Group Sparse Matrix Optimization for Efficient Quantum State Transformation
arXiv:2406.00698 · doi:10.1103/PhysRevA.110.022445
Abstract
Finding ways to transform a quantum state to another is fundamental to quantum information processing. In this paper, we apply the sparse matrix approach to the quantum state transformation problem. In particular, we present a new approach for searching for unitary matrices for quantum state transformation by directly optimizing the objective problem using the Alternating Direction Method of Multipliers (ADMM). Moreover, we consider the use of group sparsity as an alternative sparsity choice in quantum state transformation problems. Our approach incorporates sparsity constraints into quantum state transformation by formulating it as a non-convex problem. It establishes a useful framework for efficiently handling complex quantum systems and achieving precise state transformations.
21 pages, 3 figures
References in corpus (15)
- Quantum state tomography via compressed sensing
- Quantum Circuits for General Multiqubit Gates
- Molecular Quantum Computing by an Optimal Control Algorithm for Unitary Transformations
- Quantum process tomography of unitary and near-unitary maps
- Synthesis of Multivalued Quantum Logic Circuits by Elementary Gates
- Exploring the trade-off between fidelity- and time-optimal control of quantum unitary transformations
- Quantum state tomography by continuous measurement and compressed sensing
- Quantum Circuits for Sparse Isometries
- Double sparse quantum state preparation
- Quantum parameter estimation on coherently superposed noisy channels
- Learning physically consistent mathematical models from data using group sparsity
- Experimental Quantum Learning of a Spectral Decomposition
- Unitarity estimation for quantum channels
- Fast Reconstruction of High-qubit Quantum States via Low Rate Measurements
- Effective quantum state reconstruction using compressive sensing in NMR quantum computing