• Corpus ID: 235436018

Boson Sampling in a reconfigurable continuously-coupled 3D photonic circuit

  title={Boson Sampling in a reconfigurable continuously-coupled 3D photonic circuit},
  author={Francesco Hoch and Simone Piacentini and Taira Giordani and Zhen-Nan Tian and Mariagrazia Iuliano and Chiara Esposito and Anita Camillini and Gonzalo Carvacho and Francesco Ceccarelli and Nicol{\'o} Spagnolo and Andrea Crespi and Fabio Sciarrino and Roberto Osellame},
Francesco Hoch,1, ∗ Simone Piacentini,2, 3, ∗ Taira Giordani,1 Zhen-Nan Tian,3 Mariagrazia Iuliano,1 Chiara Esposito,1 Anita Camillini,1 Gonzalo Carvacho,1 Francesco Ceccarelli,3 Nicolò Spagnolo,1 Andrea Crespi,2, 3 Fabio Sciarrino,1, † and Roberto Osellame3, 2, ‡ Dipartimento di Fisica, Sapienza Università di Roma, Piazzale Aldo Moro 5, I-00185 Roma, Italy Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci, 32, I-20133 Milano, Italy Istituto di Fotonica e Nanotecnologie… 

Dynamical learning of a photonics quantum-state engineering process

Abstract. Experimental engineering of high-dimensional quantum states is a crucial task for several quantum information protocols. However, a high degree of precision in the characterization of the

A learning theory for quantum photonic processors and beyond

The results establish that CV circuits can be trained efficiently using a number of training samples that, unlike theirite-dimensional counterpart, does not scale with the circuit depth.

Boson bunching is not maximized by indistinguishable particles

Boson bunching is amongst the most remarkable features of quantum physics. A celebrated example in optics is the Hong-Ou-Mandel effect, where the bunching of two photons arises from a destructive

Fock state-enhanced expressivity of quantum machine learning models

A photonic-based bosonic data-encoding scheme that embeds classical data points using fewer encoding layers and circumventing the need for nonlinear optical components by mapping the data points into the high-dimensional Fock space is proposed.

Variational quantum algorithm for Gaussian discrete solitons and their boson sampling

In the context of quantum information, highly nonlinear regimes, such as those supporting solitons, are marginally investigated. We miss general methods for quantum solitons, although they can act as

Boson sampling discrete solitons by quantum machine learning

A neural network variational ansatz is used to compute Gaussian quantum discrete solitons in an array of waveguides described by the quantum discrete nonlinear Schroedinger equation and it is unveiled that bound states of discrete soliton emit correlated pairs of photons.



Experimental Collision-Free Dominant Boson Sampling

This work experimentally demonstrate the largest scale boson sampling in the collision-free dominant regime using multi-port interferometer in a 3D photonic chip and represents a solid step toward large scale bosons sampling.

Super-stable tomography of any linear optical device

Linear optical circuits of growing complexity are playing an increasing role in emerging photonic quantum technologies. Individual photonic devices are typically described by a unitary matrix

Two-Dimensional Quantum Walk of Correlated Photons

This work presents a genuine 2D quantum walk with correlated photons on a triangular photonic lattice, which can be mapped to a state space up to 37X37 dimensions and breaks through the physically restriction of single-particle evolution.

The Classical Complexity of Boson Sampling

This work studies the classical complexity of the exact Boson Sampling problem where the objective is to produce provably correct random samples from a particular quantum mechanical distribution and gives an algorithm that is much faster, running in O(n 2^n + \operatorname{poly}(m,n)) time and additional space.


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Proceedings of the 43rd annual ACM symposium on Theory of computing

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