PennyLane is a Python 3 software framework for optimization and machine learning of quantum and hybrid quantumclassical computations. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware.We provide plugins for Strawberry Fields, Rigetti Forest, Qiskit, and ProjectQ, allowing PennyLane optimizations to be run on publicly accessible quantum devices provided by Rigetti and IBM Q. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, and autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.

A quantum computer attains computational advantage when outperforming the best classical computers running the best-known algorithms on well-defined tasks. No photonic machine offering programmability over all its quantum gates has demonstrated quantum computational advantage: previous machines1,2 were largely restricted to static gate sequences. Earlier photonic demonstrations were also vulnerable to spoofing3, in which classical heuristics produce samples, without direct simulation, lying closer to the ideal distribution than do samples from the quantum hardware. Here we report quantum computational advantage using Borealis, a photonic processor offering dynamic programmability on all gates implemented. We carry out Gaussian boson sampling4 (GBS) on 216 squeezed modes entangled with three-dimensional connectivity5, using a time-multiplexed and photon-number-resolving architecture. On average, it would take more than 9,000 years for the best available algorithms and supercomputers to produce, using exact methods, a single sample from the programmed distribution, whereas Borealis requires only 36 μs. This runtime advantage is over 50 million times as extreme as that reported from earlier photonic machines. Ours constitutes a very large GBS experiment, registering events with up to 219 photons and a mean photon number of 125. This work is a critical milestone on the path to a practical quantum computer, validating key technological features of photonics as a platform for this goal.

We introduce a new relativistic astrophysics code, SpECTRE, that combines a discontinuous Galerkin method with a task-based parallelism model. SpECTRE's goal is to achieve more accurate solutions for challenging relativistic astrophysics problems such as core-collapse supernovae and binary neutron star mergers. The robustness of the discontinuous Galerkin method allows for the use of high-resolution shock capturing methods in regions where (relativistic) shocks are found, while exploiting high-order accuracy in smooth regions. A taskbased parallelism model allows efficient use of the largest supercomputers for problems with a heterogeneous workload over disparate spatial and temporal scales. We argue that the locality and algorithmic structure of discontinuous Galerkin methods will exhibit good scalability within a task-based parallelism framework. We demonstrate the code on a wide variety of challenging benchmark problems in (non)-relativistic (magneto)hydrodynamics. We demonstrate the code's scalability including its strong scaling on the NCSA Blue Waters supercomputer up to the machine's full capacity of 22, 380 nodes using 671, 400 threads. variety of astrophysics codes (e.g., Refs. [6,[9][10][11][12][13][14]) have been designed based on these fundamental building blocks.These strategies work well when the computations are reasonably homogeneous or when one seeks good parallelization to only a few thousand cores. As the number of MPI processes increases, so does the cost of communication which, together with non-uniform workload typical of astrophysics problems, limits the maximum number of useful cores that codes can run on. Efficient core utilization becomes non-trivial, often requiring careful optimization by hand to achieve good scalability [15]. Standard finite-volume and finitedifference methods achieve higher order accuracy with increasingly large (overlapping) stencil sizes, and may require additional effort to achieve scalability on massively parallel machines.As one looks ahead to the arrival of exascale computing, it will become increasingly important to focus on developing algorithms that can take full advantage of these very large machines.Discontinuous Galerkin (DG) methods [16-21], together with a task-based parallelization strategy, have the potential to tackle many of these problems. DG methods offer high-order accuracy in smooth regions (although, for stability, increasing the scheme's order requires decreasing the timestep, which restricts the largest usable order in practice), robustness for shocks and other discontinuities, and grid flexibility including a formulation that allows for comparatively straightforward hp-adaptivity and local timestepping. DG methods can be combined with positivity preserving strategies [22][23][24] or "atmosphere treatments" [25] which seek to maintain non-negative values of the pressure and density in challenging regions such as those containing high-speed astrophysical flow. DG methods are also well suited for parallelization: Their formulation in terms of l...

The discovery of GW170817 with gravitational waves (GWs) and electromagnetic (EM) radiation is prompting new questions in strong-gravity astrophysics. Importantly, it remains unknown whether the progenitor of the merger comprised two neutron stars (NSs) or a NS and a black hole (BH). Using new numerical-relativity simulations and incorporating modeling uncertainties, we produce novel GW and EM observables for NS-BH mergers with similar masses. A joint analysis of GW and EM measurements reveals that if GW170817 is a NS-BH merger, ≲40% of the binary parameters consistent with the GW data are compatible with EM observations.