About me#
I am Daniel Mo Houshmand: an artist, applied mathematician and founder of QDaria, a quantum-computing company in Oslo. I came to quantum computing through fluid mechanics and art, and this site is where I work through what I learn.
Background#
I am an artist at heart. My journey began in art and grew into an appreciation of where art and science meet, above all in fluid art and fluid mechanics: the way acrylic paint flows, cells and spreads on a canvas is fluid mechanics you can watch. Alongside it run applied mathematics, physics, data science and, now, quantum computing.
I hold an MSc in Applied Mathematics, Mechanics and Numerical Physics from the University of Oslo, a BSc in Civil Engineering from Oslo and Akershus University College of Applied Sciences (HiOA, now OsloMet), and a BA in Design and Modelling Techniques from ESMOD Bordeaux/Oslo. I have since completed specialisations in mathematics for machine learning, quantum machine learning and quantum computer hardware; the last two are the backbone of the quantum parts of this site.
Work#
I proofread for Professor Hans Petter Langtangen, among other things his book Finite Difference Computing with Partial Differential Equations: A Modern Software Approach.
In 2017 I joined the AI Lab of the Norwegian Labour and Welfare Administration (NAV) as one of its first data scientists. I returned to NAV in 2022, working on data handling, infrastructure design and full-stack development. There I began Zipminator, a tool for handling sensitive data securely; it has since grown into the post-quantum encryption software described in the Zipminator chapter.
Ventures#
Between 2017 and 2021 I worked on ventures in quantum technologies. In 2018, as CTO of iProud, backed by Xplorico and Malaysian officials, we set out to build a personal coach on hybrid machine-learning algorithms. From 2019, as CEO of QDaria, Norway’s first quantum-computer company, the aim was a quantum computer for problems classical machines cannot reach, organised in four departments: quantum hardware engineering, a quantum AI lab, a synthetic-biology lab and an investment hub, with health technology (bioinformatics, diagnosis, prediction and drug discovery) as the first application.
QDaria is active today. From Oslo it works on topological quantum computing with Fibonacci anyons, and on products that put quantum methods to work: Zipminator for post-quantum encryption, Qm9 for finance, QDiana for education, QNilaya for health and QMikeAI for quantum-enhanced fluid dynamics and high-performance computing. More at qdaria.com.
Interests#
I believe interdisciplinary work is where new technologies open up. My own fields are fluid art, fluid mechanics and turbulence, computational science, data science and quantum computing, and this site shows them side by side: paintings next to proofs, notebooks next to device physics.
Teachers#
I have been fortunate to learn from people who set an example of kindness and respect as much as of brilliance. Two of them died far too early, in 2017 and 2019.
Hans Petter Langtangen (1962–2017)#
Hans Petter Langtangen was my professor and an enduring inspiration: an award-winning educator who changed how scientific computing is taught. After his diagnosis with pancreatic cancer in early 2015 he still wrote and finished a series of books on scientific computing for Springer before he died in the autumn of 2017. He believed there were no “stupid” people, that anyone could reach a hard goal with dedication and work, and he made the most self-doubting of us believe it too. He was part of the Centre for Neuroplasticity, and he gave me a lasting love of numerical methods for partial differential equations. I am proud to have proofread some of his writing, including his last book, Finite Difference Computing with Partial Differential Equations. I think of him every day, with grief and with gratitude.
Peter Wittek (1980–2019)#
Peter Wittek of the University of Toronto introduced me to quantum machine learning in 2017, when I had only just begun with machine learning, natural-language processing and topic modelling (latent Dirichlet allocation) as a data-science intern at Norway’s first publicly funded AI lab. My physics background helped me follow his teaching, with considerable effort. He died in a Himalayan avalanche in 2019. I believe our third attempt at a quantum-computing start-up would have succeeded had he still been with us. I remember him for his work in quantum machine learning, and for his generosity in guiding a newcomer through a field that was then completely new, and for his faith in our ambitious plan to build Norway’s first quantum-computer company. The quantum machine learning series here is adapted from his course.
Jon Magne Leinaas#
Jon Magne Leinaas taught me non-relativistic quantum mechanics. Nearly fifty years ago he and Jan Myrheim predicted anyons, particles that are neither fermions nor bosons; experiments confirmed them independently twice during the pandemic. Jon is a member of the Royal Swedish Academy of Sciences, which awards the physics prize, and many consider him and Jan strong candidates for it. Anyons are the theoretical basis of topological quantum computing, which uses them for computation that is protected against errors by its very construction, and fault tolerance is one of the central open problems in the field. That is why the topological chapters on this site get the most space, and why I intend to spend much of my future work there.
Contact#
The quickest way to reach me is through LinkedIn or GitHub. If you spot an error on this site, or see how a page could be clearer, please tell me; the site is better for every correction.