2025

Two-dimensional electronic spectroscopy (2DES) is a powerful tool for exploring quantum effects in energy transport within photosynthetic systems and investigating novel material properties. However, simulating the dynamics of these experiments poses significant challenges for classical computers due to the large system sizes, long timescales, and numerous experiment repetitions involved. This paper introduces the probe qubit protocol (PQP)-for quantum simulation of 2DES on quantum devices-addressing these challenges. The PQP offers several enhancements over standard methods, notably reducing computational resources, by requiring only a single-qubit measurement per circuit run and achieving Heisenberg scaling in detection frequency resolution, without the need to apply expensive controlled evolution operators in the quantum circuit. The implementation of the PQP protocol requires only one additional ancilla qubit, the probe qubit, with one-to-all connectivity and two-qubit interactions between each system and probe qubits. We evaluate the computational resources necessary for this protocol in detail, demonstrating its function as a dynamic frequency-filtering method through numerical simulations. We find that simulations of the PQP on classical and quantum computers enable a reduction on the number of measurements, i.e., simulation runtime, and memory savings of several orders of magnitude relative to standard quantum simulation protocols of 2DES. The paper discusses the applicability of the PQP on near-term quantum devices and highlights potential applications where this spectroscopy simulation protocol could provide significant speedups over standard approaches such as the quantum simulation of 2DES applied to the Fenna-Matthews-Olson (FMO) complex in green sulfur bacteria.

13C hyperpolarization with nitrogen-vacancy centers in micro- and nanodiamonds for sensitive magnetic resonance applications, Rémi Blinder, Yuliya Mindarava, Martin Korzeczek, Alastair Marshall, Felix Glöckler, Steffen Nothelfer, Alwin Kienle, Christian Laube, Wolfgang Knolle, Christian Jentgens, Martin B. Plenio, and Fedor Jelezko, Sci. Adv. 11, eadq6836 (2025), arXiv:2403.14521

Nuclear hyperpolarization is a known method to enhance the signal in nuclear magnetic resonance (NMR) by orders of magnitude. The present work addresses the 13C hyperpolarization in diamond micro- and nanoparticles, using the optically pumped nitrogen-vacancy center (NV) to polarize 13C spins at room temperature. Consequences of the small particle size are mitigated by using a combination of surface treatment improving the 13C relaxation (T1) time, as well as that of NV, and applying a technique for NV illumination based on a microphotonic structure. Adjustments to the dynamical nuclear polarization sequence (PulsePol) are performed, as well as slow sample rotation, to improve the NV-13C polarization transfer rate. The hyperpolarized 13C NMR signal is observed in particles of 2-micrometer and 100-nanometer median sizes, with enhancements over the thermal signal (at 0.29-tesla magnetic field) of 1500 and 940, respectively. The present demonstration of room-temperature hyperpolarization anticipates the development of agents based on nanoparticles for sensitive magnetic resonance applications.

YASTN: Yet another symmetric tensor networks; A Python library for Abelian symmetric tensor network calculations, Marek M. Rams, Gabriela Wójtowicz, Aritra Sinha, and Juraj Hasik, SciPost Phys. Codebases (2025), arXiv:2405.12196

We present an open-source tensor network Python library for quantum many-body simulations. At its core is an abelian-symmetric tensor, implemented as a sparse block structure managed by a logical layer on top of a dense multi-dimensional array backend. This serves as the basis for higher-level tensor network algorithms, operating on matrix product states and projected entangled pair states. An appropriate backend, such as PyTorch, gives direct access to automatic differentiation (AD) for cost-function gradient calculations and execution on GPU and other supported accelerators. We show the library performance in simulations with infinite projected entangled-pair states, such as finding the ground states with AD and simulating thermal states of the Hubbard model via imaginary time evolution. For these challenging examples, we identify and quantify sources of the numerical advantage exploited by the symmetric-tensor implementation.

Unlocking Heisenberg Sensitivity with Sequential Weak Measurement Preparation, Trinidad B Lantaño, Dayou Yang, Koenraad M R Audenaert, Susana F Huelga, and Martin B Plenio, Quantum 9, 1590 (2025), arXiv:2403.05954

We propose a state preparation protocol based on sequential measurements of a central spin coupled with a spin ensemble, and investigate the usefulness of the generated multi-spin states for quantum enhanced metrology. Our protocol is shown to generate highly entangled spin states, devoid of the necessity for non-linear spin interactions. The metrological sensitivity of the resulting state surpasses the standard quantum limit, reaching the Heisenberg limit under symmetric coupling strength conditions. We also explore asymmetric coupling strengths, identifying specific preparation windows in time for optimal sensitivity. Our findings introduce a novel method for generating large-scale, non-classical, entangled states, enabling quantum-enhanced metrology within current experimental capabilities.

Contact Bluesky logoX logoGitHub logo

Ulm University
Institute of Theoretical Physics
Albert-Einstein-Allee 11
D - 89081 Ulm
Germany

Tel: +49 731 50 22911
Fax: +49 731 50 22924

Office: Building M26, room 4117

Click here if you are interested in joining the group.

Most Recent Papers

Accelerating two-dimensional electronic spectroscopy simulations with a probe qubit protocol, Phys. Rev. Research 7, 023130 (2025), arXiv:2411.16290

13C hyperpolarization with nitrogen-vacancy centers in micro- and nanodiamonds for sensitive magnetic resonance applications, Sci. Adv. 11, eadq6836 (2025), arXiv:2403.14521

YASTN: Yet another symmetric tensor networks; A Python library for Abelian symmetric tensor network calculations, SciPost Phys. Codebases (2025), arXiv:2405.12196

Unlocking Heisenberg Sensitivity with Sequential Weak Measurement Preparation, Quantum 9, 1590 (2025), arXiv:2403.05954

Time dependent Markovian master equation beyond the adiabatic limit, Quantum 8, 1534 (2024), arXiv:2304.06166