Quantum Machine Learning for Optimizing Entanglement Distribution in Quantum Sensor Circuits
This work addresses the challenge of improving quantum sensor performance for applications requiring high sensitivity and precision, representing an incremental advancement by applying machine learning to a known bottleneck.
The paper tackled the problem of optimizing entanglement distribution in quantum sensor circuits to enhance sensitivity and efficiency, achieving significant improvements with QFI and entropy in the range of 0.84-1.0 and depth/gate count reductions of 20-86%.
In the rapidly evolving field of quantum computing, optimizing quantum circuits for specific tasks is crucial for enhancing performance and efficiency. More recently, quantum sensing has become a distinct and rapidly growing branch of research within the area of quantum science and technology. The field is expected to provide new opportunities, especially regarding high sensitivity and precision. Entanglement is one of the key factors in achieving high sensitivity and measurement precision [3]. This paper presents a novel approach utilizing quantum machine learning techniques to optimize entanglement distribution in quantum sensor circuits. By leveraging reinforcement learning within a quantum environment, we aim to optimize the entanglement layout to maximize Quantum Fisher Information (QFI) and entanglement entropy, which are key indicators of a quantum system's sensitivity and coherence, while minimizing circuit depth and gate counts. Our implementation, based on Qiskit, integrates noise models and error mitigation strategies to simulate realistic quantum environments. The results demonstrate significant improvements in circuit performance and sensitivity, highlighting the potential of machine learning in quantum circuit optimization by measuring high QFI and entropy in the range of 0.84-1.0 with depth and gate count reduction by 20-86%.