Time-Bin-Encoded Boson Sampling with a Single-Photon Device.

Boson sampling is a problem strongly believed to be intractable for classical computers, but can be naturally solved on a specialized photonic quantum simulator. Here, we implement the first time-bin-encoded boson sampling using a highly indistinguishable (∼94%) single-photon source based on a single quantum-dot-micropillar device. The protocol requires only one single-photon source, two detectors, and a loop-based interferometer for an arbitrary number of photons. The single-photon pulse train is time-bin encoded and deterministically injected into an electrically programmable multimode network. The observed three- and four-photon boson sampling rates are 18.8 and 0.2 Hz, respectively, which are more than 100 times faster than previous experiments based on parametric down-conversion.

Experimental Ten-Photon Entanglement.

We report the first experimental demonstration of quantum entanglement among ten spatially separated single photons. A near-optimal entangled photon-pair source was developed with simultaneously a source brightness of ∼12 MHz/W, a collection efficiency of ∼70%, and an indistinguishability of ∼91% between independent photons, which was used for a step-by-step engineering of multiphoton entanglement. Under a pump power of 0.57 W, the ten-photon count rate was increased by about 2 orders of magnitude compared to previous experiments, while maintaining a state fidelity sufficiently high for proving the genuine ten-particle entanglement. Our work created a state-of-the-art platform for multiphoton experiments, and enabled technologies for challenging optical quantum information tasks, such as the realization of Shor's error correction code and high-efficiency scattershot boson sampling.

Entanglement-based machine learning on a quantum computer.

Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. A challenge is that machine learning with the rapidly growing "big data" could become intractable for classical computers. Recently, quantum machine learning algorithms [Lloyd, Mohseni, and Rebentrost, arXiv.1307.0411] were proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of two-, four-, and eight-dimensional vectors to different clusters using a small-scale photonic quantum computer, which are then used to implement supervised and unsupervised machine learning. The results demonstrate the working principle of using quantum computers to manipulate and classify high-dimensional vectors, the core mathematical routine in machine learning. The method can, in principle, be scaled to larger numbers of qubits, and may provide a new route to accelerate machine learning.

Experimental quantum computing to solve systems of linear equations.

Solving linear systems of equations is ubiquitous in all areas of science and engineering. With rapidly growing data sets, such a task can be intractable for classical computers, as the best known classical algorithms require a time proportional to the number of variables N. A recently proposed quantum algorithm shows that quantum computers could solve linear systems in a time scale of order log(N), giving an exponential speedup over classical computers. Here we realize the simplest instance of this algorithm, solving 2×2 linear equations for various input vectors on a quantum computer. We use four quantum bits and four controlled logic gates to implement every subroutine required, demonstrating the working principle of this algorithm.