A performance characterization of quantum generative models

Quantum generative modeling is a growing area of interest for industry-relevant applications. With the field still in its infancy, there are many competing techniques. This work is an attempt to systematically compare a broad range of these techniques to guide quantum computing practitioners when deciding which models and techniques to use in their applications. We compare fundamentally different architectural ansatzes of parametric quantum circuits used for quantum generative modeling: 1. A continuous architecture, which produces continuous-valued data samples, and 2. a discrete architecture, which samples on a discrete grid. We compare the performance of different data transformations: normalization by the min-max transform or by the probability integral transform. We learn the underlying probability distribution of the data sets via two popular training methods: 1. quantum circuit Born machines (QCBM), and 2. quantum generative adversarial networks (QGAN). We study their performance and trade-offs as the number of model parameters increases, with the baseline of similarly trained classical neural networks. The study is performed on six low-dimensional synthetic and two real financial data sets. Our two key findings are that: 1. For all data sets, our quantum models require similar or fewer parameters than their classical counterparts. In the extreme case, the quantum models require two of orders of magnitude less parameters. 2. We empirically find that a variant of the discrete architecture, which learns the copula of the probability distribution, outperforms all other methods

Quantum Computing Techniques for Multi-Knapsack Problems

Optimization problems are ubiquitous in various industrial settings, and multi-knapsack optimization is one recurrent task faced daily by several industries. The advent of quantum computing has opened a new paradigm for computationally intensive tasks, with promises of delivering better and faster solutions for specific classes of problems. This work presents a comprehensive study of quantum computing approaches for multi-knapsack problems, by investigating some of the most prominent and state-of-the-art quantum algorithms using different quantum software and hardware tools. The performance of the quantum approaches is compared for varying hyperparameters. We consider several gate-based quantum algorithms, such as QAOA and VQE, as well as quantum annealing, and present an exhaustive study of the solutions and the estimation of runtimes. Additionally, we analyze the impact of warm-starting QAOA to understand the reasons for the better performance of this approach. We discuss the implications of our results in view of utilizing quantum optimization for industrial applications in the future. In addition to the high demand for better quantum hardware, our results also emphasize the necessity of more and better quantum optimization algorithms, especially for multi-knapsack problems.Comment: 20 page

Random mutagenesis of the prokaryotic peptide transporter YdgR identifies potential periplasmic gating residues

The peptide transporter (PTR) family represents a group of proton-coupled secondary transporters responsible for bulk uptake of amino acids in the form of di- and tripeptides, an essential process employed across species ranging from bacteria to humans. To identify amino acids critical for peptide transport in a prokaryotic PTR member, we have screened a library of mutants of the Escherichia coli peptide transporter YdgR using a high-throughput substrate uptake assay.Wehave identified 35 single point mutations that result in a full or partial loss of transport activity. Additional analysis, including homology modeling based on the crystal structure of the Shewanella oneidensis peptide transporter PepT so, identifies Glu 56 and Arg 305 as potential periplasmic gating residues. In addition to providing new insights into transport by members of the PTR family, these mutants provide valuable tools for further study of the mechanism of peptide transport

Optimized Encapsulation of the FLAP/PGES-1 Inhibitor BRP-187 in PVA-Stabilized PLGA Nanoparticles Using Microfluidics

The dual inhibitor of the 5-lipoxygenase-activating protein (FLAP) and the microsomal prostaglandin E2 synthase-1 (mPGES-1), named BRP-187, represents a promising drug candidate due to its improved anti-inflammatory efficacy along with potentially reduced side effects in comparison to non-steroidal anti-inflammatory drugs (NSAIDs). However, BRP-187 is an acidic lipophilic drug and reveals only poor water solubility along with a strong tendency for plasma protein binding. Therefore, encapsulation in polymeric nanoparticles is a promising approach to enable its therapeutic use. With the aim to optimize the encapsulation of BRP-187 into poly(lactic-co-glycolic acid) (PLGA) nanoparticles, a single-phase herringbone microfluidic mixer was used for the particle preparation. Various formulation parameters, such as total flow rates, flow rate ratio, the concentration of the poly(vinyl alcohol) (PVA) as a surfactant, initial polymer concentration, as well as presence of a co-solvent on the final particle size distribution and drug loading, were screened for best particle characteristics and highest drug loading capacities. While the size of the particles remained in the targeted region between 121 and 259 nm with low polydispersities (0.05 to 0.2), large differences were found in the BRP-187 loading capacities (LC = 0.5 to 7.29%) and drug crystal formation during the various formulations

PEG–Lipid–PLGA Hybrid Particles for Targeted Delivery of Anti-Inflammatory Drugs

Hybrid nanoparticles (HNPs) were designed by combining a PLGA core with a lipid shell that incorporated PEG–Lipid conjugates with various functionalities (-RGD, -cRGD, -NH2, and -COOH) to create targeted drug delivery systems. Loaded with a neutral lipid orange dye, the HNPs were extensively characterized using various techniques and investigated for their uptake in human monocyte-derived macrophages (MDMs) using FC and CLSM. Moreover, the best-performing HNPs (i.e., HNP-COOH and HNP-RGD as well as HNP-RGD/COOH mixed) were loaded with the anti-inflammatory drug BRP-201 and prepared in two size ranges (dH ~140 nm and dH ~250 nm). The HNPs were examined further for their stability, degradation, MDM uptake, and drug delivery efficiency by studying the inhibition of 5-lipoxygenase (5-LOX) product formation, whereby HNP-COOH and HNP-RGD both exhibited superior uptake, and the HNP-COOH/RGD (2:1) displayed the highest inhibition