Algorithmic Shadow Spectroscopy

We present shadow spectroscopy as a simulator-agnostic quantum algorithm for estimating energy gaps using very few circuit repetitions (shots) and no extra resources (ancilla qubits) beyond performing time evolution and measurements. The approach builds on the fundamental feature that every observable property of a quantum system must evolve according to the same harmonic components: we can reveal them by post-processing classical shadows of time-evolved quantum states to extract a large number of time-periodic signals $N_o\propto 10^8$, whose frequencies correspond to Hamiltonian energy differences with Heisenberg-limited precision. We provide strong analytical guarantees that (a) quantum resources scale as $O(\log N_o)$, while the classical computational complexity is linear $O(N_o)$, (b) the signal-to-noise ratio increases with the number of analysed signals as $\propto \sqrt{N_o}$, and (c) peak frequencies are immune to reasonable levels of noise. Moreover, performing shadow spectroscopy to probe model spin systems and the excited state conical intersection of molecular CH$_2$ in simulation verifies that the approach is intuitively easy to use in practice, robust against gate noise, amiable to a new type of algorithmic-error mitigation technique, and uses orders of magnitude fewer number of shots than typical near-term quantum algorithms -- as low as 10 shots per timestep is sufficient. Finally, we measured a high-quality, experimental shadow spectrum of a spin chain on readily-available IBM quantum computers, achieving the same precision as in noise-free simulations without using any advanced error mitigation.Comment: 31 pages, 13 figures, new results with hardware and figure

Multi-Agent Reinforcement Learning for Railway Rescheduling

Malfunctions, congestions, and accidents occur in every railway system from time to time, which influences the railway traffic on a given section of the system. The disturbance may cause inconvenience for several passengers and disruption in rail freight. Both the schedule and route of the affected trains must be modified to avoid further congestion and minimalize delays. The rigidity of the railway system (e.g., single tracks, vast distances without a service station, no viable alternative in case of malfunction) poses restrictions, unlike other transportation systems. Replanning schedules and train routes (called the railway rescheduling problem) is complex and demanding, even for human operators, as one must consider numerous factors. Thus, finding a satisfying solution poses a significant challenge. This paper presents a MARL-base (Multi-Agent Reinforcement Learning) solution that shows great potential for tackling this problem, even in the case of multiple connected stations

A molecular motor from lignocellulose

Lignin is the largest natural source of functionalized aromatics on the planet, therefore exploiting its inherent structural features for the synthesis of aromatic products is a timely and ambitious goal. While the recently developed lignin depolymerization strategies gave rise to well-defined aromatic platform chemicals, the diversification of these structures, especially toward high-end applications is still poorly addressed. Molecular motors and switches have found widespread application in many important areas such as targeted drug delivery systems, responsive coatings for self-healing surfaces, paints and resins or muscles for soft robotics. They typically comprise a functionalized aromatic backbone, yet their synthesis from lignin has not been considered before. In this contribution, we showcase the synthesis of a novel light-driven unidirectional molecular motor from the specific aromatic platform chemical 4-(3-hydroxypropyl)-2,6-dimethoxyphenol (dihydrosynapyl alcohol) that can be directly obtained from lignocellulose via a reductive catalytic fractionation strategy. The synthetic path takes into account the principles of green chemistry and aims to maintain the intrinsic functionality of the lignin-derived platform molecule

Sequence analysis of the membrane protein gene and nucleocapsid gene of porcine reproductive and respiratory syndrome virus isolated from a swine herd in Hungary

Porcine reproductive and respiratory syndrome virus (PRRSV) was isolated from blood samples taken at a pig farm in Hungary from pigs showing clinical signs of the disease. The virus (ABV 32) was identified as belonging to the European genotype by using type-specific monoclonal antibodies. This was confirmed by comparing the sequence of the membrane protein gene (ORF 6) and the nucleocapsid gene (ORF 7) with the American VR2332 and the European LV genotype reference strain, respectively. Analysis of the amino acid sequence of the ORF 6 and ORF 7 of ABV 32 revealed five amino acid changes in both ORFs when compared with LV, of which two changes in ORF 7 were only found in the Spanish isolates. Additionally, the ORF 7 sequence was compared with corresponding sequences of a total of 21 other European strains. Phylogenetic analysis using the PHYLIP package confirmed the close relationship between the Hungarian and the Spanish isolates. Of all the isolates analysed, ABV 32 and LV were the least related

MobiDB 3.0: more annotations for intrinsic disorder, conformational diversity and interactions in proteins

The MobiDB (URL: mobidb.bio.unipd.it) database of protein disorder and mobility annotations has been significantly updated and upgraded since its last major renewal in 2014. Several curated datasets for intrinsic disorder and folding upon binding have been integrated from specialized databases. The indirect evidence has also been expanded to better capture information available in the PDB, such as high temperature residues in X-ray structures and overall conformational diversity. Novel nuclear magnetic resonance chemical shift data provides an additional experimental information layer on conformational dynamics. Predictions have been expanded to provide new types of annotation on backbone rigidity, secondary structure preference and disordered binding regions. MobiDB 3.0 contains information for the complete UniProt protein set and synchronization has been improved by covering all UniParc sequences. An advanced search function allows the creation of a wide array of custom-made datasets for download and further analysis. A large amount of information and cross-links to more specialized databases are intended to make MobiDB the central resource for the scientific community working on protein intrinsic disorder and mobility