13 citations · 14 across the 7 of their papers we have counts for
7 papers
AlphaClifford: Efficient Clifford Synthesis and Transpilation with Model-based RL
Daniele Lizzio Bosco, Jacopo Cossio, Carla Piazza +1
Clifford circuits play a foundational role in quantum computing, particularly due to their importance in quantum error correction and fault-tolerant logical synthesis. While these…
AlphaCNOT: Learning CNOT Minimization with Model-Based Planning
Jacopo Cossio, Daniele Lizzio Bosco, Riccardo Romanello +2
Quantum circuit optimization is a central task in quantum computing, as current NISQ devices suffer from error propagation that often scales with the number of operations. Among qu…
Quantum Circuit Pre-Synthesis: Learning Local Edits to Reduce -count
Daniele Lizzio Bosco, Lukasz Cincio, Giuseppe Serra +1
Compiling quantum circuits into Clifford+ gates is a central task for fault-tolerant quantum computing using stabilizer codes. In the near term, gates will dominate the cost…
Quantum Implicit Neural Representations for Novel View Synthesis
Daniele Lizzio Bosco, Shuteng Wang, Giuseppe Serra +1
Quantum implicit neural representations have recently emerged as compact function approximators for continuous visual signals. In parallel, Neural Radiance Fields (NeRFs) have beco…
CNOT Minimal Circuit Synthesis: A Reinforcement Learning Approach
Riccardo Romanello, Daniele Lizzio Bosco, Jacopo Cossio +4
CNOT gates are fundamental to quantum computing, as they facilitate entanglement, a crucial resource for quantum algorithms. Certain classes of quantum circuits are constructed exc…
Integrated Encoding and Quantization to Enhance Quanvolutional Neural Networks
Daniele Lizzio Bosco, Beatrice Portelli, Giuseppe Serra
Image processing is one of the most promising applications for quantum machine learning (QML). Quanvolutional Neural Networks with non-trainable parameters are the preferred soluti…
Automatic and effective discovery of quantum kernels
Massimiliano Incudini, Daniele Lizzio Bosco, Francesco Martini +3
Quantum computing can empower machine learning models by enabling kernel machines to leverage quantum kernels for representing similarity measures between data. Quantum kernels are…