Roadmap on quantum nanotechnologies

Laucht, Arne; Hohls, Frank; Ubbelohde, Niels; González-Zalba, M. Fernando; Reilly, David; Stobbe, Søren; Schröder, Tim; Scarlino, Pasquale; Koski, Jonne; Dzurak, Andrew S.; Yang, C. H.; Yoneda, Jun; Kuemmeth, Ferdinand; Bluhm, Hendrik; Pla, Jarryd; Hill, Charles D.; Salfi, Joe; Oiwa, A.; Muhonen, Juha T.; Verhagen, Ewold; LaHaye, Matthew; Kim, Hyun Ho; Tsen, Adam W.; Culcer, Dimitrie; Geresdi, Attila; Mol, Jan A.; Mohan, Varun; Jain, Prashant K.; Baugh, Jonathan

doi:10.1088/1361-6528/abb333

Cited by 83 publications

(55 citation statements)

References 196 publications

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“…Quantum nanoscience is the study of nanostructured systems that incorporate and exploit quantum effects [111]. The fabrication of integrated circuits and nanomedicine are two of the primary applications of quantum nanoscience [112,113].…”

Section: Quantum Nanoscience For Neurobiologymentioning

confidence: 99%

Quantum Neurobiology

2022

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Quantum neurobiology is concerned with potential quantum effects operating in the brain and the application of quantum information science to neuroscience problems, the latter of which is the main focus of the current paper. The human brain is fundamentally a multiscalar problem, with complex behavior spanning nine orders of magnitude-scale tiers from the atomic and cellular level to brain networks and the central nervous system. In this review, we discuss a new generation of bio-inspired quantum technologies in the emerging field of quantum neurobiology and present a novel physics-inspired theory of neural signaling (AdS/Brain (anti-de Sitter space)). Three tiers of quantum information science-directed neurobiology applications can be identified. First are those that interpret empirical data from neural imaging modalities (EEG, MRI, CT, PET scans), protein folding, and genomics with wavefunctions and quantum machine learning. Second are those that develop neural dynamics as a broad approach to quantum neurobiology, consisting of superpositioned data modeling evaluated with quantum probability, neural field theories, filamentary signaling, and quantum nanoscience. Third is neuroscience physics interpretations of foundational physics findings in the context of neurobiology. The benefit of this work is the possibility of an improved understanding of the resolution of neuropathologies such as Alzheimer’s disease.

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Section: Quantum Nanoscience For Neurobiologymentioning

confidence: 99%

Quantum Neurobiology

2022

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“…Solid-state electron quantum optics is a branch of quantum technologies and concerns the creation, characterization and exploitation of individual excitations of electrical current. It offers potential applications in sensing, metrology and quantum information processing [1][2][3][4]. In direct analogy with photonics [5], a hallmark signature of quantum statistics in electron quantum optics is the electronic Hong-Ou-Mandel (HOM) two-particle interference at a beamsplitter, first demonstrated [6] for on-demand sources of well-screened excitations of chiral edge states in an integer filling factor quantum Hall system [7].…”

Section: Introductionmentioning

confidence: 99%

Collision of two interacting electrons on a mesoscopic beamsplitter: exact solution in the classical limit

Pavlovska¹,

Silvestrov²,

Recher³

et al. 2022

Preprint

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Experiments on collisions of isolated electrons guided along the edges in quantum Hall setups can mimic mixing of photons with the important distinction that electrons are charged fermions. In the so-called electronic Hong-Ou-Mandel (HOM) setup uncorrelated pairs of electrons are injected towards a beamsplitter. If the two electron wave packets were identical, Fermi statistics would force the electrons to scatter to different detectors, yet this quantum antibunching may be confounded by Coulomb repulsion. Here we model an electronic HOM experiment using a quadratic 2D saddle point potential for the beamsplitter and unscreened Coulomb interaction between the two injected electrons subjected to a strong out-of-plane magnetic field. We show that classical equations of motion for the drift dynamics of electrons' guiding centers take on the form of Hamilton equations for canonically conjugated variables subject to the saddle point potential and the Coulomb potential where the dynamics of the cente-of-mass coordinate and the relative coordinate separate. We use these equations to determine collision outcomes in terms of a few experimentally tuneable parameters: the initial energies of the uncorrelated electrons, relative time delay of injection and the shape of the saddle point potential. A universal phase diagram of deterministic bunching and antibunching scattering outcomes is presented with a single energy scale characterizing the increase of the effective barrier height due to interaction of coincident electrons. We suggest clear-cut experimental strategies to detect the predicted effects and give analytical estimates of conditions when the classical dynamics is expected to dominate over quantum effects.

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“…In this context, we review the scientific progress in fabricating qubits based on electron spins in gate‐defined silicon quantum dots. [ 1–5 ] These devices have the potential to leverage the most advanced form of semiconductor engineering from the complementary metal‐oxide‐semiconductor (CMOS) industry. We focus on the choices of materials and what is known about their impact on qubit performance, substantiated by decades of academic research.…”

Section: Introductionmentioning

confidence: 99%

Materials for Silicon Quantum Dots and their Impact on Electron Spin Qubits

Saraiva

Lim

Yang

et al. 2021

Adv Funct Materials

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Quantum computers have the potential to efficiently solve problems in logistics, drug and material design, finance, and cybersecurity. However, millions of qubits will be necessary for correcting inevitable errors in quantum operations. In this scenario, electron spins in gate‐defined silicon quantum dots are strong contenders for encoding qubits, leveraging the microelectronics industry know‐how for fabricating densely populated chips with nanoscale electrodes. The sophisticated material combinations used in commercially manufactured transistors, however, will have a very different impact on the fragile qubits. Here some key properties of the materials that have a direct impact on qubit performance and variability are reviewed.

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Roadmap on quantum nanotechnologies

Cited by 83 publications

References 196 publications

Quantum Neurobiology

Quantum Neurobiology

Collision of two interacting electrons on a mesoscopic beamsplitter: exact solution in the classical limit

Materials for Silicon Quantum Dots and their Impact on Electron Spin Qubits

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