General Description
Quantum physics has an enormous potential to transform computing technology. When making the leap from a PC or supercomputer to a quantum computer, the logical processing and the physical basis change in dramatic ways. The basis for the PC is classical physics, but for the quantum computer it is quantum physics, opening the door to non-intuitive properties such as superposition and entanglement. On the logical side, the elementary unit of information changes from the bit to the qubit, allowing for entirely novel ways of data processing.
Quantum computers with a restricted number of qubits have recently been demonstrated in several laboratories. However, owing to the unusual properties of quantum algorithms even a small number of qubits may be sufficient to obtain meaningful computational results. The goal of this research program is to demonstrate that existing and near-term (5-8 years) quantum computing (QC) technologies can be used to generate meaningful computational results of scientic and/or commercial value that cannot be efficiently obtained through classical computation alone. It will approach this through a unique-to-QMI coordinated effort to develop novel strategies for using present day (NISQ-era) QC hardware. We will focus on:
- Fundamental quantum computing theory relevant to the potential use of symmetry and topological properties of quantum states
- The experimental realization of novel quantum hardware designed to carry out special purpose quantum simulations, and
- The integration of 1 and 2, along with conventional quantum and classical programming, into a hybrid approach employing Bayesian machine learning. The utility of these novel strategies will be tested by applying them to solve a select set of scientifically important problems, chosen mostly from the field of Quantum Materials.
Once validated in specific applications, the QC techniques we develop should be generally applicable to a wide range of other engineering, economic, medical and materials problems.
Principal Investigators
Robert Raussendorf, Team Lead
SBQMI
UBC Physics & Astronomy
Ian Affleck
SBQMI
UBC Physics & Astronomy
Mona Berciu
SBQMI
UBC Physics & Astronomy
Sarah Burke
SBQMI
UBC Physics & Astronomy
Lukas Chrostowski
SBQMI
UBC Electrical & Computer Engineering
Josh Folk
SBQMI
UBC Physics & Astronomy
Marcel Franz
SBQMI
UBC Physics & Astronomy
Roman Krems
SBQMI collaborator
UBC Chemistry
Joe Salfi
SBQMI Collaborator
UBC Electrical & Computer Engineering
Jeff Young
SBQMI
UBC Physics & Astronomy
Eran Sela
SBQMI Collaborator
University of Tel Aviv
Current Opportunities
We have the following positions open for those interested to become a part of this initiative. The postdoctoral appointments will be for a period of 2 to 3 years and will offer unique career growth opportunities. Please send your inquiries to the relevant principal investigator.
Postdoctoral Opportunities
Responsibilities:
Development of codes for generalization of high-dimensional sparse data with Gaussian processes for applications to extrapolation in the Hamiltonian parameter space and solving inverse problems in quantum physics. Development of new algorithms for generalization with Bayesian machine learning. Coordinating with other postdoctoral fellows and students to apply algorithms and codes to specific quantum materials problems. Coordinating with a research associate on the development and implementation of algorithms for training Gaussian processes with composite kernels on quantum hardware. Development of new theory and algorithms for machine learning based on quantum hardware. Development of user-friendly applications based on the codes produced.
To apply for this position contact: Roman Krems
Responsibilities:
This position will lead fabrication and experiments on a quantum simulator implemented using coupled quantum dots, together with one or more graduate students. The target of the quantum simulation will be to probe phenomenology of the resonating valence bond states both at half-filling of a small two-dimensional lattice, and away from half-filling. Responsibilities include device fabrication, cryogenic experimental design, cryogenic experiments, data analysis, and supervision of graduate students. The postdoc will work closely with a theory postdoc on applying machine learning techniques, with guidance from Krems, Afflect, and Raussendorf.
To apply for this position contact: Joe Salfi
Responsibilities:
Development of novel methods for mapping Fermionic systems to bosons. Collaborations with research associate and other postdoctoral fellows on how the Kondo screening cloud could be seen in a quantum algorithm. Classification of measurement-based quantum computation within the framework of symmetry protected topological order. The candidate should have a background in both quantum information and condensed matter physics; the former covering computational models such as circuit, measurement-based, adiabatic and topological, plus quantum error correction and the stabilizer formalism. The latter should cover fermionic systems and symmetry-protectedtopological order, and topological order. Minimal algorithms. This postdoc position is part of the Quantum Computing Theory (QCT) building pillar, so there is no quantum algorithm development.
To apply for this position contact: Ian Affleck or Robert Rassendorf
Responsibilities:
Work with algorithms examining Kondo, Hubbard, (existing quantum hardware, Adiabatic protocols), symmetry-protected topological (SPT), classical simulation of small-scale algorithms; analysis of operational requirements and decoherence; running small-scale quantum algorithms on the existing platforms for quantum computation. Benchmarking and constraint analysis.
To apply for this position contact: Robert Raussendorf, Ian Affleck, Sarah Burke, or Joe Salfi
Responsibilities:
Algorithmic work with solar energy transducers, quantum emitters, and others. Identifying relevant Hamiltonian models for the specified problems and figuring out how to solve them in various limits. Interfacing with other postdoctoral fellows to incorporate these solutions, and experimental results, into first classical machine learning routines, andeventually into hybrid approaches that utilize quantum algorithms to enhance the machine learning.
To apply for this position contact: Mona Berciu, Jeff Young, Lukas Chrostowski, or Sarah Burke
Research Associate Opportunity
Responsibilities:
This research associate will directly supervise all postdocs and related students associated with this program, through which all quantum algorithms will flow, and have more general responsibility for all personnel hired to carry out this Grand Challenge. The successful candidate will work with the Principal Investigators and other researchers to explore the search space for NISQ era quantum algorithms and to benchmark existing quantum computer hardware, and, to this end, is expected to contribute ideas of their own.
To apply for this position contact: Robert Raussendorf, Mona Berciu, Roman Krems, or Joe Salfi