Hugo Thomas

Quantum Machine Learning (QML) has become a promising area for real world applications of quantum computers, and near-term methods and their scalability are still important research topics. A consequent amount of efforts has been put into understanding how to avoid Barren Plateaus (BPs), a vanishing gradient phenomenon that prevents the variational algorithms from being trained efficiently. In particular, evidence has recently been shown that the structures that allow us to avoid BP seem to allow classical simulation techniques. In addition, other important questions must be tackled to design near-term quantum algorithms that may offer an advantage. How to ensure that the performance of the algorithms will scale with input size, and how to compare classical and quantum algorithms on different figures of merit for a same use case? Recent works have proposed to use subspace preserving quantum circuits to mimic classical neural network architectures. By restricting the Hilbert space to a subspace of polynomial size with respect to the number of qubits, such architectures are likely to avoid BPs. This comes at the cost of that is, a classical method can perform the same computation in polynomial time. In this work, we propose a paradigm shift: focusing on subspace-preserving methods that aim for a practical polynomial advantage. In particular, we propose to use linear optical circuits that are intrinsically subspace preserving as they conserve the number of particles during the computation. We believe that this approach could be sufficient to create useful QML applications as the generation of Fock states with few particles can be extremely high. In this talk, we will present two recent contributions from our team published in Physical Review Research. [1] and Advanced Photonics [2]. First, we will recall how linear optical circuit are limited in their expressivity due to the photonic homomorphism described by Aaronson and Arkhipov. We propose in [1] a new scheme for near-term photonic quantum devices that allows to increase the expressive power of the quantum models beyond what linear optics can do. This scheme relies upon State Injection (SI), a measurement-based technique that can produce states that are more controllable, and solve learning tasks that are believed to be intractable classically. Then we will show how using [2] how we propose to adapt a subspace preserving Quantum Convolutional Neural Network (QCNN) architecture for linear optic setting with SI adaptivity. We realize a proof-of-concept experiment by employing a cutting-edge single-photon source based on a semiconductor Quantum Dot (QD) , a time-to-spatial demultiplexer, and universal programmable 12-mode and 8-mode interferometers realized with the femtosecond laser-writing technique. The designed PQCNN scheme is tailored to the experimental platform at hand, with the goal of carrying out a binary image classification. As a complement to the experimental investigation, we provide a systematic study on the scaling and complexity of the protocol, by leveraging numerical simulations on larger quantum systems, demonstrating the potential behind the proposed scheme for PQCNNs.