ISOTOPE SHIFT SPECTROSCOPY OF ULTRACOLD STRONTIUM

We describe the design, construction, and performance of a laser system to probe the ultra-narrow (Γ/2π ≈ mHz) clock transition 1S0 → 3P0 in strontium. We present the first reported spectroscopy of this transition in two of the bosonic isotopes, 84Sr and 86Sr. Furthermore, we measure the complete set of isotope shifts between all four stable isotopes on the clock line and the narrow intercombination line 1S0 → 3P1, permitting a King plot analysis of the isotope shifts. Complications arising from the unambiguous determination of a line center in 87Sr 3P1 prevent us from making claims about the King linearity, but we provide a statistical boot- strap analysis of the isotope shifts 88−84Sr and 88−86Sr to compute a field shift ratio F698/F689 = 0.9979, with a 95% confidence interval [0.9952,1.0008]. The intercept term K698 − (F698/F689) K689 is similarly determined to be -2.0 GHz-amu, with a 95% confidence interval [−3.9, −0.3] GHz-amu. Finally, we describe the design of a next-generation apparatus that will enable improvements on the results described here, as well as other studies that involve coherent manipulation of strontium atoms on the clock line

Extremum seeking control of quantum gates

To be useful for quantum computation, gate operations must be maintained at high fidelities over long periods of time. In addition to decoherence, slow drifts in control hardware leads to inaccurate gates, causing the quality of operation of as-built quantum computers to vary over time. Here, we demonstrate a data-driven approach to stabilized control, combining extremum-seeking control (ESC) with direct randomized benchmarking (DRB) to stabilize two-qubit gates under unknown control parameter fluctuations. As a case study, we consider these control strategies in the context of a trapped ion quantum computer using physically-realistic simulation. We then experimentally demonstrate this control strategy on a state-of-the-art, commercial trapped-ion quantum computer.Comment: 5 pages, 6 figure

Generative Quantum Learning of Joint Probability Distribution Functions

Modeling joint probability distributions is an important task in a wide variety of fields. One popular technique for this employs a family of multivariate distributions with uniform marginals called copulas. While the theory of modeling joint distributions via copulas is well understood, it gets practically challenging to accurately model real data with many variables. In this work, we design quantum machine learning algorithms to model copulas. We show that any copula can be naturally mapped to a multipartite maximally entangled state. A variational ansatz we christen as a `qopula' creates arbitrary correlations between variables while maintaining the copula structure starting from a set of Bell pairs for two variables, or GHZ states for multiple variables. As an application, we train a Quantum Generative Adversarial Network (QGAN) and a Quantum Circuit Born Machine (QCBM) using this variational ansatz to generate samples from joint distributions of two variables for historical data from the stock market. We demonstrate our generative learning algorithms on trapped ion quantum computers from IonQ for up to 8 qubits and show that our results outperform those obtained through equivalent classical generative learning. Further, we present theoretical arguments for exponential advantage in our model's expressivity over classical models based on communication and computational complexity arguments.Comment: 19 pages, 11 figures. v2: published versio

A-Kinase Anchoring in Dendritic Cells Is Required for Antigen Presentation

BACKGROUND: Dendritic cells (DC) are the most potent antigen presenting cells (APC) of the immune system. Prostaglandin E(2), cyclic AMP, and protein kinase A (PKA) have all been shown to regulate DC maturation and activity. In other cells, the ability of these molecules to convey their signals has been shown to be dependent on A-kinase anchoring proteins (AKAPs). Here we present evidence for the existence and functional importance of AKAPs in human DC. METHODOLOGY/PRINCIPAL FINDINGS: Using immunofluorescence and/or western analyses we identify AKAP79, AKAP149, AKAP95, AKAP LBC and Ezrin. We also demonstrate by western analysis that expression of AKAP79, AKAP149 and RII are upregulated with DC differentiation and maturation. We establish the functional importance of PKA anchoring in multiple aspects of DC biology using the anchoring inhibitor peptides Ht31 and AKAP-IS. Incubation of protein or peptide antigen loaded DC with Ht31 or AKAP-IS results in a 30-50% decrease in antigen presentation as measured by IFN-gamma production from antigen specific CD4(+) T cells. Incubation of LPS treated DC with Ht31 results in 80% inhibition of TNF-alpha and IL-10 production. Ht31 slightly decreases the expression of CD18 and CD11a and CD11b, slightly increases the basal expression of CD83, dramatically decreases the LPS stimulated expression of CD40, CD80 and CD83, and significantly increases the expression of the chemokine receptor CCR7. CONCLUSIONS: These experiments represent the first evidence for the functional importance of PKA anchoring in multiple aspects of DC biology