Grant: Alliance Grants
University of British Columbia
$149,000.00
Quantum computing with differentiable quantum transforms
FY 2022-23
Summary
Quantum computing is an interdisciplinary field developing a new type of computer based on quantum mechanics. A number of quantum algorithms exist that are capable of solving socially- and industrially-relevant problems, across a spectrum of domains, that are considered intractable for regular computers. Developing algorithms, in particular those that provide a meaningful speedup, is extremely challenging. Currently, most quantum programming is done by writing software in regular languages, augmented by quantum software libraries. Quantum software enables researchers to go beyond the theory, and prototype and test algorithms on both simulators and real hardware. However, the benefits are not merely one-way: just as the requirements of research inform the development of software, features unlocked by software drive further research.This research is a partnership between UBC and Xanadu Quantum Technologies, a Toronto-based startup that develops the open-source software library PennyLane. PennyLane is tailored for designing algorithms for quantum machine learning through application of autodifferentiation to quantum programs. The software has recently been augmented with a new feature called differentiable quantum transforms. Transforms modify and manipulate quantum programs. Their implementation within the autodifferentiation framework of PennyLane has unlocked the ability to train and learn transforms. Through this partnership, we will explore how this software can help us develop novel methods for compiling and optimizing quantum algorithms, mitigating noise in quantum hardware, and simulating noisy devices. Quantum computers are challenging to build and to scale to large sizes; we have many devices today, however they are considered small, and are quite noisy. The software and methods developed in this research will enable us to reduce the resources required to run and test many quantum algorithms, as well as obtain more accurate results when running on today's devices.
Program
Alliance Grants
Recipient
Location
British Columbia, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
576833-2022
Name nom
Di Matteo, Olivia ON
Department département
Electrical and Computer Engineering
Organization id
2
Institution établissement
University of British Columbia
Province en
British Columbia
Province fr
Colombie-Britannique
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2022
Award amount
149000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1207
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Information, computer and communication technologies
Area of application fr
Technologie de l'information, des ordinateurs et des réseaux de communications
Research subject code
2720
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Computer systems software
Research subject fr
Logiciel de systèmes informatiques
Installment
inst:1;2
Partie
2
Nb partie
2
Application title
Quantum computing with differentiable quantum transforms
Application summary
Quantum computing is an interdisciplinary field developing a new type of computer based on quantum mechanics. A number of quantum algorithms exist that are capable of solving socially- and industrially-relevant problems, across a spectrum of domains, that are considered intractable for regular computers. Developing algorithms, in particular those that provide a meaningful speedup, is extremely challenging. Currently, most quantum programming is done by writing software in regular languages, augmented by quantum software libraries. Quantum software enables researchers to go beyond the theory, and prototype and test algorithms on both simulators and real hardware. However, the benefits are not merely one-way: just as the requirements of research inform the development of software, features unlocked by software drive further research.This research is a partnership between UBC and Xanadu Quantum Technologies, a Toronto-based startup that develops the open-source software library PennyLane. PennyLane is tailored for designing algorithms for quantum machine learning through application of autodifferentiation to quantum programs. The software has recently been augmented with a new feature called differentiable quantum transforms. Transforms modify and manipulate quantum programs. Their implementation within the autodifferentiation framework of PennyLane has unlocked the ability to train and learn transforms. Through this partnership, we will explore how this software can help us develop novel methods for compiling and optimizing quantum algorithms, mitigating noise in quantum hardware, and simulating noisy devices. Quantum computers are challenging to build and to scale to large sizes; we have many devices today, however they are considered small, and are quite noisy. The software and methods developed in this research will enable us to reduce the resources required to run and test many quantum algorithms, as well as obtain more accurate results when running on today's devices.