22 citations · 26 across the 6 of their papers we have counts for
6 papers
Machine Learning Message-Passing for the Scalable Decoding of QLDPC Codes
Arshpreet Singh Maan, Alexandru Paler
We present Astra, a novel and scalable decoder using graph neural networks. Our decoder works similarly to solving a Sudoku puzzle of constraints represented by the Tanner graph. I…
On the Need for Extensible Quantum Compilers with Verification
Tyler LeBlond, Xiao Xiao, Eugene Dumitrescu +2
In this position paper, we posit that a major Department of Energy (DOE)-funded open-source quantum compilation platform is needed to facilitate: (a) resource optimization at the f…
Graph Neural Network Autoencoders for Efficient Quantum Circuit Optimisation
Ioana Moflic, Vikas Garg, Alexandru Paler
Reinforcement learning (RL) is a promising method for quantum circuit optimisation. However, the state space that has to be explored by an RL agent is extremely large when consider…
Wire Recycling for Quantum Circuit Optimization
Alexandru Paler, Robert Wille, Simon J. Devitt
Quantum information processing is expressed using quantum bits (qubits) and quantum gates which are arranged in the terms of quantum circuits. Here, each qubit is associated to a q…
Cross-level Validation of Topological Quantum Circuits
Alexandru Paler, Simon J. Devitt, Kae Nemoto +1
Quantum computing promises a new approach to solving difficult computational problems, and the quest of building a quantum computer has started. While the first attempts on constru…
Mapping of Topological Quantum Circuits to Physical Hardware
Alexandru Paler, Simon J. Devitt, Kae Nemoto +1
Topological quantum computation is a promising technique to achieve large-scale, error-corrected computation. Quantum hardware is used to create a large, 3-dimensional lattice of e…