Although quantum computers promise significant advantages, the complexity of quantum algorithms remains a major technological obstacle. We have developed and demonstrated an architecture-independent technique that simplifies adding control qubits to arbitrary quantum operations—a requirement in many quantum algorithms, simulations and metrology. The technique, which is independent of how the operation is done, does not require knowledge of what the operation is, and largely separates the problems of how to implement a quantum operation in the laboratory and how to add a control. Here, we demonstrate an entanglement-based version in a photonic system, realizing a range of different two-qubit gates with high fidelity.

Until recently, quantum photonic architecture comprised of large-scale (bulk) optical elements, leading to severe limitations in miniaturization, scalability and stability. The development of the first integrated quantum optical circuitry removes this bottleneck and allows realization of quantum optical schemes whose greatly increased capacity for circuit complexity is crucial to the progress of experimental quantum information science and the development of practical quantum technologies.Integrated quantum photonic circuits within Silica-on-Silicon waveguide chips were simulated, designed and tested. Hundreds of devices have been fabricated with the core components found to be robust and highly repeatable. Amongst these demonstrations, all the basic components required for quantum information applications are shown. The first integrated quantum metrology experiments are demonstrated by beating the standard quantum limit with twoand four-photon entangled states while providing the first re-configurable integrated quantum circuit capable of adaptively controlling levels of non-classical interference of photons. The tested integrated devices show no limitations to obtain high quality performances. It is reported near-unity visibility of two-photon non-classical interference and a Controlled-NOT gate that could in principle work in the fault tolerant regime.It is demonstrated the realization of a compiled version of Shors quantum factoring algorithm on an integrated waveguide chip. This demonstration serves as an illustration to the importance of using integrated optics for quantum optical experiments.

A quantum algorithm solves computational tasks using fewer physical resources than the best-known classical algorithm. Of most interest are those for which an exponential reduction is achieved. The key example is the phase estimation algorithm, which provides the quantum speedup in Shor's factoring algorithm and quantum simulation algorithms. To date, fully quantum experiments of this type have demonstrated only the read-out stage of quantum algorithms, but not the steps in which input data is read in and processed to calculate the final quantum state. Indeed, knowing the answer beforehand was essential. We present a photonic demonstration of a full quantum algorithm-the iterative phase estimation algorithm (IPEA)-without knowing the answer in advance. This result suggests practical applications of the phase estimation algorithm, including quantum simulations and quantum metrology in the near term, and factoring in the long term.M any quantum computations can be roughly broken down into two stages: read-in and processing of the input data; and processing and read-out of the solution. In the first phase, the initial data are read into a quantum register and processed with quantum gates, sometimes multiple times. This produces a quantum state in which the solution is encoded. In the second phase the quantum state may be subjected to further processing followed by measurement, producing a classical data string containing the solution. Even though quantum computers are currently limited to a small number of qubits, there is considerable interest in the small-scale demonstration of quantum algorithms, even if the size of the problems solved means that they remain easily tractable with classical techniques. Such demonstrations remain challenging, even for small numbers of qubits, as they typically require the sequential application of a large number of quantum gates 1 . Note we are making a distinction here between quantum algorithms and direct quantum simulation (Supplementary Section S1).In recent years there have been a number of elegant demonstrations of the read-out phase of Shor's factoring algorithm 2-5 and a quantum chemistry simulation algorithm 6-8 . In these demonstrations, quantum gates have been used to produce the quantum state corresponding to a particular solution of the algorithm. It was then shown that the corresponding solution could be read out with high fidelity from this state. However, in each case, the method for producing the quantum state explicitly required the solution to be already known from a classical calculation. That is, the solution was put into the quantum state by hand, before being read out through further processing and measurement. It is clearly important to go beyond this restriction and demonstrate both stages of a quantum algorithm. Phase estimation algorithmFirst, we briefly review the standard phase estimation algorithm 1 . Given a unitary U and one of its eigenstates |cl that fulfil the equationthe task is to find what the corresponding eigenvalue is-in other words, find th...

Raman spectroscopy is a vital technique being able to detect and identify molecular information with advantages of being fast and non-invasive. This technique also enables numbers of potential applications, including forensic drugs detector, explosive detection, and biomedical analysis. In this work, we investigated the identification performance of a custom-made low-resolution Raman system equipped with machine learning capability to classify various types of materials. Here, a relatively broadband laser diode with center wavelength of 808 nm was used for Raman excitation. An off-axis parabolic mirror with through hole was used in place of a beamspiltter for sample excitation, as well as collection, and collimation of scattered light from long working distance of 50 mm. The signal was filtered and delivered to a cooled spectrometer via an optical fiber for spectra measurements. Raman spectra of test samples were on the range of 100-2000 cm−1 with 7.65 cm−1 data steps. For spectral analysis, a convolutional neural network (CNN) was implemented as classification algorithm with feature extraction from multiple layers together with error-back propagation, which displayed the performance in term of accuracy. It was found that with only three sets of convolution layers up to 96.7% testing performance can be achieved even with low spectral resolution input.