Quantum and Continuum Computing – QCC

Research Activity

The research activities of the QCC group focus on the design and execution of intelligent algorithms for solving problems of interest across a wide range of application domains, through the use of new architectures that enable the convergence of heterogeneous computational levels, including Cloud, Edge Computing, the Internet of Things, and quantum computers.

The convergence between classical and quantum computing is becoming essential in modern computer science for at least three reasons: 1) new architectures must be able to integrate classical and quantum hardware in order to execute algorithms with heterogeneous requirements; 2) in the current NISQ (Noisy Intermediate-Scale Quantum) era, the most promising algorithms are hybrid-variational ones, in which quantum computation is parametric and parameter tuning is performed through classical methods; 3) quantum algorithms can be applied to data coming from advanced classical devices, several of which (robots, drones, advanced sensors) are available to the QCC group.

In particular, the aim is to use quantum hardware to improve the efficiency and scalability of machine learning and optimization algorithms. The research activities focus on Quantum Machine Learning (QML), a paradigm that exploits the intrinsic parallelism of quantum systems and the exponential dimensionality of Hilbert space to process and analyze data, offering potential advantages in clustering, classification, regression, and prediction tasks. Two relevant examples currently under investigation are Quantum Reservoir Computing and Quantum Extreme Learning Machines, architectures inspired by their classical counterparts but enhanced through the guided evolution of quantum systems.

The group’s research activities range from methodological and architectural approaches for designing cognitive and intelligent Cyber-Physical systems to the study of distributed algorithms and protocols capable of exploiting the full potential of the continuum to achieve adaptive behaviors, learn control policies, predict user needs, adopt predictive maintenance techniques, ensure specific security requirements, recognize anomalous situations, and respond to them. Current research topics include:

• distributed/decentralized algorithms based on cognitive paradigms (deep reinforcement learning, federated learning, swarm learning) that enable devices to cooperate, learn from their experiences, adapt to changing situations, and predict likely future scenarios by exchanging information, distributing tasks, and coordinating actions;
• multi-agent cognitive edge platforms for the design, development, scheduling, deployment, and maintenance of large-scale complex systems that support monitoring, control, and co-simulation through digital twins in the context of cyber-physical and IoT systems;
• the use of blockchain techniques in IoT devices to reduce hacking risks by minimizing potential access points;
• cognitive environments integrating sensors, actuators, robots, drones, and advanced human-machine interface devices such as Smart Gloves, EEG diadems, and head-mounted displays.

Goals

The Quantum and Continuum Computing (QCC) group at ICAR conducts research in the field of Continuum Computing, a concept that encompasses heterogeneous platforms and devices within a multi-layered architecture spanning from Cloud to Edge Computing, the Internet of Things (IoT), and quantum computers. The goal is to design and execute intelligent algorithms for data analysis, computation, and problem-solving, making optimal use of the computing capabilities available at each level of the continuum. This approach supports a wide range of application domains, such as smart cities, cognitive buildings, and energy management. Depending on the complexity of the problems and the application workload, the execution of algorithms can either be migrated from the Cloud toward the peripheral ecosystem, leveraging new AI chips capable of supporting complex real-time applications on IoT devices, or performed on quantum computers, to exploit and anticipate the scalability achievable through the greater richness of the quantum space (Hilbert space) and the ability to perform parallel computations on superposed states.

Application Fields

The main fields of application are: energy management, smart cities, smart streets, smart grids, intelligent buildings, smart water, urban intelligence, cognitive smart homes, smart mobility, smart parking, and fluid dynamics.

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