A Peptide in a Pill - Oral Semaglutide in the Management of Type 2 Diabetes.

T2DM (type 2 diabetes mellitus) is a chronic and progressive illness with high morbidity and death rates. Oral semaglutide (Rybelsus®) is a combination of semaglutide, a glucagon-like peptide-1 receptor agonist (GLP-1 RA), and sodium N- (8- [2-hydroxybenzoyl] amino) caprylate (SNAC), an absorption enhancer that facilitates semaglutide absorption across the gastric epithelium in a concentration-dependent manner. This family of drugs apart from glucose lowering effects causes significant weight loss with lower risk of hypoglycemia, and some of them have been linked to a significant reduced major adverse cardiovascular events. GLP-1 RAs may assist persons with T2DM and chronic kidney disease (CKD), a major microvascular consequence of T2DM, in ways other than lowering blood sugar. Several large clinical studies, the bulk of which are cardiovascular outcome trials, show that GLP-1 RA treatment is safe and tolerated for persons with T2DM and impaired renal function and that it may potentially have renoprotective characteristics. This article focuses on the advances of oral GLP1-RA and describes the key milestones and predicted advantages.

Hamiltonian Learning from Time Dynamics Using Variational Algorithms.

The Hamiltonian of a quantum system governs the dynamics of the system via the Schrodinger equation. In this paper, the Hamiltonian is reconstructed in the Pauli basis using measurables on random states forming a time series data set. The time propagation is implemented through Trotterization and optimized variationally with gradients computed on the quantum circuit. We validate our output by reproducing the dynamics of unseen observables on a randomly chosen state not used for the optimization. Unlike existing techniques that try and exploit the structure/properties of the Hamiltonian, our scheme is general and provides freedom with regard to what observables or initial states can be used while still remaining efficient with regard to implementation. We extend our protocol to doing quantum state learning where we solve the reverse problem of doing state learning given time series data of observables generated against several Hamiltonian dynamics. We show results on Hamiltonians involving XX, ZZ couplings along with transverse field Ising Hamiltonians and propose an analytical method for the learning of Hamiltonians consisting of generators of the SU(3) group. This paper is likely to pave the way toward using Hamiltonian learning for time series prediction within the context of quantum machine learning algorithms.

Quantum machine learning for chemistry and physics.

Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.

Prime factorization using quantum variational imaginary time evolution.

The road to computing on quantum devices has been accelerated by the promises that come from using Shor's algorithm to reduce the complexity of prime factorization. However, this promise hast not yet been realized due to noisy qubits and lack of robust error correction schemes. Here we explore a promising, alternative method for prime factorization that uses well-established techniques from variational imaginary time evolution. We create a Hamiltonian whose ground state encodes the solution to the problem and use variational techniques to evolve a state iteratively towards these prime factors. We show that the number of circuits evaluated in each iteration scales as [Formula: see text], where n is the bit-length of the number to be factorized and d is the depth of the circuit. We use a single layer of entangling gates to factorize 36 numbers represented using 7, 8, and 9-qubit Hamiltonians. We also verify the method's performance by implementing it on the IBMQ Lima hardware to factorize 55, 65, 77 and 91 which are greater than the largest number (21) to have been factorized on IBMQ hardware.