Proposal for optical parity state re-encoder

We propose a re-encoder to generate a refreshed parity encoded state from an existing parity encoded state. This is the simplest case of the scheme by Gilchrist et al. (Phys. Rev. A 75, 052328). We show that it is possible to demonstrate with existing technology parity encoded quantum gates and teleportation.Comment: 8 pages, 4 figure

Probabilistic Quantum Logic Operations Using Polarizing Beam Splitters

It has previously been shown that probabilistic quantum logic operations can be performed using linear optical elements, additional photons (ancilla), and post-selection based on the output of single-photon detectors. Here we describe the operation of several quantum logic operations of an elementary nature, including a quantum parity check and a quantum encoder, and we show how they can be combined to implement a controlled-NOT (CNOT) gate. All of these gates can be constructed using polarizing beam splitters that completely transmit one state of polarization and totally reflect the orthogonal state of polarization, which allows a simple explanation of each operation. We also describe a polarizing beam splitter implementation of a CNOT gate that is closely analogous to the quantum teleportation technique previously suggested by Gottesman and Chuang [Nature 402, p.390 (1999)]. Finally, our approach has the interesting feature that it makes practical use of a quantum-eraser technique.Comment: 9 pages, RevTex; Submitted to Phys. Rev. A; additional references inlcude

Loss Tolerant Optical Qubits

We present a linear optics quantum computation scheme that employs a new encoding approach that incrementally adds qubits and is tolerant to photon loss errors. The scheme employs a circuit model but uses techniques from cluster state computation and achieves comparable resource usage. To illustrate our techniques we describe a quantum memory which is fault tolerant to photon loss

Loss-tolerant operations in parity-code linear optics quantum computing

A heavy focus for optical quantum computing is the introduction of error-correction, and the minimisation of resource requirements. We detail a complete encoding and manipulation scheme designed for linear optics quantum computing, incorporating scalable operations and loss-tolerant architecture.Comment: 8 pages, 6 figure

Protecting Quantum Information with Entanglement and Noisy Optical Modes

We incorporate active and passive quantum error-correcting techniques to protect a set of optical information modes of a continuous-variable quantum information system. Our method uses ancilla modes, entangled modes, and gauge modes (modes in a mixed state) to help correct errors on a set of information modes. A linear-optical encoding circuit consisting of offline squeezers, passive optical devices, feedforward control, conditional modulation, and homodyne measurements performs the encoding. The result is that we extend the entanglement-assisted operator stabilizer formalism for discrete variables to continuous-variable quantum information processing.Comment: 7 pages, 1 figur

High-Fidelity Z-Measurement Error Correction of Optical Qubits

We demonstrate a quantum error correction scheme that protects against accidental measurement, using an encoding where the logical state of a single qubit is encoded into two physical qubits using a non-deterministic photonic CNOT gate. For the single qubit input states |0>, |1>, |0>+|1>, |0>-|1>, |0>+i|1>, and |0>-i|1> our encoder produces the appropriate 2-qubit encoded state with an average fidelity of 0.88(3) and the single qubit decoded states have an average fidelity of 0.93(5) with the original state. We are able to decode the 2-qubit state (up to a bit flip) by performing a measurement on one of the qubits in the logical basis; we find that the 64 1-qubit decoded states arising from 16 real and imaginary single qubit superposition inputs have an average fidelity of 0.96(3).Comment: 4 pages, 4 figures, comments welcom

Geometry meets semantics for semi-supervised monocular depth estimation

Depth estimation from a single image represents a very exciting challenge in computer vision. While other image-based depth sensing techniques leverage on the geometry between different viewpoints (e.g., stereo or structure from motion), the lack of these cues within a single image renders ill-posed the monocular depth estimation task. For inference, state-of-the-art encoder-decoder architectures for monocular depth estimation rely on effective feature representations learned at training time. For unsupervised training of these models, geometry has been effectively exploited by suitable images warping losses computed from views acquired by a stereo rig or a moving camera. In this paper, we make a further step forward showing that learning semantic information from images enables to improve effectively monocular depth estimation as well. In particular, by leveraging on semantically labeled images together with unsupervised signals gained by geometry through an image warping loss, we propose a deep learning approach aimed at joint semantic segmentation and depth estimation. Our overall learning framework is semi-supervised, as we deploy groundtruth data only in the semantic domain. At training time, our network learns a common feature representation for both tasks and a novel cross-task loss function is proposed. The experimental findings show how, jointly tackling depth prediction and semantic segmentation, allows to improve depth estimation accuracy. In particular, on the KITTI dataset our network outperforms state-of-the-art methods for monocular depth estimation.Comment: 16 pages, Accepted to ACCV 201