Category: Data-driven Physics

Inverse design of high-dimensional quantum optical circuits in Nature Physics !

Programmable optical circuits are an important tool in developing quantum technologies such as transceivers for quantum communication and integrated photonic chips for quantum information processing. Maintaining precise control over every individual component becomes challenging at large scales, leading to a reduction in the quality of operations performed. In parallel, minor imperfections in circuit fabrication are amplified in this regime, dramatically inhibiting their performance. Here we use inverse design techniques to embed optical circuits in the higher-dimensional space of a large, ambient mode mixer such as a commercial multimode fibre. This approach allows us to forgo control over each individual circuit element, and retain a high degree of programmability. We use our circuits as quantum gates to manipulate high-dimensional spatial-mode entanglement in up to seven dimensions. Their programmability allows us to turn a multimode fibre into a generalized multioutcome measurement device, allowing us to both transport and certify entanglement within the transmission channel. With the support of numerical simulations, we show that our method is a scalable approach to obtaining high circuit fidelity with a low circuit depth by harnessing the resource of a high-dimensional mode mixer.

https://www.nature.com/articles/s41567-023-02319-6

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See also Quantum Gates by TensorFlow and Reservoir Computing

Quantum Gates by TensorFlow and Reservoir Computing

Dawn and fall of non-Gaussianity in the quantum parametric oscillator

Systems of coupled optical parametric oscillators (OPOs) forming an Ising machine are emerging as large-scale simulators of the Ising model. The advances in computer science and nonlinear optics have triggered not only the physical realization of hybrid (electro-optical) or all-optical Ising machines, but also the demonstration of quantum-inspired algorithms boosting their performances. To date, the use of the quantum nature of parametrically generated light as a further resource for computation represents a major open issue. A key quantum feature is the non-Gaussian character of the system state across the oscillation threshold. In this paper, we perform an extensive analysis of the emergence of non-Gaussianity in the single quantum OPO with an applied external field. We model the OPO by a Lindblad master equation, which is numerically solved by an ab initio method based on exact diagonalization. Non-Gaussianity is quantified by means of three different metrics: Hilbert-Schmidt distance, quantum relative entropy, and photon distribution. Our findings reveal a nontrivial interplay between parametric drive and applied field: (i) Increasing pump monotonously enhances non-Gaussianity, and (ii) Increasing field first sharpens non-Gaussianity, and then restores the Gaussian character of the state when above a threshold value.

https://arxiv.org/abs/2312.16530

Quantum Machine Learning Book Published !

https://link.springer.com/book/10.1007/978-3-031-44226-1

Related activities

Inverse design of high-dimensional quantum optical circuits in Nature Physics !
Quantum Gates by TensorFlow and Reservoir Computing
Quantum Machine Learning course on Github

Optimal quantum communication networks: capacitance versus security

https://arxiv.org/abs/2312.04221

The rate and security of quantum communications between users placed at arbitrary points of a quantum communication network depend on the structure of the network, on its extension and on the nature of the communication channels. In this work we propose a strategy of network optimization that intertwines classical network approaches and quantum information theory. Specifically, by suitably defining a quantum efficiency functional, we identify the optimal quantum communication connections through the network by balancing security and the quantum communication rate. The optimized network is then constructed as the network of the maximal quantum efficiency connections and its performance is evaluated by studying the scaling of average properties as functions of the number of nodes and of the network spatial extension.