Soft-Quantum Algorithms
Quantum operations on pure states can be fully represented by unitary matrices. Variational quantum circuits, also known as quantum neural networks, embed data and trainable parameters into gate-based operations and optimize the parameters via gradient descent. The high cost of training and low fidelity of current quantum devices, however, restricts much of quantum machine learning to classical simulation. For few-qubit problems with large datasets, training the matrix elements directly, as is done with weight matrices in classical neural networks, can be faster than decomposing data and parameters into gates. We propose a method that trains matrices directly while maintaining unitarity t...
| Authors | Basil Kyriacou, Mo Kordzanganeh, Maniraman Periyasamy et al. |
|---|---|
| Primary category | quant-ph |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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