arXiv subjects

Jean Senellart

Publications and source records attributed to Jean Senellart.

At least 19 recordsLinked to original sources

arXiv2026

Industry-ready spin-photon interfaces for hybrid photonic quantum computing

Hybrid photonic quantum computers, combining stationary matter qubits and flying photonic qubits, offer an intrinsically networked and resource-efficient route to large-scale, error-corrected quantum computation. Their core components are cavity-coupled matter qubits that act as light--matter interfaces, enabling: high-efficiency on-demand single-photon generation, stable near-unity photon indistinguishability and spin--multi-photon entanglement. Semiconductor quantum dots in microcavities are a leading platform for realizing such devices. Yet reaching the performance, reproducibility and spin-coherence thresholds for large-scale error correction remains a major challenge requiring industrial fabrication and control. Here we report thousands of monolithic semiconductor quantum-dot devices fabricated using a III--V pilot production-line process compatible with large-scale deployment. Systematic control of source parameters yields state-of-the-art efficiency and supports a path to optical losses below fault-tolerance thresholds. Using field-quadrature state reconstruction as a stringent joint test of efficiency and indistinguishability, we observe near-unity photon quantum purity stable over tens of minutes and a record single-photon Wigner-function negativity. We further demonstrate seven-partite spin--multi-photon entanglement and spin coherence extendable to microsecond timescales in the low-magnetic-field regime. Finally, photons from distant sources are as indistinguishable as photons emitted successively by a single source. These results establish foundry-compatible III--V quantum dots as a scalable platform for hybrid photonic quantum computing.

quant-ph↗

arXiv2026

Pre-Asymptotic Trainability in Photonic Variational Circuits under Postselection

Barren plateaus (BP) in variational quantum circuits are commonly attributed to strong mixing dynamics that cause gradient variance to vanish exponentially with system size. Passive photonic circuits challenge this picture. Because observables and postselection maps generically extend beyond the algebra's reachable image, we show that trainability is governed by how postselection redistributes weight across the full irreducible decomposition of the operator space. Building on recent representation-theoretic second-moment techniques for passive linear optics, we express postselection through the pulled-back observable and obtain a closed-form Haar-averaged gradient variance, exact for fixed observables. The resulting scaling predictions are tested by statevector simulation of the nonlinear Bhattacharyya loss across allow-bunching, collision-free, and dual-rail postselection, using a four-test protocol (AICc, sliding-window, crossover, and truncation analyses) to separate polynomial from exponential scaling in finite data. Allow-bunching and collision-free filtering remain polynomial-consistent across all tested sizes and initializations, while dual-rail postselection induces a genuine BP at a rate roughly an order of magnitude below the 2-design value.

quant-ph↗

arXiv2026

MerLin: A Discovery Engine for Photonic and Hybrid Quantum Machine Learning

Identifying where quantum models may offer practical benefits in near term quantum machine learning (QML) requires moving beyond isolated algorithmic proposals toward systematic and empirical exploration across models, datasets, and hardware constraints. We introduce MerLin, an open-source framework designed as a discovery engine for photonic and hybrid quantum machine learning. MerLin integrates optimized strong simulation of linear optical circuits into standard PyTorch and scikit learn workflows, enabling end-to-end differentiable training of quantum layers. MerLin is designed around systematic benchmarking and reproducibility. As an initial contribution, we reproduce eighteen state-of-the-art photonic and hybrid QML works spanning kernel methods, reservoir computing, convolutional and recurrent architectures, generative models, and modern training paradigms. These reproductions are released as reusable, modular experiments that can be directly extended and adapted, establishing a shared experimental baseline consistent with empirical benchmarking methodologies widely adopted in modern artificial intelligence. By embedding photonic quantum models within established machine learning ecosystems, MerLin allows practitioners to leverage existing tooling for ablation studies, cross-modality comparisons, and hybrid classical-quantum workflows. The framework already implements hardware-aware features, allowing tests on available quantum hardware while enabling exploration beyond its current capabilities, positioning MerLin as a forward-looking co-design tool linking algorithms, benchmarks, and hardware.

cs.LG↗

arXiv2025

Establishing Baselines for Photonic Quantum Machine Learning: Insights from an Open, Collaborative Initiative

The Perceval Challenge is an open, reproducible benchmark designed to assess the potential of photonic quantum computing for machine learning. Focusing on a reduced and hardware-feasible version of the MNIST digit classification task or near-term photonic processors, it offers a concrete framework to evaluate how photonic quantum circuits learn and generalize from limited data. Conducted over more than three months, the challenge attracted 64 teams worldwide in its first phase. After an initial selection, 11 finalist teams were granted access to GPU resources for large-scale simulation and photonic hardware execution through cloud service. The results establish the first unified baseline of photonic machine-learning performance, revealing complementary strengths between variational, hardware-native, and hybrid approaches. This challenge also underscores the importance of open, reproducible experimentation and interdisciplinary collaboration, highlighting how shared benchmarks can accelerate progress in quantum-enhanced learning. All implementations are publicly available in a single shared repository (https://github.com/Quandela/HybridAIQuantum-Challenge), supporting transparent benchmarking and cumulative research. Beyond this specific task, the Perceval Challenge illustrates how systematic, collaborative experimentation can map the current landscape of photonic quantum machine learning and pave the way toward hybrid, quantum-augmented AI workflows.

quant-ph↗

arXiv2025

Resource-efficient crosstalk mitigation for the high-fidelity operation of photonic integrated circuits with induced phase shifters

Photonic integrated circuits (PICs) are key platforms for the compact and stable manipulation of classical and quantum light. Imperfections arising from fabrication constraints, tolerances, and operation wavelength limit the accuracy of intended operations on light and impede the practical utility of current PICs. In particular, crosstalk between reconfigurable phase shifters is challenging to characterize due to the large number of parameters to estimate and the difficulty in isolating individual parameters. Previous studies have attempted to model crosstalk solely as an interaction between controlled phase shifters, overlooking the broader scope of this issue. We introduce the concept of induced phase shifter, arising from crosstalk on bare waveguide sections as predicted by simulations, resulting in an exhaustive description and systematic analysis of crosstalk. We characterize induced phase shifters in physical devices using a machine learning-based method and propose a mitigation framework. This framework further allows to establish a criterion certifying that a given interferometer has a sufficient number of degrees of freedom adequately laid out to fully mitigate crosstalk. Our approach is experimentally validated on a 12-mode Clements interferometer. We demonstrate the efficacy of our extended crosstalk model to accurately recover physical crosstalk properties of the PIC and cancel induced phase shifters following our mitigation framework.

physics.optics↗

arXiv2025

Systematic benchmarking of quantum computers: status and recommendations

Architectures for quantum computing can only be scaled up when they are accompanied by suitable benchmarking techniques. The document provides a comprehensive overview of the state and recommendations for systematic benchmarking of quantum computers. Benchmarking is crucial for assessing the performance of quantum computers, including the hardware, software, as well as algorithms and applications. The document highlights key aspects such as component-level, system-level, software-level, HPC-level, and application-level benchmarks. Component-level benchmarks focus on the performance of individual qubits and gates, while system-level benchmarks evaluate the entire quantum processor. Software-level benchmarks consider the compiler's efficiency and error mitigation techniques. HPC-level and cloud benchmarks address integration with classical systems and cloud platforms, respectively. Application-level benchmarks measure performance in real-world use cases. The document also discusses the importance of standardization to ensure reproducibility and comparability of benchmarks, and highlights ongoing efforts in the quantum computing community towards establishing these benchmarks. Recommendations for future steps emphasize the need for developing standardized evaluation routines and integrating benchmarks with broader quantum technology activities.

quant-ph↗

arXiv2024

Scalable machine learning-assisted clear-box characterization for optimally controlled photonic circuits

Photonic integrated circuits offer a compact and stable platform for generating, manipulating, and detecting light. They are instrumental for classical and quantum applications. Imperfections stemming from fabrication constraints, tolerances and operation wavelength impose limitations on the accuracy and thus utility of current photonic integrated devices. Mitigating these imperfections typically necessitates a model of the underlying physical structure and the estimation of parameters that are challenging to access. Direct solutions are currently lacking for mesh configurations extending beyond trivial cases. We introduce a scalable and innovative method to characterize photonic chips through an iterative machine learning-assisted procedure. Our method is based on a clear-box approach that harnesses a fully modeled virtual replica of the photonic chip to characterize. The process is sample-efficient and can be carried out with a continuous-wave laser and powermeters. The model estimates individual passive phases, crosstalk, beamsplitter reflectivity values and relative input/output losses. Building upon the accurate characterization results, we mitigate imperfections to enable enhanced control over the device. We validate our characterization and imperfection mitigation methods on a 12-mode Clements-interferometer equipped with 126 phase shifters, achieving beyond state-of-the-art chip control with an average 99.77 % amplitude fidelity on 100 implemented Haar-random unitary matrices.

physics.optics↗

arXiv2023

One nine availability of a Photonic Quantum Computer on the Cloud toward HPC integration

The integration of Quantum Computers (QC) within High-Performance Computing (HPC) environments holds significant promise for solving real-world problems by leveraging the strengths of both computational paradigms. However, the integration of a complex QC platform in an HPC infrastructure poses several challenges, such as operation stability in non-laboratory like environments, and scarce access for maintenance. Currently, only a few fully-assembled QCs currently exist worldwide, employing highly heterogeneous and cutting-edge technologies. These platforms are mostly used for research purposes, and often bear closer resemblance to laboratory assemblies rather than production-ready, stable, and consistently-performing turnkey machines. Moreover, public cloud services with access to such quantum computers are scarce and their availability is generally limited to few days per week. In November 2022, we introduced the first cloud-accessible general-purpose quantum computer based on single photons. One of the key objectives was to maintain the platform's availability as high as possible while anticipating seamless compatibility with HPC hosting environment. In this article, we describe the design and implementation of our cloud-accessible quantum computing platform, and demonstrate one nine availability (92 %) for external users during a six-month period, higher than most online services. This work lay the foundation for advancing quantum computing accessibility and usability in hybrid HPC-QC infrastructures.

quant-ph↗

arXiv2023

Strong Simulation of Linear Optical Processes

In this paper, we provide an algorithm and general framework for the simulation of photons passing through linear optical interferometers. Given $n$ photons at the input of an $m$-mode interferometer, our algorithm computes the probabilities of all possible output states with time complexity $O\left({n\binom{n+m-1}{m-1}}\right)$, linear in the number of output states $\binom{n+m-1}{m-1}$. It outperforms the permanent-based method by an exponential factor, and for the restricted problem of computing the probability for one given output it improves the time complexity over the state-of-the-art for the permanent of matrices with multiple rows or columns, with a tradeoff in the memory usage. Our algorithm also has additional versatility by virtue of its use of memorisation -- the storing of intermediate results -- which is advantageous in situations where several input states may be of interest. Additionally it allows for hybrid simulations, in which outputs are sampled from output states whose probability exceeds a given threshold, or from a restricted set of states. We consider a concrete, optimised implementation, and we benchmark the efficiency of our approach compared to existing tools.

quant-ph↗

arXiv2023

A general-purpose single-photon-based quantum computing platform

Quantum computing aims at exploiting quantum phenomena to efficiently perform computations that are unfeasible even for the most powerful classical supercomputers. Among the promising technological approaches, photonic quantum computing offers the advantages of low decoherence, information processing with modest cryogenic requirements, and native integration with classical and quantum networks. To date, quantum computing demonstrations with light have implemented specific tasks with specialized hardware, notably Gaussian Boson Sampling which permitted quantum computational advantage to be reached. Here we report a first user-ready general-purpose quantum computing prototype based on single photons. The device comprises a high-efficiency quantum-dot single-photon source feeding a universal linear optical network on a reconfigurable chip for which hardware errors are compensated by a machine-learned transpilation process. Our full software stack allows remote control of the device to perform computations via logic gates or direct photonic operations. For gate-based computation we benchmark one-, two- and three-qubit gates with state-of-the art fidelities of $99.6\pm0.1 \%$, $93.8\pm0.6 \%$ and $86\pm1.2 \%$ respectively. We also implement a variational quantum eigensolver, which we use to calculate the energy levels of the hydrogen molecule with high accuracy. For photon native computation, we implement a classifier algorithm using a $3$-photon-based quantum neural network and report a first $6$-photon Boson Sampling demonstration on a universal reconfigurable integrated circuit. Finally, we report on a first heralded 3-photon entanglement generation, a key milestone toward measurement-based quantum computing.

quant-ph↗

arXiv2023

Perceval: A Software Platform for Discrete Variable Photonic Quantum Computing

We introduce Perceval, an open-source software platform for simulating and interfacing with discrete-variable photonic quantum computers, and describe its main features and components. Its Python front-end allows photonic circuits to be composed from basic photonic building blocks like photon sources, beam splitters, phase-shifters and detectors. A variety of computational back-ends are available and optimised for different use-cases. These use state-of-the-art simulation techniques covering both weak simulation, or sampling, and strong simulation. We give examples of Perceval in action by reproducing a variety of photonic experiments and simulating photonic implementations of a range of quantum algorithms, from Grover's and Shor's to examples of quantum machine learning. Perceval is intended to be a useful toolkit for experimentalists wishing to easily model, design, simulate, or optimise a discrete-variable photonic experiment, for theoreticians wishing to design algorithms and applications for discrete-variable photonic quantum computing platforms, and for application designers wishing to evaluate algorithms on available state-of-the-art photonic quantum computers.

quant-ph↗

arXiv2022

High-fidelity generation of four-photon GHZ states on-chip

Mutually entangled multi-photon states are at the heart of all-optical quantum technologies. While impressive progresses have been reported in the generation of such quantum light states using free space apparatus, high-fidelity high-rate on-chip entanglement generation is crucial for future scalability. In this work, we use a bright quantum-dot based single-photon source to demonstrate the high fidelity generation of 4-photon Greenberg-Horne-Zeilinger (GHZ) states with a low-loss reconfigurable glass photonic circuit. We reconstruct the density matrix of the generated states using full quantum-state tomography reaching an experimental fidelity to the target $|{\text{GHZ}_4}\rangle$ of $\mathcal{F}_{\text{GHZ}_4} (86.0\pm0.4)\,\%$, and a purity of $\mathcal{P}_{\text{GHZ}_4}=(76.3\pm0.6)\,\%$. The entanglement of the generated states is certified with a semi device-independent approach through the violation of a Bell-like inequality by more than 39 standard deviations. Finally, we carry out a four-partite quantum secret sharing protocol on-chip where a regulator shares with three interlocutors a sifted key with up to 1978 bits, achieving a qubit-error rate of $10.87\,\%$. These results establish that the quantum-dot technology combined with glass photonic circuitry for entanglement generation on chip offers a viable path for intermediate scale quantum computation and communication.

quant-ph↗

arXiv2018

OpenNMT: Neural Machine Translation Toolkit

OpenNMT is an open-source toolkit for neural machine translation (NMT). The system prioritizes efficiency, modularity, and extensibility with the goal of supporting NMT research into model architectures, feature representations, and source modalities, while maintaining competitive performance and reasonable training requirements. The toolkit consists of modeling and translation support, as well as detailed pedagogical documentation about the underlying techniques. OpenNMT has been used in several production MT systems, modified for numerous research papers, and is implemented across several deep learning frameworks.

cs.CL↗

arXiv2017

Boosting Neural Machine Translation

Training efficiency is one of the main problems for Neural Machine Translation (NMT). Deep networks need for very large data as well as many training iterations to achieve state-of-the-art performance. This results in very high computation cost, slowing down research and industrialisation. In this paper, we propose to alleviate this problem with several training methods based on data boosting and bootstrap with no modifications to the neural network. It imitates the learning process of humans, which typically spend more time when learning "difficult" concepts than easier ones. We experiment on an English-French translation task showing accuracy improvements of up to 1.63 BLEU while saving 20% of training time.

cs.CL↗

arXiv2017

Domain Control for Neural Machine Translation

Machine translation systems are very sensitive to the domains they were trained on. Several domain adaptation techniques have been deeply studied. We propose a new technique for neural machine translation (NMT) that we call domain control which is performed at runtime using a unique neural network covering multiple domains. The presented approach shows quality improvements when compared to dedicated domains translating on any of the covered domains and even on out-of-domain data. In addition, model parameters do not need to be re-estimated for each domain, making this effective to real use cases. Evaluation is carried out on English-to-French translation for two different testing scenarios. We first consider the case where an end-user performs translations on a known domain. Secondly, we consider the scenario where the domain is not known and predicted at the sentence level before translating. Results show consistent accuracy improvements for both conditions.

cs.CL↗

arXiv2017

SYSTRAN Purely Neural MT Engines for WMT2017

This paper describes SYSTRAN's systems submitted to the WMT 2017 shared news translation task for English-German, in both translation directions. Our systems are built using OpenNMT, an open-source neural machine translation system, implementing sequence-to-sequence models with LSTM encoder/decoders and attention. We experimented using monolingual data automatically back-translated. Our resulting models are further hyper-specialised with an adaptation technique that finely tunes models according to the evaluation test sentences.

cs.CL↗

arXiv2017

OpenNMT: Open-source Toolkit for Neural Machine Translation

We introduce an open-source toolkit for neural machine translation (NMT) to support research into model architectures, feature representations, and source modalities, while maintaining competitive performance, modularity and reasonable training requirements.

cs.CL↗

arXiv2017

OpenNMT: Open-Source Toolkit for Neural Machine Translation

We describe an open-source toolkit for neural machine translation (NMT). The toolkit prioritizes efficiency, modularity, and extensibility with the goal of supporting NMT research into model architectures, feature representations, and source modalities, while maintaining competitive performance and reasonable training requirements. The toolkit consists of modeling and translation support, as well as detailed pedagogical documentation about the underlying techniques.

cs.CL↗