Quantum Optical Networks based on Exciton-polaritons

Informazioni relative al progetto

Q-ONE

ID dell’accordo di sovvenzione: 101115575

DOI

10.3030/101115575

Data della firma CE 31 Luglio 2023

Data di avvio 1 Settembre 2023

Data di completamento 31 Agosto 2027

Finanziato da

• The European Innovation Council (EIC)

Costo totale

€ 3 980 960,00

Contributo UE

€ 3 980 960,00

Coordinato da

CONSIGLIO NAZIONALE DELLE RICERCHE
Italy

Periodic Reporting for period 1 - Q-ONE (Quantum Optical Networks based on Exciton-polaritons)

Periodo di rendicontazione: 2023-09-01 al 2024-08-31

Sintesi del contesto e degli obiettivi generali del progetto

The Q-ONE project addresses key challenges in quantum information and quantum optics by developing a novel platform based on Quantum Neural Networks (QNNs). These QNNs are designed to perform tasks such as recognizing and generating quantum states of light, an essential step toward advancing technologies like quantum computing, sensing, and communications.

Quantum states of light, including entangled pairs and squeezed states, are central to a range of quantum applications. However, their generation and characterization typically involve complex, resource-intensive setups. Current methods, like quantum tomography, rely on interferometric systems and specific detectors, which are challenging to maintain and scale. Additionally, different quantum states require distinct experimental setups, leading to inefficiencies and limitations in scalability.

Q-ONE proposes a novel solution: a quantum platform based on exciton-polaritons, quasiparticles that combine light and matter. These polaritons provide strong nonlinear interactions, crucial for quantum neural networks, while maintaining coherence over long distances. The project aims to use a QNN composed of nonlinear polariton nodes to achieve two primary goals:

Recognition of quantum states such as Fock states, squeezed states and entangled pairs without using traditional correlation measurements.
Generation of quantum states from classical light, a task that would otherwise require separate systems for discrete and continuous variable domains.

This project is situated at the frontier of quantum physics and artificial intelligence. By implementing neuromorphic computing principles, Q-ONE aims to develop a single, reconfigurable, platform that can both generate and detect quantum states. If successful, this device would simplify the current methodologies, providing a faster, more accessible means of advancing quantum technology.

The expected impact of Q-ONE is profound. It will provide a universal, scalable quantum platform that can be applied across various fields, from quantum computing to secure communications. Furthermore, the project aligns with EU objectives of driving innovation in quantum technologies, positioning Europe as a leader in the emerging quantum information landscape.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

During the first year of Q-ONE, the consortium made significant progress toward the project's overall goals. The work focused on establishing the foundation for the Quantum Neural Network (QNN) platform, particularly in the design and development of polaritonic systems and quantum state preparation.

Key achievements include:

Quantum State Identification and Generation:
Theoretical and experimental work led to the identification of squeezed thermal states as the primary quantum states for training and testing the QNN. These states were chosen due to their experimental feasibility and robustness. The first experimental setup for generating squeezed thermal states was successfully realied, and optical characterization was completed, ensuring the states could be effectively injected into the QNN. This work is documented in Deliverable D1.1 ("Identification of the best set of quantum states to test against the QNN").

Polaritonic Nonlinear Network Development:
Significant progress was made in the design and fabrication of the QNN core based on exciton-polaritons. The initial photonic building blocks, such as grating couplers and waveguides, were designed and fabricated. Notably, propagating polariton modes were successfully observed, and a waveguide structure with a single quantum well was characterised. Work on II-VI materials also advanced with the growth of new microcavities designed to increase the Q factor and the quantum well homogeneity. These developments are captured in Deliverable D2.2 ("Growth of AlGaAs and CdTe heterostructures") and Deliverable D2.1 ("Best design of an integrated polariton QNN based on the GaAs waveguides and the CdTe vertical microcavity platform").

Numerical Simulations and Design Optimization:
Extensive numerical simulations were carried out using the Positive-P and Hartree-Fock-Bogoliubov methods, which were selected for simulating the quantum system. These simulations were crucial for the design optimisation of the QNN and are detailed in Deliverable D3.1 ("Report on numerical studies of optimal designs for quantum reservoirs").

Integration of Quantum Sources with QNN:
Work toward integrating quantum light sources with the QNN platform is underway, with ongoing research into coupling mechanisms. This integration will enable efficient quantum state recognition and generation within the QNN, contributing to the realisation of Milestone M5 ("Efficient coupling of quantum light to polariton QNN") by month 36.

Overall, the project is on schedule, with all deliverables deployed and one milestone already achieved. These accomplishments provide a solid foundation for the next stages of the project, particularly the final design and implementation of the QNN device.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

The Q-ONE project holds the potential to significantly impact a variety of fields, including quantum computing, communications, and sensing. The ability to combine state recognition and generation in a single device will simplify and speed up the development of next-generation quantum technologies, could enable quicker uptake and commercialisation.

Continued research is necessary to refine the QNN’s capabilities, particularly in scaling up the system for larger quantum networks. Demonstrating the platform’s performance in real-world applications will attract interest from industry and potential end-users.

Given the disruptive potential of Q-ONE’s quantum platform, if successful, pathways for commercialisation will need to be established. This includes securing investment and developing partnerships with technology companies, particularly those in the quantum computing and quantum information.

Intellectual property protection will play a critical role in ensuring the results of the project can be commercially exploited. In parallel, engaging with regulatory bodies to align Q-ONE’s innovations with evolving quantum technology standards and protocols will be necessary to facilitate integration into global markets.

Overall, Q-ONE is pioneering the development of quantum neural networks that go beyond the current state of the art by offering a versatile, scalable, and reconfigurable platform capable of both recognizing and generating quantum states. These advances have the potential to transform the quantum technology landscape and significantly impact various industries.