Summer 2018

The last decade has seen the rapid growth of interest in materials that are topological because of strong spin-orbit coupling. These samples exhibit electronic surface states that have only half the electrons that a carrier accumulation layer has. The surface states have energy between the occupied valence band and the unoccupied conduction band and the energy/momentum dispersion is linear forming two so-called Dirac cones that meet at the Dirac point, often k=0. They exhibit spin-momentum locking so that a surface state with momentum in some direction has spin...

Quantum computation is a promising way to expand computational power as well as perform quantum simulations. There are many proposals on implementing quantum computation, including topological materials, trapped ions, superconducting circuits as well as semiconductor quantum dots. Semiconductor quantum dot qubits are promising candidates for quantum information processing and have recently made substantial experimental progress. One challenge for qubits without topological protection, however, is to suppress decoherence. Performing qubit gate operations as quickly as possible can be important to minimize the effects of decoherence. For resonant gates, this requires applying a strong ac drive. However, strong driving can present control challenges because of the strong driving effects that cannot be described using the rotating-wave approximation. Here we analyze resonant X rotations of a silicon double quantum dot hybrid qubit within a dressed-state formalism. We show that the strong driving effects can be suppressed to the point that gate fidelities above 99.99% are possible, in the absence of decoherence. When coupled to 1/f charge noise typical to our device, we further show that, by applying strong driving, gate fidelities can be above 99.9%. This shows that the quantum operations on silicon quantum dot hybrid qubits can be above the error-correction threshold, which is an important step towards realizing quantum computation.

We present a covariant theory for the ageing characteristics of phase-ordering systems that possess dynamical symmetries beyond mere scalings. A chiral spin dynamics which conserves the spin-up (+) and spin-down (−) fractions,  and , serves as the emblematic paradigm of our theory. Beyond a parabolic spatio-temporal scaling, we discover a hidden Lorentzian dynamical symmetry therein, and thereby prove that the characteristic length L of spin domainsgrows in time t according to , where  (the invariant spin-excess) and βis a universal constant. Furthermore, the normalised length distributions of the spin-up and the spin-down domains each provably adopt a coincident universal (σ-independent) time-invariant form, and this supra-universal probability distribution is empirically verified to assume a form reminiscent of the Wigner surmise.

Artificial neural networks are performing tasks, image recognition and natural language processing, for artificial intelligence. However, these algorithms run on traditional computers and consume orders of magnitude more energy more than the brain does at the same task. One promising path to reduce the energy consumption is to build dedicated hardware to perform artificial intelligence. Nanodevices are particularly interesting because they allow for complex functionality with low energy consumption and small size. I discuss two nanodevices. First, I focus on stochastic magnetic tunnel junctions, which can emulate the spike trains emitted by neurons with a switching rate that can be controlled by an input. junctions can be combined with CMOS circuitry to implement population coding to build low power computing systems capable of controlling output behavior. Second, I turn to different nanodevices, memristors, to implement a different type of computation occurring in nature: swarm intelligence. A broad class of algorithms inspired by the behavior of swarms have been proven successful at solving optimization problems (for example an ant colony can solve a maze). Networks of memristors can perform swarm intelligence and find the shortest paths in mazes, without any supervision or training. These results are striking illustrations of how matching the functionalities of nanodevices with relevant properties of natural systems open the way to low power hardware implementations of difficult computing problems.