Hybrid quantum computers: a "one for all and all for one" approach with superconductors and spin ensembles
Professor Klaus Mølmer, University of Aarhus, Denmark
Since the 1994 discovery by Peter Shor that a quantum computer may factor large numbers efficiently, the potential for quantum computing has been recognized by a variety of public, strategic, and commercial organizations.
Quantum computing may be implemented with physical components that are already studied extensively in the laboratory: trapped ions, cold atoms, superconducting circuits, liquid and solid state spin ensembles, etc., and elementary gate operations and algorithms have already been demonstrated in experiments.
The basic challenge for quantum computing is to find and control a physical quantum system that offers rapid processing, long-time storage, scalability to a sufficient processor size, and means for intermediate or long distance communication. Since no single quantum system meets all these requirements, the concept of hybrid quantum technologies has emerged, where the different tasks are shared between physical components that are individually optimized for the different functions.
The interfacing of physical systems with very different spatial, temporal and energetic properties presents a big challenge in itself. By means of a recent successful example involving superconducting circuits and atomic spin degrees of freedom I shall give an illustrative example of how that challenge may be met.
NMR classical computation (expt)
Dr Matthias Bechmann, Johannes Kepler University Linz, Austria
Nuclear magnetic resonance (NMR) spectroscopy has been very successful, both for its role as spectroscopic tool to determine molecular structure on one side and as a test case for the quantum mechanical description of spins and their dynamics on the other. Precise measurement of the dynamics of spin-system ensembles is today facilitated by a high level of hardware engineering that has been put into the hardware. This all together made NMR also very attractive as a means for researching quantum computing.
Here the potential of such NMR spectrometers to implement classical computational paradigms is demonstrated. This scenario then makes it possible to combine and assess quantum and classical contributions to "computation" in a single experimental set-up.
Quantum and classical resources in measurement-based quantum computation
Dr Janet Anders, Dorothy Hodgkin Fellow, University of Exeter
Quantum physics is known to allow the implementation of powerful algorithms , however, the precise power increase over classical computation remains open. I will describe an insightful example where the computation relies on the interplay of two components - a restricted classical computer and its interaction with quantum correlated information. It turns out that quantum correlations can enhance the classical computer in such a way that it is able to compute problems beyond its own capability . The example belongs to a type of computation, measurement-based quantum computation, that has no counterpart in classical computing as the computation is driven by quantum measurements rather than gates. Future implementations of this model may require the combination of mobile information carriers that sequentially interact with a stationary register  or adiabatic implementations, where the random measurement is replaced by a deterministic evolution .
. Anders and Wiesner, Chaos 21, 037102 (2011)
. Anders and Browne, Phys Rev Lett 103, 070502 (2009)
. Kashefi et al, Theoretical Computer Science 430, 51 (2012)
. Antonio et al, arxiv 1309.1443 (2013)
Professor Tony Hey CBE FREng, Microsoft Research, USA
Theory and practice of molecular computing with DNA
Dr Damien Woods, Caltech, USA
One of the main aims of molecular engineering and computation is to control the structure and dynamics of molecular systems at the nanoscale. We'd like to design systems that are robust to fluctuations in temperature, fluid flow and other uncontrolled factors, yet consist of millions of interacting components. DNA is a versatile and programmable material that meets these criteria. The kinetics and thermodynamics of DNA are reasonably well-understood, and through straightforward Watson-Crick base-pairing interactions we can program this material to create complicated shapes and patterns, as well as to have intricate, even algorithmic, chemical dynamics, all at nanoscale spatial resolution. In this talk, I will give an overview of the state of the art with an emphasis on programability, abilities and limitations of these chemical systems. I will describe how computer science and biology can inspire us about which molecular systems we should try to build next, and how we can use mathematical and algorithmic thinking to give us the tools to control a cacophony of interacting molecules by simply letting them interact in a hands-off self-assembling fashion.