Beyond Ans"atze: Learning Quantum Circuits as Unitary Operators
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Abstract (arXiv)
This paper explores the advantages of optimizing quantum circuits on N wires as operators in the unitary group U(2N). We run gradient-based optimization in the Lie algebra mathfrak u(2N) and use the exponential map to parametrize unitary matrices. We argue that U(2N) is not only more general than the search space induced by an ansatz, but in ways easier to work with on classical computers. The resulting approach is quick, ansatz-free and provides an upper bound on performance over all ans"atze on N wires.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 37 references that are in the map. First posted on arXiv 1 Mar 2022.
Builds on
9 of 37 references.
References · 9
- 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
- 2020Robust data encodings for quantum classifierscited by 91
- 2015DeepSat - A Learning framework for Satellite Imagerycited by 37
- 2015Application of Quantum Annealing to Training of Deep Neural Networkscited by 37
- 2021On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classificationcited by 33
- 2021Capacity and quantum geometry of parametrized quantum circuitscited by 32
- 2020Quantum versus Classical Generative Modelling in Financecited by 21
- 2021Synthetic weather radar using hybrid quantum-classical machine learningcited by 5
- 2020Evaluation of Parameterized Quantum Circuits: on the relation between classification accuracy, expressibility and entangling capabilitycited by 2
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.