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Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC)

Probing Graph Similarity Through Multiple Photons in Frequency-Time Bin Modes Generated by a Silicon Nitride Microresonator

Massimo Borghi, Emanuele Brusaschi, Marco Liscidini, Mattéo Galli, Daniele Bajoni

Published Jun 23, 2025• 0 citations
Publisher

Abstract

Boson-sampling problems are specific cases of multi-photon quantum interference in which the complexity of sampling from the output distribution of multiple photons on multiple modes arises from its relation to certain matrix functions that cannot be efficiently computed in polynomial time using the best-known classical algorithms. Specifically, gaussian boson sampling (GBS) is a protocol that employs Gaussian states as inputs to a linear interferometer, and whose output samples can be used to accelerate graph search and optimization problems [1]. To date, GBS experiments have been reported by using path or time-bin encoding of the optical modes.

DOI
10.1109/cleo/europe-eqec65582.2025.11110431

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Boson-sampling problems are specific cases of multi-photon quantum interference in which the complexity of sampling from the output distribution of multiple photons on multiple modes arises from its relation to certain matrix functions that cannot be efficiently computed in polynomial time using the best-known classical algorithms. Specifically, gaussian boson sampling (GBS) is a protocol that employs Gaussian states as inputs to a linear interferometer, and whose output samples can be used to accelerate graph search and optimization problems [1]. To date, GBS experiments have been reported by using path or time-bin encoding of the optical modes.
Gaussian boson sampling (GBS) is a tool for demonstrating quantum computational utilities and also exhibits mathematical connections to specific graph-related and quantum chemistry problems. It has been suggested that the samples generated from GBS could be utilized to improve stochastic algorithms in searching for certain graph features. In this study, we employed X8, a noisy intermediate-scale quantum computer, to address graph problems. The samples were produced from a 4-mode quantum photonic processor. This research represents a step toward analyzing Gaussian boson sampling as a tool for solving real-world problems using existing noisy intermediate-scale quantum computers and aims to encourage the development of quantum algorithms.
Gaussian boson sampling (GBS) is not only a feasible protocol for demonstrating quantum computational advantage, but also mathematically associated with certain graph-related and quantum chemistry problems. In particular, it is proposed that the generated samples from the GBS could be harnessed to enhance the classical stochastic algorithms in searching some graph features. Here, we use Jiǔzhāng, a noisy intermediate-scale quantum computer, to solve graph problems. The samples are generated from a 144-mode fully connected photonic processor, with photon click up to 80 in the quantum computational advantage regime. We investigate the open question of whether the GBS enhancement over the classical stochastic algorithms persists-and how it scales-with an increasing system size on noisy quantum devices in the computationally interesting regime. We experimentally observe the presence of GBS enhancement with a large photon-click number and a robustness of the enhancement under certain noise. Our work is a step toward testing real-world problems using the existing noisy intermediate-scale quantum computers and hopes to stimulate the development of more efficient classical and quantum-inspired algorithms.
Gaussian boson sampling is a quantum computing concept based on drawing samples from a multimode nonclassical Gaussian state using photon-number resolving detectors.It was initially posed as a near-term approach to achieve quantum advantage, and several applications have been proposed since, including the calculation of graph features.For the first time, we use a time-bin encoded interferometer to implement Gaussian boson sampling experimentally and extract samples to enhance the search for dense 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.In addition, we numerically explore the role of imperfections in the optical circuit and on the performance of the algorithm.
We demonstrate bipartite gaussian boson sampling with squeezed light in 6 mixed time-frequency modes. Non-degenerate two-mode squeezing is generated in two time-bins from a silicon nitride microresonator with simultaneous high spectral purity (>0.86(3)) and indistinguishability (0.985(2)). An unbalanced interferometer embedding electro-optic modulators, which is stabilized by exploiting the continuous energy-time entanglement of the generated photon pairs, controls time and frequency-bin modes. We measure 144 collision-free events with 4 photons at the output, achieving a fidelity >0.98 with the theoretical probability distribution. We use this result to identify the similarity between families of isomorphic graphs with 6 vertices, and present an approach for the realization of universal operations on time-frequency modes.
We demonstrate high-dimensional bipartite Gaussian boson sampling with squeezed light across 6 mixed time-frequency modes. Non-degenerate two-mode squeezing is generated in two time-bins from a silicon nitride microresonator. An unbalanced interferometer embedding electro-optic modulators and stabilized by exploiting the continuous energy-time entanglement of the generated photon pairs, couples time and frequency-bin modes arranged in a two-dimensional 3 by 2 rectangular lattice, thus enabling both local and non-local interactions. We measure 144 collision-free events with 4 photons at the output, achieving a fidelity greater than 0.98 with the theoretical probability distribution. We use this result to identify the similarity between families of isomorphic graphs with 6 vertices.