Universal quantum state preparation via revised greedy algorithm
arXiv:2108.03351 · doi:10.1088/2058-9565/ac1dfe
Abstract
Preparation of quantum state lies at the heart of quantum information processing. The greedy algorithm provides a potential method to effectively prepare quantum states. However, the standard greedy algorithm, in general, cannot take the global maxima and instead becomes stuck on a local maxima. Based on the standard greedy algorithm, in this paper we propose a revised version to design dynamic pulses to realize universal quantum state preparation, i.e., preparing any arbitrary state from another arbitrary one. As applications, we implement this scheme to the universal preparation of single- and two-qubit state in the context of semiconductor quantum dots and superconducting circuits. Evaluation results show that our scheme outperforms the alternative numerical optimizations with higher preparation quality while possesses the comparable high efficiency. Compared with the emerging machine learning, it shows a better accessibility and does not require any training. Moreover, the numerical results show that the pulse sequences generated by our scheme are robust against various errors and noises. Our scheme opens a new avenue of optimization in few-level system and limited action space quantum control problems.
References in corpus (17)
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Charge insensitive qubit design derived from the Cooper pair box
- A Quantum Approximate Optimization Algorithm
- Strong quantum computational advantage using a superconducting quantum processor
- Demonstration of Entanglement of Electrostatically Coupled Singlet-Triplet Qubits
- Universal quantum control of two-electron spin quantum bits using dynamic nuclear polarization
- Superconducting Quantum Computing: A Review
- Quantum walks on a programmable two-dimensional 62-qubit superconducting processor
- High-Fidelity Single-Shot Toffoli Gate via Quantum Control
- Self-guided quantum tomography
- Deep Reinforcement Learning for Quantum Gate Control
- Nonperturbative master equation solution of central spin dephasing dynamics
- Quantum bits with Josephson junctions
- Deep Reinforcement Learning Control of Quantum Cartpoles
- Classifying global state preparation via deep reinforcement learning
- Deep reinforcement learning for universal quantum state preparation via dynamic pulse control
- Fast pulse sequences for dynamically corrected gates in single-triplet qubits
Cited by in corpus (4)
- Simulating noisy quantum channels via quantum state preparation algorithms
- Simulation of positive operator-valued measures and quantum instruments via quantum state preparation algorithms
- Modularized and Scalable Compilation for Double Quantum Dot Quatum Computing
- Approximate quantum gates compiling with self-navigation algorithm