Experimental quantum adversarial learning with programmable superconducting qubits.

Quantum computing promises to enhance machine learning and artificial intelligence. However, recent theoretical works show that, similar to traditional classifiers based on deep classical neural networks, quantum classifiers would suffer from adversarial perturbations as well. Here we report an experimental demonstration of quantum adversarial learning with programmable superconducting qubits. We train quantum classifiers, which are built on variational quantum circuits consisting of ten transmon qubits featuring average lifetimes of 150 µs, and average fidelities of simultaneous single- and two-qubit gates above 99.94% and 99.4%, respectively, with both real-life images (for example, medical magnetic resonance imaging scans) and quantum data. We demonstrate that these well-trained classifiers (with testing accuracy up to 99%) can be practically deceived by small adversarial perturbations, whereas an adversarial training process would substantially enhance their robustness to such perturbations.

The urgent need for integrated science to fight COVID-19 pandemic and beyond.

The COVID-19 pandemic has become the leading societal concern. The pandemic has shown that the public health concern is not only a medical problem, but also affects society as a whole; so, it has also become the leading scientific concern. We discuss in this treatise the importance of bringing the world's scientists together to find effective solutions for controlling the pandemic. By applying novel research frameworks, interdisciplinary collaboration promises to manage the pandemic's consequences and prevent recurrences of similar pandemics.

Quantum machine learning.

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

Quantum Simulation of Helium Hydride Cation in a Solid-State Spin Register.

Ab initio computation of molecular properties is one of the most promising applications of quantum computing. While this problem is widely believed to be intractable for classical computers, efficient quantum algorithms exist which have the potential to vastly accelerate research throughput in fields ranging from material science to drug discovery. Using a solid-state quantum register realized in a nitrogen-vacancy (NV) defect in diamond, we compute the bond dissociation curve of the minimal basis helium hydride cation, HeH(+). Moreover, we report an energy uncertainty (given our model basis) of the order of 10(-14) hartree, which is 10 orders of magnitude below the desired chemical precision. As NV centers in diamond provide a robust and straightforward platform for quantum information processing, our work provides an important step toward a fully scalable solid-state implementation of a quantum chemistry simulator.

High-fidelity spin entanglement using optimal control.

Precise control of quantum systems is of fundamental importance in quantum information processing, quantum metrology and high-resolution spectroscopy. When scaling up quantum registers, several challenges arise: individual addressing of qubits while suppressing cross-talk, entangling distant nodes and decoupling unwanted interactions. Here we experimentally demonstrate optimal control of a prototype spin qubit system consisting of two proximal nitrogen-vacancy centres in diamond. Using engineered microwave pulses, we demonstrate single electron spin operations with a fidelity F≈0.99. With additional dynamical decoupling techniques, we further realize high-quality, on-demand entangled states between two electron spins with F>0.82, mostly limited by the coherence time and imperfect initialization. Crosstalk in a crowded spectrum and unwanted dipolar couplings are simultaneously eliminated to a high extent. Finally, by high-fidelity entanglement swapping to nuclear spin quantum memory, we demonstrate nuclear spin entanglement over a length scale of 25 nm. This experiment underlines the importance of optimal control for scalable room temperature spin-based quantum information devices.

Quantum transport enhancement by time-reversal symmetry breaking.

Quantum mechanics still provides new unexpected effects when considering the transport of energy and information. Models of continuous time quantum walks, which implicitly use time-reversal symmetric Hamiltonians, have been intensely used to investigate the effectiveness of transport. Here we show how breaking time-reversal symmetry of the unitary dynamics in this model can enable directional control, enhancement, and suppression of quantum transport. Examples ranging from exciton transport to complex networks are presented. This opens new prospects for more efficient methods to transport energy and information.