In this project the intern will work with an expert team from the company Xanadu, to explore new computational methods and approaches which could be helpful for optimizing hybrid calculations involving both classical and quantum computing combined together.
This project is focused on improving the theory, software and scope of applications of quantum computational chemistry. The intern will work with the company research team to further develop new theoretical methods which are under development, implement them into code as part of the existing open-source PennyLane package, and run tests for modelling the vibrational properties of a specific sample molecule. The intern will gain valuable experience working with a leading company in the field of quantum computing and will be able to lead the preparation of a journal-ready article.
The proposed project investigates an approach to solve difficult physics problems, which are too computationally intensive for standard computers, using Xanadu’s near-term quantum computers. The goal of the project is to create a simulation tool that harnesses the exponential increase in efficiency offered by quantum computers to simulate the movement of particles and the subsequent emitted radiation at the nanometer scale. These simulations could have practical implications for experiments involving optical and laser physics and could lead to further insights concerning atomic behaviour.
Current quantum computers are in the “NISQ”, or Noisy-Intermediate-Scale-Quantum regime. The true potential of quantum computing will only be realized when noise levels are reduced or controlled, and large scale is achieved. Xanadu’s approach is to use photonic technology as the building blocks of their machines. This project addresses two related questions concerning the future development of these machines: A - In which conditions does a photonic quantum computer reach quantum advantage (demonstrating large speedups compared to today’s most powerful conventional computers)?
With the small qubit devices now becoming accessible across various hardware and cloud platforms, it is imperative to find useful tasks for the devices to perform. Such devices are known as NISQ - Noisy Intermediate Scale Quantum - devices. In this regime of a few qubits, we expect the physical qubits to be noisy in the absence of sophisticated error-correction or fault-tolerant coding techniques. Therefore, it is important to understand and identify qubit algorithms that are of interest in the immediate future or near term, capable of running usefully on NISQ devices.
Continuous variable (CV) encodings in photonic systems are emerging as one of the most promising avenues to near term, practical, quantum computing. In order for a CV quantum computer to outperform its classical counterparts it requires the integration of at least one “non-Gaussian” element.
Over the past 2-3 years, commercial quantum computing hardware has begun to come online. While emerging quantum processing devices (QPUs) are still small and noisy compared to ideal quantum hardware, they are nevertheless expected to demonstrate quantum supremacy soon. During the same period, quantum machine learning (QML) has emerged as a rapidly expanding research field, perceived as one of the most promising algorithmic paradigms for near-term quantum computers. In this project, the candidate will leverage their skills in machine learning to carry out research in QML.
Qubits are fundamental units for quantum computation. Photonics is a promising physical medium to realize large-scale quantum computation. One proposal to realize photonic qubits was proposed by Gottesman, Kitaev and Preskill (GKP). Here, the logical qubit is encoded into states of a bosonic mode or a quantum harmonic oscillator. It is expected that such a procedure will lead to a better quality and number of qubits.
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