Optimization of Circuits for IBM's five-qubit Quantum Computers

IBM has made several quantum computers available to researchers around the world via cloud services. Two architectures with five qubits, one with 16, and one with 20 qubits are available to run experiments. The IBM architectures implement gates from the Clifford+T gate library. However, each architecture only implements a subset of the possible CNOT gates. In this paper, we show how Clifford+T circuits can efficiently be mapped into the two IBM quantum computers with 5 qubits. We further present an algorithm and a set of circuit identities that may be used to optimize the Clifford+T circuits in terms of gate count and number of levels. It is further shown that the optimized circuits can considerably reduce the gate count and number of levels and thus produce results with better fidelity

Is there a Moore's law for quantum computing?

There is a common wisdom according to which many technologies can progress according to some exponential law like the empirical Moore's law that was validated for over half a century with the growth of transistors number in chipsets. As a still in the making technology with a lot of potential promises, quantum computing is supposed to follow the pack and grow inexorably to maturity. The Holy Grail in that domain is a large quantum computer with thousands of errors corrected logical qubits made themselves of thousands, if not more, of physical qubits. These would enable molecular simulations as well as factoring 2048 RSA bit keys among other use cases taken from the intractable classical computing problems book. How far are we from this? Less than 15 years according to many predictions. We will see in this paper that Moore's empirical law cannot easily be translated to an equivalent in quantum computing. Qubits have various figures of merit that won't progress magically thanks to some new manufacturing technique capacity. However, some equivalents of Moore's law may be at play inside and outside the quantum realm like with quantum computers enabling technologies, cryogeny and control electronics. Algorithms, software tools and engineering also play a key role as enablers of quantum computing progress. While much of quantum computing future outcomes depends on qubit fidelities, it is progressing rather slowly, particularly at scale. We will finally see that other figures of merit will come into play and potentially change the landscape like the quality of computed results and the energetics of quantum computing. Although scientific and technological in nature, this inventory has broad business implications, on investment, education and cybersecurity related decision-making processes.Comment: 32 pages, 24 figure

Compilation Optimizations to Enhance Resilience of Big Data Programs and Quantum Processors

Modern computers can experience a variety of transient errors due to the surrounding environment, known as soft faults. Although the frequency of these faults is low enough to not be noticeable on personal computers, they become a considerable concern during large-scale distributed computations or systems in more vulnerable environments like satellites. These faults occur as a bit flip of some value in a register, operation, or memory during execution. They surface as either program crashes, hangs, or silent data corruption (SDC), each of which can waste time, money, and resources. Hardware methods, such as shielding or error correcting memory (ECM), exist, though they can be difficult to implement, expensive, and may be limited to only protecting against errors in specific locations. Researchers have been exploring software detection and correction methods as an alternative, commonly trading either overhead in execution time or memory usage to protect against faults. Quantum computers, a relatively recent advancement in computing technology, experience similar errors on a much more severe scale. The errors are more frequent, costly, and difficult to detect and correct. Error correction algorithms like Shor’s code promise to completely remove errors, but they cannot be implemented on current noisy intermediate-scale quantum (NISQ) systems due to the low number of available qubits. Until the physical systems become large enough to support error correction, researchers instead have been studying other methods to reduce and compensate for errors. In this work, we present two methods for improving the resilience of classical processes, both single- and multi-threaded. We then introduce quantum computing and compare the nature of errors and correction methods to previous classical methods. We further discuss two designs for improving compilation of quantum circuits. One method, focused on quantum neural networks (QNNs), takes advantage of partial compilation to avoid recompiling the entire circuit each time. The other method is a new approach to compiling quantum circuits using graph neural networks (GNNs) to improve the resilience of quantum circuits and increase fidelity. By using GNNs with reinforcement learning, we can train a compiler to provide improved qubit allocation that improves the success rate of quantum circuits

Estado del arte en computación cuántica con plataformas de fuente abierta

The idea behind the QC paradigm lies in the information storage capacity in amplitude values that use systems based on Qubits or quantum bits -not in bits-, and in the processing of it when transformations are required to change these amplitudes in a precise and controlled way. Therefore, the description of the states in a computer evolves obeying different algorithms than the known ones: correcting errors and digitizing arbitrarily precise calculations through limited resources. The present documentary research, carried out by the Orion research group in 2018, establishes a state of the art of knowledge in QC oriented to education as a baseline for future research whose development platforms are open source kind. A methodology based on indexes is established to categorize and subcategorize the QC, as are: fundamentals and background, QC history, concept and operation, companies that implement the QC field, applications, platforms that are managed in QC, architecture, and finally the programming languages. Source databases such as IEEE-Xplore, EBSCO, and websites, were used to illustrate the fundamental concepts and developments that companies have specified in applications using this new paradigm.La idea que subyace al paradigma de la CC estriba en la capacidad de almacenamiento de información en valores de amplitud que usan sistemas basados en Qubits o bits cuánticos –no en bits-, y en el procesamiento de la misma cuando se requieren transformaciones para cambiar estas amplitudes de una manera precisa y controlada. Por lo anterior, la descripción de los estados en una computadora evoluciona obedeciendo a algoritmos distintos a los conocidos: corrigiendo errores y digitalizando cálculos arbitrariamente precisos a través de recursos limitados. La presente investigación documental, adelantada por el grupo de investigación Orión en 2018, establece un estado del arte del conocimiento en CC orientada a educación como línea de base para investigaciones cuyas plataformas de desarrollo sean tipo open source. Para lo anterior, se establece una metodología basada en índices para categorizar y subcategorizar la CC, como lo son: fundamentos y antecedentes, historia de la CC, concepto y funcionamiento, empresas que implementan el campo de la CC, aplicaciones, plataformas que se manejan en la CC, arquitectura, y por último los lenguajes de programación.  Las bases de datos fuente usadas fueron: IEEE-Xplore, EBSCO, y sitios web para identificar las empresas que hacen uso del paradigma CC

Software Techniques to Mitigate Errors on Noisy Quantum Computers

Quantum computers are domain-specific accelerators that can provide a large speedup for important problems. Quantum computers with few tens of qubits have already been demonstrated, and machines with 100+ qubits are expected soon. These machines face significant reliability and scalability challenges. The high hardware error rates limit quantum computers. To enable quantum speedup, it is essential to mitigate hardware errors. Our first work exploits the variability in the error rates of qubits to steer more operations towards qubits with lower error rates and avoid error-prone qubits. Our second work looks at executing different versions of the programs tuned to cause diverse mistakes so that the machine is less vulnerable to correlated errors, thereby making it easier to infer the correct answer. Our third work looks at exploiting the state-dependent bias in measurement errors (state 1 is more error-prone than state 0) and dynamically flips the state of the qubit to measure the stronger state. We perform our evaluations on real quantum machines from IBM and demonstrate significant improvement in the overall system reliability.Ph.D

Understanding Quantum Technologies 2022

Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma

La Logistica del Computer Quantistico e l'Informatica Relativa

Notizie da giornali e siti web riportato sempre più enfaticamente i successi relativi a computer quantistici. È pertanto naturale porsi delle domante circa questi computer, su come essi operano, su dove sono fisicamente, su cosa serve alla loro gestione. Anzi, è doveroso porsi domande, poiché i titoli delle notizie possono indurre ad intenderli come “supercomputer” effettivamente esistenti, capaci di risolvere in pochi secondi dei problemi di calcolo che i computer classici impiegherebbero millenni a processare. Questo lavoro propone quindi una review degli attuali Computer Quantistici, con particolare riguardo, ove possibile, alla relativa logistica. Nel panorama dell’Informatica Quantistica, si intende approfondire come potrà essere il futuro delle infrastrutture ad essi legate e quali saranno i sistemi base di algoritmi relativi al Calcolo Quantistico. In definitiva, si cercherà di comprendere quale sarà la futura logistica, intesa come nuova «arte del computare». Particolare attenzione verrà data al vantaggio quantistico, la strategia di medio termine relativa alla progettazione di algoritmi per le prossime generazioni di computer quantistici. Tali algoritmi sono richiesti da un ventaglio di applicazioni strategiche. Attualmente sono principalmente algoritmi di simulazione di fisica e chimica quantistica. Si illustreranno due metodi di calcolo: quello basato sulla computazione quantistica abiabatica, che si ottiene attraverso il quantum annealing (sistemi D-Wave), e quello che utilizza un array di porte quantistiche (sistemi IBM, Google ed altri). Si vedranno, con maggiore dettaglio, le simulazioni basate sul metodo del Variational Quantum Eigensolver. Alcune nozioni relative ai qubit, alle porte quantistiche ed alle Hamiltoniane ad esse legate verranno proposte. Oltre a parlare di qubit, si parlerà di qumode. L’arte del computare quantistico ha infatti due facce, quella basata sul qubit e quella basata sul qumode. Il quantum computer Borealis di Xanadu è capace di sviluppare un sistema ibrido, con una emulazione di qubit di tipo GKP insieme a luce squeezed. Verrà inoltre menzionato il primo desktop a tre qubits di SpinQ. Verrà anche fornito un accenno alla comunicazione quantistic

The Nexus between Artificial Intelligence and Economics

This book is organized as follows. Section 2 introduces the notion of the Singularity, a stage in development in which technological progress and economic growth increase at a near-infinite rate. Section 3 describes what artificial intelligence is and how it has been applied. Section 4 considers artificial happiness and the likelihood that artificial intelligence might increase human happiness. Section 5 discusses some prominent related concepts and issues. Section 6 describes the use of artificial agents in economic modeling, and section 7 considers some ways in which economic analysis can offer some hints about what the advent of artificial intelligence might bring. Chapter 8 presents some thoughts about the current state of AI and its future prospects.