Adaptive optimal control of entangled qubits.

Area of Science:

• Quantum Information Science
• Quantum Computing
• Quantum Control

Background:

• Optimal control of quantum systems is crucial but computationally expensive.
• Existing algorithms often overlook the high computational cost for interacting particle systems.
• Need for efficient and accurate methods for quantum control applications.

Purpose of the Study:

• To present a novel, fast, and accurate optimal control algorithm for interacting qubit systems.
• To address the computational cost limitations of current quantum control methods.
• To provide a general and accurate solution compatible with diverse Hamiltonian structures.

Main Methods:

• Introduced QOALA (Quantum Optimal Control by Adaptive Low-cost Algorithm).
• Employs inexpensive, low-accuracy approximations for propagators far from the optimum.
• Adaptively switches to higher-accuracy, higher-cost propagators as the optimum is approached.
• Utilizes analytical Lie algebraic derivatives, avoiding computationally expensive matrix exponentials.

Main Results:

• QOALA offers a predicted O(M^2) speedup for an M-qubit system compared to state-of-the-art exact methods.
• The algorithm maintains the overall accuracy of the optimal solution.
• Demonstrates enhanced performance due to the use of analytical derivatives.

Conclusions:

• QOALA provides a significant advancement in computational efficiency for quantum optimal control.
• The adaptive strategy and analytical derivatives make quantum control more accessible and practical.
• This method is broadly applicable to various quantum systems and Hamiltonian structures.