Research

Tensor Network Methods

Tensor Networks provide efficient representation for quantum states and the basis for variational algorithms for finding the ground-state and thermal-states of quantum many-body Hamiltonians.

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Frustrated Quantum Magnetism and Spin Liquids

Frustrated spin systems are a unique and ideal platform for hosting exotic phases of matter such as quantum spin liquids, valence-bond crystals, and other interesting types of quantum paramagnets.

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Topological Spin Models and Quantum Computation

Topological quantum codes such as the Kitaev model and topological color codes are not only potential candidates for fault-tolerant quantum computation but also ideal playgrounds for characterizing quantum spin liquids, their anyonic excitations, and fractionalization mechanism.

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Quantum and Quantum-Inspired Optmiziation

Quantum-Inspired methods based on tensor networks and also pure quantum approaches such as QAOA or VQE are the future of optimization and quantum technology.

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Quantum Machine Learning and Deep Neural Networks

Quantum Computers and quantum-inspired approaches based on the tensor network can enhance and boost the machine learning tasks such as classification, clustering and even training of deep neural networks.

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Numerical Algorithms and High-Performance Computing

A great part of our research at IASBS involves developing advanced and computationally fast algorithms for simulating physics problems. Our main computational resource is the GAVAZANG HPC cluster

GAVAZANG Cluster