Spaces Shinagawa was packed at our ML TOKYO TALKS event – Quantum Computing/Quantum Machine Learning edition in collaboration with the Association of Italian Researchers in Japan (AIRJ).

We were fortunate to welcome two experts in the field of Quantum Computing: Mattia Fiorentini (Head of Machine Learning and Quantum Algorithms at Cambridge Quantum Computing) and Nathan Shammah (Postdoctoral Research Scientist, Theoretical Quantum Physics Laboratory, RIKEN Japan).

**Open-source for quantum technologies, Nathan Shammah**

*Find the slides and many other MLT talks here, or download the PDF.*

In past decades there has been considerable advancement both in theory and in experiments in research laboratories around the world. More recently, there has been an increased effort toward spinning out technology from our scientific understanding of quantum theory. The software stack interfacing with these early prototypes of quantum machines is heavily relying on the open-source paradigm. Nathan will review software and hardware trends, and give an intro to quantum information processing. He’ll speak about the quantum toolbox in Python (QuTiP), general community efforts aimed at creating a sustainable ecosystem for open-source projects and provide some examples of how QC and ML are beginning to interact with each other.

**NATHAN SHAMMAH** is a Postdoctoral Research Scientist at RIKEN, Japan’s national lab, in the Quantum Physics Theory Lab, where he investigates the dynamics of open quantum systems. Lead developer of QuTiP, the quantum toolbox in Python, he also writes the quantum tech newsletter.

**Recent applications of quantum algorithms to the financial service industry, Mattia Fiorentini**

Quantum computing (QC) offers novel avenues to process information by increasing both the amount of data we can process and the sophistication of the algorithms and model we employ for business applications. Quantum algorithms have been discovered to solve combinatorial optimisation problems that can find better allocation in complex investment decisions and even use entirely new optimisation paradigms such as quantum semi-definite programming. QC can simulate complex stochastic processes natively and therefore more efficiently, which can help for modelling option paths, and also accelerate Monte Carlo sampling, which is vital for risk management.

**MATTIA FIORENTINI** is the Head of Machine Learning and Quantum Algorithms at Cambridge Quantum Computing. He applies deep learning to algorithmic trading and financial time-series forecasting. With his team, he has implemented many intra-day trading strategies in diverse financial markets. His QC research involves high-impact applications of quantum algorithm in the fields of machine learning and optimisation, specifically targeted at noisy intermediate-scale quantum computers.

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