CoQHoNet · Connecting Quantum Hopfield Networks
Horizon Europe — Marie Skłodowska-Curie Actions
- Duration
- 2023-06-01 → 2025-05-31
- EU contribution
- €165,313
- Participants
- 2
- Scheme
- HORIZON-TMA-MSCA-PF-EF
Lines connect the coordinator with its partners.
Results in brief
Connecting Quantum Hopfield Networks
Nowadays Classical Neural Networks (NNs) are widely used in Machine Learning (ML) for tasks like pattern recognition, big data analysis, and digitalization. At the same time, the development of quantum technologies is seen as a promising advancement over classical ML, leading to growing interest in quantum NNs as the foundation of quantum ML. However, a comprehensive framework for quantum NNs is still lacking. In this context, this project aims to contribute to define the core features of quantum NNs and to explore their practical applications. Specifically, the project focuses on quantum versions of associative memory-type NNs, with the Hopfield NN serving as a key example. Associative memories can perform simple tasks like pattern retrieval, but they also play crucial roles in more complex architectures, such the Boltzmann Machines. Current quantum associative memory research leverages quantum spin NNs and bosonic systems, modelling them as Markovian (or memory-less) open quantum systems. These models are especially relevant to condensed matter and photonic implementations. The goal of this project consists in integrating these proposals into a broader theoretical framework, particularly bridging the gap between Markovian quantum Hopfield-type NNs and the more general formalism of quantum maps. This framing is expected to provide valuable insights to address important questions, such as storing non-classical states, understanding non-Markovian effects on retrieval tasks, and characterizing the storage capacity.
Data: CORDIS, © European Union
Project objective
Classical Neural Networks (NNs) are architectures successfully employed in Machine Learning (ML) tasks, such as pattern recognition, analysis of big data, and digitalization. Currently, a full development of quantum technologies is considered the most promising improvement on classical ML. Motivated by this, various contributions are focusing on the emerging field of quantum NNs, regarded as the backbones of quantum ML. Though strategic to tackle digitalization challenges of Europe concerning e.g. secure information, a unifying framework for quantum NNs is still missing.This project aims at contributing to the general effort of defining the main features of quantum NNs, and further exploiting them for practical purposes. More concretely, it considers quantum generalizations of associative memory-type NNs, such as the prototypical example referred to as Hopfield NN. To start with, associative memories can perform relatively easy tasks such as pattern retrieval. However, they are employed in more complex architectures too, as it is the case of Hopfield NNs when embedded in Boltzmann Machines. State-of-the-art quantum associative memories exploit quantum spin NNs and bosonic platforms, describing them as Markovian, or memory-less, open quantum systems. Importantly, these setups are relevant for condensed matter and photonic implementations. Inserting these proposals in a more general theoretical frame can provide insights to answer timely questions, such as storing non-classical states, accounting for non-Markovian effects on the retrieval task, characterizing the storage capacity. The challenge of this proposal consists in unveiling the bridge between Markovian quantum Hopfield-type NNs, and the formalism of quantum maps. Fulfilling such a gap further enables to use associative memories for designing near-term quantum devices that are robust to detrimental effects of the environment, which generically cause deterioration of e.g. coherence and entanglement.
Original text from CORDIS.
Participants
Links
- View on CORDIS
- DOI: 10.3030/101105267
- https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e50c325c8d&appId=PPGMS
- https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e51b946bd7&appId=PPGMS
Data: CORDIS, © European Union