The Ueda Group studies quantum many-body systems, tensor network methods, and quantum algorithms from the unified perspective of quantum entanglement.

Our central goal is to establish scalable computational frameworks that bridge classical high-performance computing and quantum computation.

We view tensor networks not only as numerical tools, but also as guiding principles for designing quantum algorithms, hybrid computational workflows, and efficient representations of quantum states.

By combining condensed matter physics, quantum information, large-scale numerical computation, and quantum computing, we aim to develop practical methodologies for understanding and simulating quantum systems.

We investigate tensor network representations as both numerical methods and computational principles for quantum algorithms.

Our research includes:

• Matrix Product States (MPS)
• Projected Entangled Pair States (PEPS)
• Tree Tensor Networks (TTN)
• Canonical forms and entanglement structures
• Tensor-network optimization
• Variational tensor-network ansätze
• Tensor-network-inspired quantum circuits

We are particularly interested in how entanglement structures reduce computational complexity in both classical and quantum computation.

Current projects also explore tensor-network approaches to lattice gauge theories, nonequilibrium dynamics, and scalable hybrid algorithms.

We develop hybrid quantum-classical algorithms inspired by tensor network structures and variational representations of quantum states.

Our recent work includes:

• Variational quantum singular value decomposition
• Entanglement spectrum estimation
• Hybrid quantum-classical optimization
• Quantum algorithms for strongly correlated systems
• Shallow-circuit quantum computation
• Tensor-network-assisted quantum algorithms

A key direction of our research is the separation of numerical accuracy from quantum circuit optimization.

By introducing classical orthogonality correction and tensor-network-inspired post-processing, we aim to enable robust computations on realistic near-term quantum devices.

We investigate computational frameworks integrating tensor networks, quantum devices, and classical numerical computation.

Our goal is to establish practical hybrid workflows for large-scale quantum simulations beyond the reach of purely classical approaches.

Current topics include:

• Variational quantum eigensolvers
• Concurrent quantum-classical computation
• Error mitigation through classical post-processing
• Tensor-network-assisted quantum optimization
• Hybrid simulation of strongly correlated systems

We are particularly interested in architectures where classical and quantum resources cooperate rather than compete.

We study strongly correlated quantum systems, frustrated magnetism, lattice gauge theories, and quantum critical phenomena.

Our research explores the relationship between entanglement structures, emergent many-body phenomena, and computational complexity.

Topics of interest include:

• Quantum spin systems
• Frustrated magnetism
• Kagome and low-dimensional systems
• Quantum phase transitions
• Entanglement structures in many-body physics
• Nonequilibrium quantum dynamics
• Gauge-theory-inspired quantum simulations

We combine analytical methods, tensor network techniques, and large-scale numerical simulations to investigate quantum many-body phenomena.

We explore scalable computational infrastructures integrating high-performance computing (HPC) and quantum computation.

Our research aims to establish next-generation frameworks for large-scale many-body simulations using both classical supercomputers and quantum hardware.

This includes:

• Distributed tensor-network computation
• Quantum-assisted numerical methods
• Concurrent hybrid workflows
• HPC acceleration of variational algorithms
• Scalable simulation infrastructures

We believe that future quantum simulation platforms will emerge from the cooperation of classical HPC systems, tensor-network methods, and quantum accelerators.