arXiv subjects

Tom R. Rieckmann

Publications and source records attributed to Tom R. Rieckmann.

2 recordsLinked to original sources

arXiv2026

Vector Magnetometry with Broadband Microwave Fields in Nitrogen-Vacancy Centers in Diamond

We present a novel method for full vector magnetometry using nitrogen-vacancy (NV) centers. In contrast to conventional optically detected magnetic resonance techniques, our method employs two distinct broadband microwave pulses and measures them after transmission through the NV sensor medium, thus capturing the line splitting of the ground state triplet due to the Zeeman effect. Two orthogonally polarized microwave pulses allow resolving all magnetic field components independently by reading out differently oriented NV centers. Simulated data is analyzed using deep neural networks, whose efficacy we expect to translate very well to experiments. Our method yields sensitivities between $5~\mathrm{pT}/\sqrt{\mathrm{Hz}}$ and $100~\mathrm{pT}/\sqrt{\mathrm{Hz}}$ across different magnetic field vector components, while achieving approximately $\mathrm{nT}$ accuracy at a signal-to-noise (SNR) ratio of $70~\mathrm{dB}$. By being capable of accurately measuring magnetic fields down to $25~\mathrm{μT}$, the need for a bias field beyond Earth's magnetic field is eliminated.

quant-ph↗

arXiv2025

Gate Sequence Optimization for Parameterized Quantum Circuits using Reinforcement Learning

Current experimental quantum computing devices are limited by noise, mainly originating from entangling gates. If an efficient gate sequence for an operation is unknown, one often employs layered parameterized quantum circuits, especially hardware-efficient ansätze, with fixed entangling layer structures. We demonstrate a reinforcement learning algorithm to improve on these by optimizing the entangling gate sequence in the task of quantum state preparation. This allows us to restrict the required number of CNOT gates while taking the qubit connectivity architecture into account. Recent advancements using reinforcement learning have already demonstrated the power of this technique when optimizing the circuit for a sequence of non-parameterized gates. We extend this approach to parameterized gate sets by incorporating general single-qubit unitaries, thus allowing us to consistently reach higher state preparation fidelities at the same number of CNOT gates compared to a hardware-efficient ansatz.

quant-ph↗