A quantum neural network framework for scalable quantum circuit approximation of unitary matrices

Autor:	Sarkar, Rohit Sarma, Adhikari, Bibhas
Rok vydání:	2024
Předmět:	Quantum Physics Computer Science - Computational Complexity Computer Science - Data Structures and Algorithms
Druh dokumentu:	Working Paper
Popis:	In this paper, we develop a Lie group theoretic approach for parametric representation of unitary matrices. This leads to develop a quantum neural network framework for quantum circuit approximation of multi-qubit unitary gates. Layers of the neural networks are defined by product of exponential of certain elements of the Standard Recursive Block Basis, which we introduce as an alternative to Pauli string basis for matrix algebra of complex matrices of order $2^n$. The recursive construction of the neural networks implies that the quantum circuit approximation is scalable i.e. quantum circuit for an $(n+1)$-qubit unitary can be constructed from the circuit of $n$-qubit system by adding a few CNOT gates and single-qubit gates. Comment: 58 pages. arXiv admin note: substantial text overlap with arXiv:2304.14096
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.00012 Zobrazit plný text záznamu View this record from Arxiv