Accurate Chemical Reaction Modeling on Noisy Intermediate-Scale Quantum Computers Using a Noise-Resilient Wavefunction Ansatz

Quantum computing is of great potential for chemical system simulations. In this study, we propose an efficient protocol of quantum computer based simulation of chemical systems which enables accurate chemical reaction modeling on noisy intermediate-scale quantum (NISQ) devices. In this protocol, we combine an correlation energy-based active orbital selection, an effective Hamiltonian from the driven similarity renormalization group (DSRG) method, and a noise-resilient wavefunction ansatz. Such a combination gives a quantum resource-efficient way to accurately simulate chemical systems. The power of this protocol is demonstrated by numerical results for systems with up to tens of atoms. Modeling of a Diels-Alder (DA) reaction is also performed on a cloud-based superconducting quantum computer. These results represent an important step forward in realizing quantum utility in the NISQ era

Mapping topology-disorder phase diagram with a quantum simulator

We explore the topology-disorder phase diagram by simulating one-dimensional Su-Schrieffer-Heeger (SSH) model with quasiperiodic disorder using a programmable superconducting simulator. We experimentally map out and identify various trivial and topological phases with extended and localized bulk states. We find that in the topological phase the bulk states can be critically localized without mobility edge or contain both critically and completely localized states. In addition, there exist trivial and topological intermediate phases with mobility edge and coexistence of extended and completely localized states. The presence of the surprisingly rich phases in the simple SSH model with quasiperiodic disorder sheds new light on the investigation of the topological and localization phenomena in condensed-matter physics.Comment: 5 pages, 4 figure

Speech Emotion Recognition Based on Attention MCNN Combined With Gender Information

Emotion recognition is susceptible to interference such as feature redundancy and speaker gender differences, resulting in low recognition accuracy. This paper proposes a speech emotion recognition (SER) method based on attention mixed convolutional neural network (MCNN) combined with gender information, including two stages of gender recognition and emotion recognition. (1) Using MCNN to identify gender and classify speech samples into male and female. (2) According to the output of the first stage classification, a gender-specific emotion recognition model is established by introducing coordinated attention and a series of gated recurrent network units connecting the attention mechanism (A-GRUs) to achieve emotion recognition results of different genders. The inputs of both stages are dynamic 3D MFCC features generated from the original speech database. The proposed method achieves 95.02% and 86.34% accuracy on EMO-DB and RAVDESS datasets, respectively. The experimental results show that the proposed SER system combined with gender information significantly improves the recognition performance

MgB2 thin films grown on graphene/Ni–Mo alloy system

200 nm Ni film is coated on 25 mu m thick Mo foil, and graphene is grown on the Ni-Mo system by CVD method. After the annealing process of CVD, the Ni/Mo bilayer transforms into Ni-Mo alloy, then we have successfully fabricated MgB2 films on graphene/Ni-Mo alloy system via the hybrid physical-chemical vapor deposition (HPCVD) technique. The transition temperature T-c onset is 38.25 K with a corresponding transition width of 0.75 K. The average thickness of MgB2 films is 200 nm (25% concentration B2H6). The critical current density derives from the magnetization measurement at 5 K is, j(c) (5 K, 0 T) = 9.6 +/- 106 A/cm(2). We can easily deposite MgB2 on graphene/Ni-Mo alloy system with a lower B2H6 concentration and less gas flow, which lays a good foundation for depositing MgB2 thick films. The graphene in this system is multilayer and with defects, it may act like an intermediary film for the growth of MgB2, or a carbon-doping source. (C) 2015 Elsevier B.V. All rights reserved.National Basic Research Program of China [2011CBA00104, 2012CB932703, 2013CB921901]; National Natural Science Foundation of China [61125402, 51172004, 11074006, 11174010]; Fund for Fostering Talents in Basic Science of the National Natural Science Foundation of China [J0630311]; Research Fund for the Doctoral Program of Higher Education [20120001110065]SCI(E)[email protected]