Design, Control and Application
of Next Generation Qubits
Arun Bansil, Northeastern University
(Principal Investigator)
Claudio Chamon, Boston University (Co-Investigator)
Adrian Feiguin, Northeastern University (Co-Investigator)
Liang Fu, MIT (Co-Investigator)
Eduardo Mucciolo, Univ. of Central Florida
(Co-Investigator)
Qimin Yan, Temple University (Co-Investigator)
The quest for
developing technologies for manipulating and storing information quantum
mechanically is currently led by approaches based on Josephson-junctions, ion-traps,
and qubits generated by defect spins in solids. Topological qubits, however,
are inherently more robust to decoherence by environmental effects, and should
be able to sprint ahead once practical barriers have been overcome. At the
present stage of the development of the field, it is important to explore a
variety of architectures and materials beyond the conventional paradigms in
order to seed breakthroughs toward building a scalable quantum computer. Our
comprehensive theoretical research program involves four interconnected thrusts
as follows.
·
A materials
discovery effort in two-dimensional compounds in search of materials to support
Majorana zero modes and defect structures suitable as qubits.
·
Exploration
of architectures for topological quantum computation by investigating both superconducting
Majorana qubits, and robust platforms for braiding with new “meta-materials”
built of arrays of Majorana qubits.
·
Investigation
of properties of hybrid metal-organic qubits based on transition-metal centers
in graphene, and molecular crystals of polyaromatic complexes with embedded transition-metal
atoms.
·
Development
of tensor-network and semiclassical approaches to study decoherence in the
presence of random and dispersive spin baths, and NV centers in diamond.
The full
spectrum of theoretical and numerical approaches is being used to address the key
problems, including first-principles, density-matrix-renormalization group,
tensor networks, and data-driven high-throughput approaches using materials
database and machine-learning. Our team combines diverse and complementary skills
for the successful completion of the project.