MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pre-trained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries

Programming a Gate-based Quantum Computer: a Comparative Analysis of the Software Development Kits for Circuit Design Automation

openThe rapid development of gate-based Quantum Computers has opened new possibilities for solving complex computational problems. However, programming these quantum systems has to deal with new challenges due to the fundamental differences between classical and Quantum Computing paradigms. This thesis presents a comparative analysis of Software Development Kits (SDKs) conceived for circuit design automation in gate-based quantum computers. The objective of this research is to evaluate and compare the capabilities, features, and usability of existing SDKs focusing on the functionalities such as allowing users to define quantum circuits, apply gate operations, and simulate their behaviour.   Apart from the widely adopted frameworks such as Qiskit, TKET, and Cirq, the analysis also includes the recently developed SDK from the University of Padua: Quantum Matcha Tea. The comparative analysis is conducted through a series of experiments and benchmarks performed on each SDK having as central points the programming interfaces usability, the documentation completeness, and the availability of support provided by the vendor or the related developer community. Another goal of this work is to explore the efficiency and flexibility of the various SDKs in handling common quantum computing tasks, such as quantum circuit design, gate operation, and circuit execution both on simulators and real quantum hardware.   The ambition of this comparative analysis is to give useful insights to researchers, developers, and practitioners in order to identify strengths and weaknesses of different SDKs depending on the specific requirements of the algorithms that need to be implemented. Additionally, the research aims to contribute to the advancement of SDKs by identifying areas of improvement and potential future directions in the development of quantum programming tools.The rapid development of gate-based Quantum Computers has opened new possibilities for solving complex computational problems. However, programming these quantum systems has to deal with new challenges due to the fundamental differences between classical and Quantum Computing paradigms. This thesis presents a comparative analysis of Software Development Kits (SDKs) conceived for circuit design automation in gate-based quantum computers. The objective of this research is to evaluate and compare the capabilities, features, and usability of existing SDKs focusing on the functionalities such as allowing users to define quantum circuits, apply gate operations, and simulate their behaviour.   Apart from the widely adopted frameworks such as Qiskit, TKET, and Cirq, the analysis also includes the recently developed SDK from the University of Padua: Quantum Matcha Tea. The comparative analysis is conducted through a series of experiments and benchmarks performed on each SDK having as central points the programming interfaces usability, the documentation completeness, and the availability of support provided by the vendor or the related developer community. Another goal of this work is to explore the efficiency and flexibility of the various SDKs in handling common quantum computing tasks, such as quantum circuit design, gate operation, and circuit execution both on simulators and real quantum hardware.   The ambition of this comparative analysis is to give useful insights to researchers, developers, and practitioners in order to identify strengths and weaknesses of different SDKs depending on the specific requirements of the algorithms that need to be implemented. Additionally, the research aims to contribute to the advancement of SDKs by identifying areas of improvement and potential future directions in the development of quantum programming tools

advligorts: The Advanced LIGO Real-Time Digital Control and Data Acquisition System

The Advanced LIGO detectors are sophisticated opto-mechanical devices. At the core of their operation is feedback control. The Advanced LIGO project developed a custom digital control and data acquisition system to handle the unique needs of this new breed of astronomical detector. The advligorts is the software component of this system. This highly modular and extensible system has enabled the unprecedented performance of the LIGO instruments, and has been a vital component in the direct detection of gravitational waves

A Language and Hardware Independent Approach to Quantum-Classical Computing

Heterogeneous high-performance computing (HPC) systems offer novel architectures which accelerate specific workloads through judicious use of specialized coprocessors. A promising architectural approach for future scientific computations is provided by heterogeneous HPC systems integrating quantum processing units (QPUs). To this end, we present XACC (eXtreme-scale ACCelerator) --- a programming model and software framework that enables quantum acceleration within standard or HPC software workflows. XACC follows a coprocessor machine model that is independent of the underlying quantum computing hardware, thereby enabling quantum programs to be defined and executed on a variety of QPUs types through a unified application programming interface. Moreover, XACC defines a polymorphic low-level intermediate representation, and an extensible compiler frontend that enables language independent quantum programming, thus promoting integration and interoperability across the quantum programming landscape. In this work we define the software architecture enabling our hardware and language independent approach, and demonstrate its usefulness across a range of quantum computing models through illustrative examples involving the compilation and execution of gate and annealing-based quantum programs