Quantum collision models are pivotal for simulating open quantum systems, yet lack comprehensive error certification. We analytically derive Markovian and non-Markovian collision models from chain mapping techniques, identifying a critical error source, thus elevating collision models to numerically exact methods and enhancing their reliability across quantum simulations.

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.

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.

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.

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.