Patel, Raj

Date:    Thursday, November 9, 2023 
Time:    14:00
Place:    ETH Campus Hönggerberg, HPF G 6
Hosts:    Robert Chapman and Rachel Grange

Gaussian Boson Sampling experiments with displacements and time-bin encoding

Raj Patel –  Imperial College London, United Kingdom

Abstract: Gaussian boson sampling (GBS) is a quantum sampling task in which samples are drawn from the photon-number distribution of a high-dimensional nonclassical squeezed state of light. GBS has garnered much attention, since its proposal, for its ability to perform a task which is intractable with a classical machine, whilst alleviating some of the experimental limitations associated with standard Boson Sampling with single photons. Here I will present GBS experiments each realised with either path or time-bin encoding.
Experiments building GBS machines have mainly focused on increasing the circuit scale and squeezing strength of the nonclassical light to make the task intractable on even the fastest supercomputers. However, no experiment has yet demonstrated the ability to displace the squeezed state in phase space, which is a key operation that opens new avenues for the utility of GBS. Here, we used a GBS device based on silicon photonics that achieves the displacement by injecting coherent laser light alongside a two-mode squeezed vacuum state into a 15-mode interferometer. We also use displacements to reconstruct the multimode Gaussian state at the output of the interferometer. Our reconstruction technique is in-situ and requires only three measurement settings regardless of the state dimension. I will briefly discuss how the addition of classical laser light in a GBS machine affects the complexity of sampling its output photon statistics.
Additionally, we use a time-bin encoded interferometer to implement GBS experimentally. Such an apparatus is highly scalable using a fixed number of physical resources. To demonstrate the utility of this architecture, we perform GBS experiments to produce samples to enhance the search for subgraphs in a graph. Our results indicate an improvement over classical methods for subgraphs of sizes three and four in a graph containing ten nodes.