Exploring out-of-equilibrium quantum magnetism and thermalization in a spin-3 many-body dipolar lattice system

Understanding quantum thermalization through entanglement build-up in isolated quantum systems addresses fundamental questions on how unitary dynamics connects to statistical physics. Here, we study the spin dynamics and approach towards local thermal equilibrium of a macroscopic ensemble of S = 3 spins prepared in a pure coherent spin state, tilted compared to the magnetic field, under the effect of magnetic dipole-dipole interactions. The experiment uses a unit filled array of 104 chromium atoms in a three dimensional optical lattice, realizing the spin-3 XXZ Heisenberg model. The buildup of quantum correlation during the dynamics, especially as the angle approaches pi/2, is supported by comparison with an improved numerical quantum phase-space method and further confirmed by the observation that our isolated system thermalizes under its own dynamics, reaching a steady state consistent with the one extracted from a thermal ensemble with a temperature dictated from the system's energy. This indicates a scenario of quantum thermalization which is tied to the growth of entanglement entropy. Although direct experimental measurements of the Renyi entropy in our macroscopic system are unfeasible, the excellent agreement with the theory, which can compute this entropy, does indicate entanglement build-up.Comment: 12 figure

Quantum Convolutional Neural Networks

We introduce and analyze a novel quantum machine learning model motivated by convolutional neural networks. Our quantum convolutional neural network (QCNN) makes use of only $O(\log(N))$ variational parameters for input sizes of $N$ qubits, allowing for its efficient training and implementation on realistic, near-term quantum devices. The QCNN architecture combines the multi-scale entanglement renormalization ansatz and quantum error correction. We explicitly illustrate its potential with two examples. First, QCNN is used to accurately recognize quantum states associated with 1D symmetry-protected topological phases. We numerically demonstrate that a QCNN trained on a small set of exactly solvable points can reproduce the phase diagram over the entire parameter regime and also provide an exact, analytical QCNN solution. As a second application, we utilize QCNNs to devise a quantum error correction scheme optimized for a given error model. We provide a generic framework to simultaneously optimize both encoding and decoding procedures and find that the resultant scheme significantly outperforms known quantum codes of comparable complexity. Finally, potential experimental realization and generalizations of QCNNs are discussed.Comment: 12 pages, 11 figures. v2: New application to optimizing quantum error correction codes, added sample complexity analysis, more details for experimental realizations, and other minor revision

Experimental Quantum Hamiltonian Learning

Efficiently characterising quantum systems, verifying operations of quantum devices and validating underpinning physical models, are central challenges for the development of quantum technologies and for our continued understanding of foundational physics. Machine-learning enhanced by quantum simulators has been proposed as a route to improve the computational cost of performing these studies. Here we interface two different quantum systems through a classical channel - a silicon-photonics quantum simulator and an electron spin in a diamond nitrogen-vacancy centre - and use the former to learn the latter's Hamiltonian via Bayesian inference. We learn the salient Hamiltonian parameter with an uncertainty of approximately $10^{-5}$. Furthermore, an observed saturation in the learning algorithm suggests deficiencies in the underlying Hamiltonian model, which we exploit to further improve the model itself. We go on to implement an interactive version of the protocol and experimentally show its ability to characterise the operation of the quantum photonic device. This work demonstrates powerful new quantum-enhanced techniques for investigating foundational physical models and characterising quantum technologies

Past answers to present concerns: the relevance of the premodern past for 21st century policy planners; comments on the state of the field

How is history relevant to the present, or indeed the future? Governments around the world have used history to inform planning and decision-making in various fields for years, but more recently it has taken on a renewed importance as governments grapple with increasingly complex challenges arising from the impacts of climatic change. Yet identifying ?lessons from the past? is not straightforward. Especially in the case of big questions about historical structures and social processes, establishing precise causal relationships is complex and interpretive, making consensus difficult among specialists. A second major challenge arises over the uses of history. Historical precedent can and does play a role in some contexts in helping formulate new strategies for addressing local environmental challenges. At the national level policy-makers and politicians often look to the past for inspiration, guidance, or justification. In both respects, the cases and examples chosen are often highly selective and tend to align with pre-existing assumptions. This article briefly reviews these challenges within the context of climate change and associated environmental and sustainability issues, comments on recent work in the field, and suggests some ways forward for historians.1 Introduction: The Challenge 2 Nuancing the Past: Continuity, Rupture, Agency, and Belief 3 Ways Forward I: Expert Elicitation and Qualitative–Quantitative Data Integration 4 Ways Forward II: Case Studies of Policy Influence 4.1 Case Study 1: The Eastern Mediterranean 4.2 Case Study 2: The North Atlantic Islands 5 Ongoing Challenges 6 Conclusio

Comparison of gene expression profiles in core biopsies and corresponding surgical breast cancer samples

INTRODUCTION: Gene expression profiling has been successfully used to classify breast cancer into clinically distinct subtypes, and to predict the risk of recurrence and treatment response. The aim of this study was to investigate whether the gene expression profile (GEP) detected in a core biopsy (CB) is representative for the entire tumor, since CB is an important tool in breast cancer diagnosis. Moreover, we investigated whether performing CBs prior to the surgical excision could influence the GEP of the respective tumor. METHODS: We quantified the RNA expression of 60 relevant genes by quantitative real-time PCR in paired CBs and surgical specimens from 22 untreated primary breast cancer patients. Subsequently, expression data were compared with independent GEPs obtained from tumors of 317 patients without preceding CB. RESULTS: In 82% of the cases the GEP detected in the CB correlated very well with the corresponding profile in the surgical sample (r(s )≥ 0.95, p < 0.001). Gene-by-gene analysis revealed four genes significantly elevated in the surgical sample compared to the CB; these comprised genes mainly involved in inflammation and the wound repair process as well as in tumor invasion and metastasis. CONCLUSION: A GEP detected in a CB are representative for the entire tumor and is, therefore, of clinical relevance. The observed alterations of individual genes after performance of CB deserve attention since they might impact the clinical interpretation with respect to prognosis and therapy prediction of the GEP as detected in the surgical specimen following CB performance

Holocene hydro-climatic variability in the Mediterranean: A synthetic multi-proxy reconstruction

Here we identify and analyze proxy data interpreted to reflect hydro-climatic variability over the last 10,000 years from the Mediterranean region to (1) outline millennial and multi-centennial-scale trends and (2) identify regional patterns of hydro-climatic variability. A total of 47 lake, cave, and marine records were transformed to z-scores to allow direct comparisons between sites, put on a common time scale, and binned into 200-year time slices. Six different regions were identified based on numerical and spatial analyzes of z-scores: S Iberia and Maghreb, N Iberia, Italy, the Balkans, Turkey, and the Levant, and the overall hydro-climate history of each region was reconstructed. N Iberia is largely decoupled from the five other regions throughout the Holocene. Wetter conditions occur in the five other regions between 8500 and 6100 yr BP. After 6000 yr BP, climate oscillated until around 3000 ± 300 yr BP, which seems to have been the overall driest period in the eastern Mediterranean and North Africa. In contrast, Italy and N Iberia seem to have remained wetter during this period. In addition, non-metric multidimensional scaling (nMDS) was applied to 18 long, continuous climate z-score records that span the majority of the Holocene. nMDS axes 1 and 2 illustrate the main trends in the z-score data. The first axis captures a long-term development of drier condition in the Mediterranean from 7900 to 3700 yr BP. Rapid shifts occur in nMDS axis 2 at 6700–6300 BP, 4500–4300 BP, and 3500–3300 BP indicating centennial-scale climate change. Our synthesis highlights a dominant south/east versus north/west Mediterranean hydro-climate dipole throughout the Holocene and therefore confirms that there was no single climate trajectory characterizing the whole Mediterranean basin during the last 10 millennia