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Paper 1
Learning Better Error Correction Codes with Hybrid Quantum-Assisted Machine Learning
Yariv Yanay
- Year
- 2026
- Journal
- arXiv preprint
- DOI
- arXiv:2601.08014
- arXiv
- 2601.08014
Quantum error correction is one of the fundamental building blocks of digital quantum computation. The Quantum Lego formalism has introduced a systematic way of constructing new stabilizer codes out of basic lego-like building blocks, which in previous work we have used to generate improved error correcting codes via an automated reinforcement learning process. Here, we take this a step further and show the use of a hybrid classical-quantum algorithm. We combine classical reinforcement learning with calls to two commercial quantum devices to search for a stabilizer code to correct errors specific to the device, as well as an induced photon loss error.
Open paperPaper 2
Optimizing and benchmarking the computation of the permanent of general matrices
Cassandra Masschelein, Michelle Richer, Paul W. Ayers
- Year
- 2025
- Journal
- arXiv preprint
- DOI
- arXiv:2510.03421
- arXiv
- 2510.03421
Evaluating the permanent of a matrix is a fundamental computation that emerges in many domains, including traditional fields like computational complexity theory, graph theory, many-body quantum theory and emerging disciplines like machine learning and quantum computing. While conceptually simple, evaluating the permanent is extremely challenging: no polynomial-time algorithm is available (unless $\textsc{P} = \textsc{NP}$). To the best of our knowledge there is no publicly available software that automatically uses the most efficient algorithm for computing the permanent. In this work we designed, developed, and investigated the performance of our software package which evaluates the permanent of an arbitrary rectangular matrix, supporting three algorithms generally regarded as the fastest while giving the exact solution (the straightforward combinatoric algorithm, the Ryser algorithm, and the Glynn algorithm) and, optionally, automatically switching to the optimal algorithm based on the type and dimensionality of the input matrix. To do this, we developed an extension of the Glynn algorithm to rectangular matrices. Our free and open-source software package is distributed via Github, at https://github.com/theochem/matrix-permanent.
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