I am a Postdoc at HelTeq, University of Helsinki, working in the group of Sabrina Maniscalco. My research focuses on the intersection of classical machine learning and quantum computing, leveraging near-term quantum hardware and beyond with advanced ML methods. I work in Quantum Architecture Search. My true self Resides Here.
A curated list of standout libraries, tutorials, research papers, and essential resources focused on Quantum Architecture Search (QAS). This collection is designed to serve as a structured and thorough reference, empowering researchers and developers to accelerate their work and stay at the forefront of QAS advancements. The repository contains:
The SYK model, known for its strong quantum correlations and chaotic behavior, serves as a key platform for quantum gravity studies. However, variationally preparing thermal states on near-term quantum devices for large systems presents a significant challenge due to the rapid growth in the complexity of parameterized quantum circuits. We address this by combining reinforcement learning with convolutional neural networks, iteratively refining the quantum circuit and its parameters using a composite reward signal from entropy and SYK Hamiltonian expectation values. Key findings:
Introducing TensorRL-QAS, a scalable framework that combines tensor network (TN) methods with RL for designing quantum circuits. By warm-starting the architecture search with a matrix product state approximation of the target solution, TensorRL-QAS effectively narrows the search space to physically meaningful circuits, accelerating convergence to the desired solution. Key findings:
