Quantum Error Correction with magnetic molecules

Quantum algorithms often assume independent spin qubits to produce trivial $|\uparrow\rangle=|0\rangle$, $|\downarrow\rangle=|1\rangle$ mappings. This can be unrealistic in many solid-state implementations with sizeable magnetic interactions. Here we show that the lower part of the spectrum of a molecule containing three exchange-coupled metal ions with $S=1/2$ and $I=1/2$ is equivalent to nine electron-nuclear qubits. We derive the relation between spin states and qubit states in reasonable parameter ranges for the rare earth $^{159}$Tb$^{3+}$ and for the transition metal Cu$^{2+}$, and study the possibility to implement Shor's Quantum Error Correction code on such a molecule. We also discuss recently developed molecular systems that could be adequate from an experimental point of view.Comment: 5 pages, 3 figures, 2 table

Spin-crossover iron (II) complex showing thermal hysteresis around room temperature with symmetry breaking and an unusually high T(LIESST) of 120 K

We report a Fe(II) complex based on 4′,4′′ carboxylic acid disubstituted dipyrazolylpyridine that shows a spin-crossover close to room temperature associated to a crystallographic phase transition and the LIESST effect with a high T(LIESST) of 120 K

Lanthanide molecular nanomagnets as probabilistic bits

Over the decades, the spin dynamics of a large set of lanthanide complexes have been explored. Lanthanide-based molecular nanomagnets are bistable spin systems, generally conceptualized as classical bits, but many lanthanide complexes have also been presented as candidate quantum bits (qubits). Here we offer a third alternative and model them as probabilistic bits (p-bits), where their stochastic behavior constitutes a computational resource instead of a limitation. We present a modelling tool for molecular spin p-bits, we demonstrate its capability to simulate bulk magnetic relaxation data and ac experiments and to simulate a minimal p-bit network under realistic conditions. Finally, we go back to a recent systematic data gathering and screen the best lanthanide complexes for p-bit behavior, lay out the performance of the different lanthanide ions and chemical families and offer some chemical design considerations

Polymer-Based Composites for Engineering Organic Memristive Devices

Memristive materials play a key role in the development of neuromorphic technology given that they can combine information processing with volatile or nonvolatile memory storage in a single computational component. Both functionalities are strictly required for the design and implementation of neuromorphic circuits. Many of these bioinspired materials emulate the characteristics of memory and learning processes that happen in the brain. The memristive properties of a two-terminal (2-T) organic device based on ionic migration mediated by an ion-transport polymer are reported here. The material possesses unique memristive properties: it is reversibly switchable, shows tens of conductive states, presents Hebbian learning demonstrated by spiking time dependent plasticity, and behaves with both short- and long-term memory in a single device. The origin and synergy of both learning phenomena are theoretically explained by means of the chemical interaction between ionic electrolytes and the ion-conductive mediator. Further discussion on the transport mechanism is included to explain the dynamic behavior of these ionic devices under a variable electric field. This polymer-based composite as an outstanding neuromorphic material is proposed for being tunable, cheap, flexible, easy to process, reproducible, and more biocompatible than their inorganic analogs

Towards peptide-based tunable multistate memristive materials

Development of new memristive hardware is a technological requirement towards widespread neuromorphic computing. Molecular spintronics seems to be a fertile field for the design and preparation of this hardware. Within molecular spintronics, recent results on metallopeptides demonstrating the interaction between paramagnetic ions and the chirality induced spin selectivity effect hold particular promise for developing fast (ns–μs) operation times. [R. Torres-Cavanillas et al., J. Am. Chem. Soc., 2020, DOI: 10.1021/jacs.0c07531]. Among the challenges in the field, a major highlight is the difficulty in modelling the spin dynamics in these complex systems, but at the same time the use of inexpensive methods has already allowed progress in that direction. Finally, we discuss the unique potential of biomolecules for the design of multistate memristors with a controlled- and indeed, programmable-nanostructure, allowing going beyond anything that is conceivable by employing conventional coordination chemistry.ERC-CoG DECRESIM 647301COST-MOLSPIN-CA15128CTQ2017-8952CEX2019-000919-MPrometeo Program of ExcellencePRECOMP14-202646Development of new memristive hardware is a technological requirement towards widespread neuromorphic computing. Molecular spintronics seems to be a fertile field for the design and preparation of this hardware. Within molecular spintronics, recent results on metallopeptides demonstrating the interaction between paramagnetic ions and the chirality induced spin selectivity effect hold particular promise for developing fast (ns–μs) operation times. [R. Torres-Cavanillas et al., J. Am. Chem. Soc., 2020, DOI: 10.1021/jacs.0c07531]. Among the challenges in the field, a major highlight is the difficulty in modelling the spin dynamics in these complex systems, but at the same time the use of inexpensive methods has already allowed progress in that direction. Finally, we discuss the unique potential of biomolecules for the design of multistate memristors with a controlled- and indeed, programmable-nanostructure, allowing going beyond anything that is conceivable by employing conventional coordination chemistry

Data-driven design of molecular nanomagnets

Three decades of research in molecular nanomagnets have raised their magnetic memories from liquid helium to liquid nitrogen temperature thanks to a wise choice of the magnetic ion and coordination environment. Still, serendipity and chemical intuition played a main role. In order to establish a powerful framework for statistically driven chemical design, here we collected chemical and physical data for lanthanide-based nanomagnets, catalogued over 1400 published experiments, developed an interactive dashboard (SIMDAVIS) to visualise the dataset, and applied inferential statistical analysis. Our analysis shows that the Arrhenius energy barrier correlates unexpectedly well with the magnetic memory. Furthermore, as both Orbach and Raman processes can be affected by vibronic coupling, chemical design of the coordination scheme may be used to reduce the relaxation rates. Indeed, only bis-phthalocyaninato sandwiches and metallocenes, with rigid ligands, consistently present magnetic memory up to high temperature. Analysing magnetostructural correlations, we offer promising strategies for improvement, in particular for the preparation of pentagonal bipyramids, where even softer complexes are protected against molecular vibrations