Andrew Briggs
Whenever a fundamental new principle of science is discovered, the chances are that sooner or later a way will be found to use it for a new technology. The quantum mechanical principles of superposition and entanglement, identified nearly a century ago, are now understood to offer spectacular potential for technological applications. Superposition describes how an object can be in two states at once, as it were "here" and "there" at the same time. Two or more objects in superposition states can be entangled, so that measurements on each of them are correlated in a way that goes beyond anything we would expect from everyday intuition. Exploiting these effects in practical devices would provide new capabilities for fields such as molecular light harvesting and for molecular quantum technologies such as sensors, simulators, and quantum computers.
Successful laboratory experiments have shown that molecules of various kinds can exhibit these crucial quantum properties. Molecules are composed of electrons and atomic cores or "nuclei". Both electrons and nuclei can have a property called spin associated with them that makes them behave like tiny bar magnets. We have confirmed that electron and nuclear spins can be put into superpositions or entangled, and they can last for a long time in that condition. Most of the experiments so far have been in small test tubes. The crucial step now is to implement the same effects in nanometre scale electrical devices, such as single electron transistors consisting of single sheets of carbon rolled up as nanotubes or flat as sheets of graphene. By making hybrid technologies that combine molecules with nanoelectronics, we will lay the foundation for scaling up to more complex systems.
At this very small size, different atoms or molecules in different places affect the behaviour of the device. A breakthrough in the past few years enables us to see the positions of individual atoms in the materials which we want to use in our devices. The technique is aberration-corrected electron microscopy, and provided the electrons are not too energetic it is possible to look at the structures which we have made without damaging them. In this way we shall be able to relate the device performance to the atomic resolution microscopy of the component materials. Individual components of this vision include the following.
- design the devices to build, based on a deep understanding of how to control their quantum states;
- produce the materials, such as molecules with suitable spin states with carbon nanotubes and graphene for electrical substrates;
- make nanoscale devices and examine them in a microscope to see where the individual atoms and molecules are;
- perform the experiments to develop the quantum control and measurement for the effects which we aim to exploit;
- undertake theoretical modelling to understand the electron behaviour and to design new materials systems for improved performance.
New Research Projects Available
Selected Publications
-
Experimental evidence of disorder enhanced electron-phonon scattering in graphene devices
December 2020|Journal article|Carbon -
Quantum device fine-tuning using unsupervised embedding learning
September 2020|Journal articleQuantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of these parameters on applied gate voltages. We experimentally demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement and assigns it a score using a variational auto-encoder. Gate voltage settings are set to optimise this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min.cond-mat.mes-hall, cond-mat.mes-hall, cs.LG, quant-ph -
Machine learning enables completely automatic tuning of a quantum device faster than human experts.
August 2020|Journal article|Nature communicationsVariability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies. -
Radio-frequency optomechanical characterization of a silicon nitride drum
February 2020|Journal article|Sci. Rep.On-chip actuation and readout of mechanical motion is key to characterize mechanical resonators and exploit them for new applications. We capacitively couple a silicon nitride membrane to an off resonant radio-frequency cavity formed by a lumped element circuit. Despite a low cavity quality factor (Q$_\mathrm{E}\approx$ 7.4) and off resonant, room temperature operation, we are able to parametrize several mechanical modes and estimate their optomechanical coupling strengths. This enables real-time measurements of the membrane's driven motion and fast characterization without requiring a superconducting cavity, thereby eliminating the need for cryogenic cooling. Finally, we observe optomechanically induced transparency and absorption, crucial for a number of applications including sensitive metrology, ground state cooling of mechanical motion and slowing of light.cond-mat.mes-hall, cond-mat.mes-hall -
A coherent nanomechanical oscillator driven by single-electron tunnelling
January 2020|Journal article|Nature PhysicsA single-electron transistor incorporated as part of a nanomechanical resonator represents an extreme limit of electron-phonon coupling. While it allows for fast and sensitive electromechanical measurements, it also introduces backaction forces from electron tunnelling which randomly perturb the mechanical state. Despite the stochastic nature of this backaction, under conditions of strong coupling it is predicted to create self-sustaining coherent mechanical oscillations. Here, we verify this prediction using time-resolved measurements of a vibrating carbon nanotube transistor. This electromechanical oscillator has intriguing similarities with a laser. The single-electron transistor, pumped by an electrical bias, acts as a gain medium while the resonator acts as a phonon cavity. Despite the unconventional operating principle, which does not involve stimulated emission, we confirm that the output is coherent, and demonstrate other laser behaviour including injection locking and frequency narrowing through feedback.cond-mat.mes-hall, cond-mat.mes-hall -
Large amplitude charge noise and random telegraph fluctuations in room-temperature graphene single-electron transistors.
January 2020|Journal article|NanoscaleWe analyze the noise in liquid-gated, room temperature, graphene quantum dots. These devices display extremely large noise amplitudes. The observed noise is explained in terms of a charge noise model by considering fluctuations in the applied source-drain and gate potentials. We show that the liquid environment and substrate have little effect on the observed noise and as such attribute the noise to charge trapping/detrapping at the disordered graphene edges. The trapping/detrapping of individual charges can be tuned by gating the device, which can result in stable two-level fluctuations in the measured current. These results have important implications for the use of electronic graphene nanodevices in single-molecule biosensing.Graphite, Equipment Design, Biosensing Techniques, Quantum Dots, Temperature, Nanotechnology, Models, Theoretical, Computer Simulation, Transistors, Electronic -
Efficiently measuring a quantum device using machine learning (vol 5, 79, 2019)
November 2019|Journal article|NPJ QUANTUM INFORMATION -
Understanding resonant charge transport through weakly coupled single-molecule junctions
October 2019|Journal article|Nature CommunicationsOff-resonant charge transport through molecular junctions has been extensively studied since the advent of single-molecule electronics and it is now well understood within the framework of the non-interacting Landauer approach. Conversely, gaining a qualitative and quantitative understanding of the resonant transport regime has proven more elusive. Here, we study resonant charge transport through graphene-based zinc-porphyrin junctions. We experimentally demonstrate an inadequacy of the non-interacting Landauer theory as well as the conventional single-mode Franck-Condon model. Instead, we model the overall charge transport as a sequence of non-adiabatic electron transfers, the rates of which depend on both outer and inner-sphere vibrational interactions. We show that the transport properties of our molecular junctions are determined by a combination of electron-electron and electron-vibrational coupling, and are sensitive to the interactions with the wider local environment. Furthermore, we assess the importance of nuclear tunnelling and examine the suitability of semi-classical Marcus theory as a description of charge transport in molecular devices.cond-mat.mes-hall, cond-mat.mes-hall, physics.chem-ph -
Efficiently measuring a quantum device using machine learning
September 2019|Journal article|njp Quantum InformationScalable quantum technologies will present challenges for characterizing and tuning quantum devices. This is a time-consuming activity, and as the size of quantum systems increases, this task will become intractable without the aid of automation. We present measurements on a quantum dot device performed by a machine learning algorithm. The algorithm selects the most informative measurements to perform next using information theory and a probabilistic deep-generative model, the latter capable of generating multiple full-resolution reconstructions from scattered partial measurements. We demonstrate, for two different measurement configurations, that the algorithm outperforms standard grid scan techniques, reducing the number of measurements required by up to 4 times and the measurement time by 3.7 times. Our contribution goes beyond the use of machine learning for data search and analysis, and instead presents the use of algorithms to automate measurement. This work lays the foundation for automated control of large quantum circuits.quant-ph, quant-ph, cond-mat.mes-hall, cs.LG -
Charge-state assignment of nanoscale single-electron transistors from their current-voltage characteristics.
August 2019|Journal article|NanoscaleThe electronic and magnetic properties of single-molecule transistors depend critically on the molecular charge state. Charge transport in single-molecule transistors is characterized by Coulomb-blocked regions in which the charge state of the molecule is fixed and current is suppressed, separated by high-conductance, sequential-tunneling regions. It is often difficult to assign the charge state of the molecular species in each Coulomb-blocked region due to variability in the work-function of the electrodes. In this work, we provide a simple and fast method to assign the charge state of the molecular species in the Coulomb-blocked regions based on signatures of electron-phonon coupling together with the Pauli-exclusion principle, simply by observing the asymmetry in the current in high-conductance regions of the stability diagram. We demonstrate that charge-state assignments determined in this way are consistent with those obtained from measurements of Zeeman splittings. Our method is applicable at 77 K, in contrast to magnetic-field-dependent measurements, which generally require low temperatures (below 4 K). Due to the ubiquity of electron-phonon coupling in molecular junctions, we expect this method to be widely applicable to single-electron transistors based on single molecules and graphene quantum dots. The correct assignment of charge states allows researchers to better understand the fundamental charge-transport properties of single-molecule transistors.