complex networks in phase space: Gaussian boson sampling
- Simulability of Imperfect Gaussian and Superposition Boson Sampling
- Classically simulating near-term partially-distinguishable and lossy boson sampling
- Simulating lossy Gaussian boson sampling with matrix product operators
- Classical models may be a better explanation of the Jiuzhang 1.0 Gaussian Boson Sampler than its targeted squeezed light model
- Measuring the quadrature coherence scale on a cloud quantum computer
- Quantum-inspired classical algorithm for graph problems by Gaussian boson sampling
- Certification of Gaussian Boson Sampling via graph theory