Driven Boson Sampling

Abstract

Sampling the distribution of bosons that have undergone a random unitary evolution is strongly believed to be a computationally hard problem. Key to outperforming classical simulations of this task is to increase both the number of input photons and the size of the network. We propose driven boson sampling, in which photons are input within the network itself, as a means to approach this goal. When using heralded single-photon sources based on parametric down-conversion, this approach offers an $\sim e$-fold enhancement in the input state generation rate over scattershot boson sampling, reaching the scaling limit for such sources. More significantly, this approach offers a dramatic increase in the signal-to-noise ratio with respect to higher-order photon generation from such probabilistic sources, which removes the need for photon number resolution during the heralding process as the size of the system increases.

References in corpus (5)

• Universal Linear Optics
• Photonic Boson Sampling in a Tunable Circuit
• Experimental Scattershot Boson Sampling
• Scalable boson-sampling with time-bin encoding using a loop-based architecture
• The complexity of simulating constant-depth BosonSampling

Cited by in corpus (18)

• Photonic quantum information processing: a review
• Gaussian Boson Sampling
• Quantum Sampling Problems, BosonSampling and Quantum Supremacy
• A detailed study of Gaussian Boson Sampling
• 8x8 Reconfigurable quantum photonic processor based on silicon nitride waveguides
• Experimental Gaussian Boson Sampling
• Exact simulation of Gaussian Boson Sampling in polynomial space and exponential time
• Simulating lossy Gaussian boson sampling with matrix product operators
• Quantum computational supremacy in the sampling of bosonic random walkers on a one-dimensional lattice
• Quantum photonics with active feedback loops
• Simulating arbitrary Gaussian circuits with linear optics
• Driven Gaussian quantum walks
• Gaussian boson sampling with partial distinguishability
• Boson sampling with random numbers of photons
• High performance Boson Sampling simulation via data-flow engines
• Unified boson sampling
• Boosting Gaussian Boson Sampling using Optical Parametric Amplification Networks
• Polynomial speedup in Torontonian calculation by a scalable recursive algorithm