The Association for Computing Machinery (ACM) has announced the inaugural issue of ACM Transactions on Quantum Computing (TQC), a new peer-reviewed journal with a focus on the theory and practice of quantum computing. TQC publishes high-impact, original research papers and select surveys on topics in quantum computing and quantum information science.
Though quantum computing, an interdisciplinary field that draws on contributions from computer science, physics, mathematics, and other disciplines, has been around since the 1980s, recent advances have propelled it to become one of the most highly anticipated innovations. Some of the largest technology companies are in a race to produce the first quantum computer, and governments around the world have invested billions in developing the burgeoning field.
It is expected that quantum computers, though not yet a fully realised technology, will be both disruptive and transformative, providing solutions to problems that were previously thought too complex. Many scientists also believe that quantum computing has the potential to usher in valuable advances in a number of areas including pharmaceuticals, materials science, artificial intelligence, and transportation, among many others.
Topics covered in TQC include, but are not limited to: models of quantum computing, quantum algorithms and complexity, quantum computing architecture, principles and methods of fault-tolerant quantum computation, design automation for quantum computing, quantum programming languages and systems, distributed quantum computing, quantum networking, quantum security and privacy, and applications (e.g., in machine learning and AI).
The inaugural issue of TQC presents a collection of five outstanding research papers that capture the breadth and sophistication of quantum computing research including (partial list): a novel technique for decomposition of a large class of quantum circuits; an efficient procedure for characterising Pauli channels; and new quantum machine learning algorithms for training and evaluating feedforward neural networks.
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