Machine Learning Enhanced

FROM ZDNET...

IBM is releasing a new module as part of its open-source quantum software development kit, Qiskit, to let developers leverage the capabilities of quantum computers to improve the quality of their machine-learning models.

Qiskit Machine Learning is now available and includes the computational building blocks that are necessary to bring machine-learning models into the quantum space.

Machine learning is a branch of artificial intelligence that is now widely used in almost every industry. The technology is capable of crunching through ever-larger datasets to identify patterns and relationships, and eventually discover the best way to calculate an answer to a given problem.

Researchers and developers, therefore, want to make sure that the software comes up with the most optimal model possible – which means expanding the amount and improving the quality of the training data that is fed to the machine-learning software. This process inevitably comes with higher costs and much longer training times.

Delegating some parts of the process to a quantum computer could resolve these issues, by speeding up the time it takes to train or evaluate a machine-learning model, but also by vastly increasing what is known as the feature space – the collection of features that are used to characterize the data that is fed to the model, for example "gender" or "age" if the system is being trained to recognize patterns about people.  READ MORE

 https://www.zdnet.com/article/ibms-new-tool-lets-developers-add-quantum-computing-power-to-machine-learning/

Machine Learning Struggling

Ben Dickson of TechTalk writes:

When you look at the following short video sequence, you can make inferences about causal relations between different elements. For instance, you can see the bat and the baseball player’s arm moving in unison, but you also know that it is the player’s arm that is causing the bat’s movement and not the other way around. You also don’t need to be told that the bat is causing the sudden change in the ball’s direction.

Likewise, you can think about counterfactuals, such as what would happen if the ball flew a bit higher and didn’t hit the bat.

Such inferences come to us humans intuitively. We learn them at a very early age, without being explicitly instructed by anyone and just by observing the world. But for machine learning algorithms, which have managed to outperform humans in complicated tasks such as go and chess, causality remains a challenge. Machine learning algorithms, especially deep neural networks, are especially good at ferreting out subtle patterns in huge sets of data. They can transcribe audio in real-time, label thousands of images and video frames per second, and examine x-ray and MRI scans for cancerous patterns. But they struggle to make simple causal inferences like the ones we just saw in the baseball video above.

In a paper titled “Towards Causal Representation Learning,” researchers at the Max Planck Institute for Intelligent Systems, the Montreal Institute for Learning Algorithms (Mila), and Google Research, discuss the challenges arising from the lack of causal representations in machine learning models and provide directions for creating artificial intelligence systems that can learn causal representations.  READ MORE

Machine Learning

As reported by Leah Crane:

Machine learning, a process used to train artificial intelligences, can take an extremely long time – but a quantum trick could massively speed things up for tasks involving particles of light called photons.

In reinforcement learning, an algorithm runs through the same problem over and over again and is given a numerical reward only when it reaches the correct answer. That process teaches it to find the correct answer more quickly when pitted against similar problems later on.

Now Valeria Saggio at the University of Vienna in Austria and her colleagues have added a quantum twist to accelerate this process. They set up an experiment involving a photon moving through a wave guide and ending up in one of four possible states. They tasked an AI with making sure the photon ended up in one particular state, and rewarded it for doing so.

In the classical version of this experiment, without any added quantum effects, the AI would only be able to move the photon to one specific state at a time, being rewarded when it made a correct guess. However, in the quantum version of the experiment, the AI could put the photon in a superposition of more than one state. This allowed it to narrow down the correct answer before making a final, classical guess at the goal state.

“Imagine you have a robot that is standing at a crossroads, and the robot has two options – it can go left or it can go right,” says Saggio. “If the robot goes right, it does not receive a reward, but if it goes left it receives a reward. At the next round, the probability of it going left will increase.”

That’s the classical version of the experiment, but the quantum version would allow it to go left and right simultaneously at each guess, requiring far fewer guesses before it learns to always go left. This strategy sped up the learning time of the AI by 63 per cent, from 270 guesses to just 100.