Linear optical quantum computation with frequency-comb qubits and passive devices

We propose a linear optical quantum computation scheme using time-frequency degree of freedom. In this scheme, a qubit is encoded in single-photon frequency combs, and manipulation of the qubits is performed using time-resolving detectors, beam splitters, and optical interleavers. This scheme does not require active devices such as high-speed switches and electro-optic modulators and is robust against temporal and spectral errors, which are mainly caused by the detectors' finite resolution. We show that current technologies almost meet the requirements for fault-tolerant quantum computation.Comment: 6+3 pages, 3 figure

3D-XY critical fluctuations of the thermal expansivity in detwinned YBa2Cu3O7-d single crystals near optimal doping

The strong coupling of superconductivity to the orthorhombic distortion in YBa2Cu3O7-d makes possible an analysis of the superconducting fluctuations without the necessity of subtracting any background. The present high-resolution capacitance dilatometry data unambiguously demonstrate the existence of critical, instead of Gaussian, fluctuations over a wide temperature region (+/- 10 K) around Tc. The values of the amplitude ratio A+/A-=0.9-1.1 and the leading scaling exponent |alpha|<0.018, determined via a least-squares fit of the data, are consistent with the 3D-XY universality class. Small deviations from pure 3D-XY behavior are discussed.Comment: 11 pages including three figure

Electrochemical Glycosylation as an Enabling Tool for the Stereoselective Synthesis of Cyclic Oligosaccharides

Electrochemical glycosylation of a linear oligosaccharide with a protecting-group-free primary hydroxyl group afforded cyclic oligo-saccharides, up to hexasaccharides, in high yields. Precursors of the cyclic oligosaccharides were prepared by automated electro-chemical assembly-a method for the automated electrochemical solution-phase synthesis of oligosaccharides. We demonstrated that electrochemical glycosylation is useful not only for intermolecular glycosylation but also for intramolecular glycosylation to synthesize cyclic oligosaccharides

On R\'{e}nyi Differential Privacy in Statistics-Based Synthetic Data Generation

Privacy protection with synthetic data generation often uses differentially private statistics and model parameters to quantitatively express theoretical security. However, these methods do not take into account privacy protection due to the randomness of data generation. In this paper, we theoretically evaluate R\'{e}nyi differential privacy of the randomness in data generation of a synthetic data generation method that uses the mean vector and the covariance matrix of an original dataset. Specifically, for a fixed $\alpha > 1$, we show the condition of $\varepsilon$ such that the synthetic data generation satisfies $(\alpha, \varepsilon)$-R\'{e}nyi differential privacy under a bounded neighboring condition and an unbounded neighboring condition, respectively. In particular, under the unbounded condition, when the size of the original dataset and synthetic datase is 10 million, the mechanism satisfies $(4, 0.576)$-R\'{e}nyi differential privacy. We also show that when we translate it into the traditional $(\varepsilon, \delta)$-differential privacy, the mechanism satisfies $(4.00, 10^{-10})$-differential privacy.Comment: 18 pages, 3 figure