A quantum computer is a computer that exploits quantum mechanical phenomena. On small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior using specialized hardware. Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster than any modern "classical" computer. Theoretically a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the art is largely experimental and impractical, with several obstacles to useful applications.
Neuromorphic computing is an approach to computing that is inspired by the structure and function of the human brain. A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems. Recent advances have even discovered ways to mimic the human nervous system through liquid solutions of chemical systems.
Quantum programming is the process of designing or assembling sequences of instructions, called quantum circuits, using gates, switches, and operators to manipulate a quantum system for a desired outcome or results of a given experiment. Quantum circuit algorithms can be implemented on integrated circuits, conducted with instrumentation, or written in a programming language for use with a quantum computer or a quantum processor.
Unconventional computing is computing by any of a wide range of new or unusual methods.
A topological quantum computer is a theoretical type of quantum computer proposed by Russian-American physicist Alexei Kitaev in 1997. It utilizes quasiparticles, known as anyons, in two-dimensional systems. These anyons' world lines intertwine to form braids in a three-dimensional spacetime. These braids act as the logic gates of the computer. The primary advantage of using quantum braids over trapped quantum particles is enhanced stability. While small, cumulative perturbations can cause quantum states to decohere and introduce errors in traditional quantum computations, such perturbations do not alter the topological properties of the braids. This stability is akin to the difference between cutting and reattaching a string to form a different braid versus a ball colliding with a wall.
Quantum annealing (QA) is an optimization process for finding the global minimum of a given objective function over a given set of candidate solutions, by a process using quantum fluctuations. Quantum annealing is used mainly for problems where the search space is discrete with many local minima; such as finding the ground state of a spin glass or solving the traveling salesman problem. The term "quantum annealing" was first proposed in 1988 by B. Apolloni, N. Cesa Bianchi and D. De Falco as a quantum-inspired classical algorithm. It was formulated in its present form by T. Kadowaki and H. Nishimori in 1998, though an imaginary-time variant without quantum coherence had been discussed by A. B. Finnila, M. A. Gomez, C. Sebenik and J. D. Doll in 1994.
D-Wave Quantum Systems Inc. is a quantum computing company with locations in Palo Alto, California and Burnaby, British Columbia. D-Wave claims to be the world's first company to sell computers that exploit quantum effects in their operation. D-Wave's early customers include Lockheed Martin, the University of Southern California, Google/NASA, and Los Alamos National Laboratory.
Bettina Heim was a Swiss competitive figure skater who now leads the language design team for Microsoft's Q# programming language.
Adiabatic quantum computation (AQC) is a form of quantum computing which relies on the adiabatic theorem to perform calculations and is closely related to quantum annealing.
Michele Parrinello is an Italian physicist particularly known for his work in molecular dynamics. Parrinello and Roberto Car were awarded the Dirac Medal of the International Centre for Theoretical Physics (ICTP) and the Sidney Fernbach Award in 2009 for their continuing development of the Car–Parrinello method, first proposed in their seminal 1985 paper, "Unified Approach for Molecular Dynamics and Density-Functional Theory". They have continued to receive awards for this breakthrough, most recently the Dreyfus Prize in the Chemical Sciences and the 2021 Benjamin Franklin Medal in Chemistry.
Quantum machine learning is the integration of quantum algorithms within machine learning programs.
IBM Quantum Platform is an online platform allowing public and premium access to cloud-based quantum computing services provided by IBM. This includes access to a set of IBM's prototype quantum processors, a set of tutorials on quantum computation, and access to an interactive textbook. As of February 2021, there are over 20 devices on the service, six of which are freely available for the public. This service can be used to run algorithms and experiments, and explore tutorials and simulations around what might be possible with quantum computing.
Andreas Wallraff is a German physicist who conducts research in quantum information processing and quantum optics. He has taught as a professor at ETH Zürich in Zürich, Switzerland since 2006. He worked as a research scientist with Robert J. Schoelkopf at Yale University from 2002 to 2005, during which time he performed experiments in which the coherent interaction of a single photon with a single quantum electronic circuit was observed for the first time. His current work at ETH Zürich focuses on hybrid quantum systems combining superconducting electronic circuits with semiconductor quantum dots and individual Rydberg atoms as well as quantum error correction with superconducting qubits.
In quantum computing, quantum supremacy or quantum advantage is the goal of demonstrating that a programmable quantum computer can solve a problem that no classical computer can solve in any feasible amount of time, irrespective of the usefulness of the problem. The term was coined by John Preskill in 2012, but the concept dates to Yuri Manin's 1980 and Richard Feynman's 1981 proposals of quantum computing.
Applying classical methods of machine learning to the study of quantum systems is the focus of an emergent area of physics research. A basic example of this is quantum state tomography, where a quantum state is learned from measurement. Other examples include learning Hamiltonians, learning quantum phase transitions, and automatically generating new quantum experiments. Classical machine learning is effective at processing large amounts of experimental or calculated data in order to characterize an unknown quantum system, making its application useful in contexts including quantum information theory, quantum technologies development, and computational materials design. In this context, it can be used for example as a tool to interpolate pre-calculated interatomic potentials or directly solving the Schrödinger equation with a variational method.
Neural Network Quantum States is a general class of variational quantum states parameterized in terms of an artificial neural network. It was first introduced in 2017 by the physicists Giuseppe Carleo and Matthias Troyer to approximate wave functions of many-body quantum systems.
Giuseppe Carleo is an Italian physicist. He is a professor of computational physics at EPFL and the head of the Laboratory of Computational Quantum Science.
The current state of quantum computing is referred to as the noisy intermediate-scale quantum (NISQ) era, characterized by quantum processors containing up to 1,000 qubits which are not advanced enough yet for fault-tolerance or large enough to achieve quantum advantage. These processors, which are sensitive to their environment (noisy) and prone to quantum decoherence, are not yet capable of continuous quantum error correction. This intermediate-scale is defined by the quantum volume, which is based on the moderate number of qubits and gate fidelity. The term NISQ was coined by John Preskill in 2018.
Torsten Hoefler is a Professor of Computer Science at ETH Zurich and the Chief Architect for Machine Learning at the Swiss National Supercomputing Centre. Previously, he led the Advanced Application and User Support team at the Blue Waters Directorate of the National Center for Supercomputing Applications, and held an adjunct professor position at the Computer Science Department at the University of Illinois at Urbana Champaign. His expertise lies in large-scale parallel computing and high-performance computing systems. He focuses on applications in large-scale artificial intelligence as well as climate sciences.
Zhenghan Wang is a Chinese-American mathematician. He is a principal researcher at Microsoft Station Q, as well as a professor of mathematics at the University of California, Santa Barbara.
