Working together, ETH Zurich and Microsoft QuArC researchers have provided the first application of machine-learning techniques to solve outstanding problems in quantum physics. The neural networks used in their study developed a genuine intuition of the bizarre behavior of quantum particles.
A major hurdle for quantum algorithms for linear systems of equations, and for quantum simulation algorithms, is the difficulty to find simple circuits for arithmetic. Prior approaches typically led to a large overhead in terms of quantum memory, required operations, or implementation error.
QCoDeS is an open source data acquisition framework that was created by distilling the homegrown solutions used in Station Q’s experimental labs, and infused with all the best practices from the open source software world.
As its name implies, the poisoning of Majorana devices by normal electrons is fatal to topological computation, so much effort is now focused on characterizing the degree of poisoning either by the creation of quasiparticle pairs within the device, or by electrons entering the device through the leads. A recent experiment (see https://arxiv.org/abs/1612.
The first signature of Majorana physics, identified experimentally at TU Delft in 2012, focused on a characteristic conductance peak at zero voltage. It bore many signatures of Majorana zero modes, but had a sizable background signal that obscured how the peak arose out of coalescing Andreev bound states.
Majorana devices will generally be much more complicated than the single-junction or single quantum dot Majorana devices that have been realized in the literature so far. (See https://arxiv.org/abs/1610.05289 by the Station Q team for examples of complex devices.
Last year saw a materials breakthrough, with the realization of a two-dimensional heterostructure combining superconductor and semiconductor layers. (See journals.aps.org/prb/abstract/10.1103/PhysRevB.93.15540.) Now, as shown in a recent report, this material has been used to study interference effects controlled by magnetic fields in a Josephson junction made from this material.
Generation of reversible circuits from high-level code is an important problem in the compilation flow of quantum algorithms to lower-level hardware. The instantiation of quantum oracles in particular will require mapping classical circuits to a reversible implementation.
In the paper, “Double semions in arbitrary dimension,” published in Communications in Mathematical Physics, Michael Freedman and Matthew Hastings present a new construction of topological phases of matter in higher dimensions, generalizing the double semion theory in two dimensions. This theory is distinct from the Dijkgraaf-Witten model and generalized toric code models. Read the published version.
In this paper, we show by computer simulations that a new material, which is a specific combination of the two semiconductors InAs and InSb (both are currently used in experimental setups on Majorana zero modes), has vastly improved properties compared to InAs or InSb alone, making it ideally suited for future devices.
In this paper, published in Physical Review A, we show how to greatly improve success at solving Constraint Satisfaction Problems on a quantum computer by using a learned schedule, instead of the standard linear ramps.
Topological materials can yield quasiparticles that behave in a manner similar to elementary particles that are part of the standard model of particle physics. In this paper, published in Physical Review X, we report on a new class of such quasiparticles—triple point fermions—which represent fermions that have mixed properties of Dirac and Weyl fermions.
