Controllable, coherent many-body systems can provide insights into the fundamental properties of quantum matter, enable the realization of new quantum phases and could ultimately lead to computational systems that outperform existing computers based on classical approaches. Here we demonstrate a method for creating controlled many-body quantum matter that combines deterministically prepared, reconfigurable arrays of individually trapped cold atoms with strong, coherent interactions enabled by excitation to Rydberg states. We realize a programmable Ising-type quantum spin model with tunable interactions and system sizes of up to 51 qubits. Within this model, we observe phase transitions into spatially ordered states that break various discrete symmetries, verify the high-fidelity preparation of these states and investigate the dynamics across the phase transition in large arrays of atoms. In particular, we observe robust many-body dynamics corresponding to persistent oscillations of the order after a rapid quantum quench that results from a sudden transition across the phase boundary. Our method provides a way of exploring many-body phenomena on a programmable quantum simulator and could enable realizations of new quantum algorithms.

Understanding quantum dynamics away from equilibrium is an outstanding challenge in the modern physical sciences. It is well known that out-of-equilibrium systems can display a rich array of phenomena, ranging from self-organized synchronization to dynamical phase transitions1,2. More recently, advances in the controlled manipulation of isolated many-body systems have enabled detailed studies of non-equilibrium phases in strongly interacting quantum matter3-6. As a particularly striking example, the interplay of periodic driving, disorder, and strong interactions has recently been predicted to result in exotic "time-crystalline" phases7, which spontaneously break the discrete time-translation symmetry of the underlying drive8-11. Here, we report the experimental observation of such discrete time-crystalline order in a driven, disordered †

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.Machine learning based on neural networks has recently provided significant advances for many practical applications 1 . In physics, one natural application involves the study of quantum many-body systems, where the extreme complexity of many-body states often makes theoretical analysis intractable. This has led to a number of recent works using machine learning to study properties of quantum systems 2-7 , using physical concepts to interpret machine learning 8,9 , or using quantum computers to enhance conventional machine learning tasks 10-13 .In this work, motivated by the progress towards realizing quantum information processors 14-17 , we bridge these approaches by proposing a quantum circuit model inspired by machine learning and illustrating its success for two important classes of quantum many-body problems. The first class of problems we consider is quantum phase recognition (QPR), which asks whether a given input quantum state ρ in belongs to a particular quantum phase of matter. Critically, in contrast to many existing schemes based on tensor network descriptions 18-20 , we assume ρ in is prepared in a physical system without direct access to its classical description. The second class, quantum error correction (QEC) optimization, asks for an optimal QEC code for a given, a priori unknown error model such as dephasing or potentially correlated depolarization in realistic experimental settings.The highly complex and intrinsically quantum nature of these problems makes them particularly difficult to solve using existing classical and quantum machine learning techniques. While conventional machine learning with large-scale neural networks can successfully solve analogous classical problems such as image recognition or improving classical error correction 1 , the exponentially large many-body...

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Nuclear magnetic resonance spectroscopy is a powerful tool for the structural analysis of organic compounds and biomolecules but typically requires macroscopic sample quantities. We use a sensor, which consists of two quantum bits corresponding to an electronic spin and an ancillary nuclear spin, to demonstrate room temperature magnetic resonance detection and spectroscopy of multiple nuclear species within individual ubiquitin proteins attached to the diamond surface. Using quantum logic to improve readout fidelity and a surface-treatment technique to extend the spin coherence time of shallow nitrogen-vacancy centers, we demonstrate magnetic field sensitivity sufficient to detect individual proton spins within 1 second of integration. This gain in sensitivity enables high-confidence detection of individual proteins and allows us to observe spectral features that reveal information about their chemical composition.

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A logarithmic signature Some one-dimensional disordered interacting quantum systems have been theoretically predicted to display a property termed many-body localization (MBL), where the system retains the memory of its initial state and fails to thermalize. However, proving experimentally that something does not occur is tricky. Instead, physicists have proposed monitoring the entanglement entropy of the system, which should grow logarithmically with evolution time in an MBL system. Lukin et al. observed this characteristic logarithmic trend in a disordered chain of interacting atoms of rubidium-87. This method should be generalizable to other experimental platforms and higher dimensions. Science , this issue p. 256

Quantum entanglement involving coherent superpositions of macroscopically distinct states is among the most striking features of quantum theory, but its realization is challenging, since such states are extremely fragile. Using a programmable quantum simulator based on neutral atom arrays with interactions mediated by Rydberg states, we demonstrate the deterministic generation of "Schrödinger cat" states of the Greenberger-Horne-Zeilinger (GHZ) type with up to 20 qubits. Our approach is based on engineering the energy spectrum and using optimal control of the many-body system. We further demonstrate entanglement manipulation by using GHZ states to distribute entanglement to distant sites in the array, establishing important ingredients for quantum information processing and quantum metrology. arXiv:1905.05721v2 [quant-ph]