Quantum-enhanced metrology aims to estimate an unknown parameter such that the precision scales better than the shot-noise bound. Single-shot adaptive quantum-enhanced metrology (AQEM) is a promising approach that uses feedback to tweak the quantum process according to previous measurement outcomes. Techniques and formalism for the adaptive case are quite different from the usual non-adaptive quantum metrology approach due to the causal relationship between measurements and outcomes. We construct a formal framework for AQEM by modeling the procedure as a decision-making process, and we derive the imprecision and the Cram\'{e}r-Rao lower bound with explicit dependence on the feedback policy. We also explain the reinforcement learning approach for generating quantum control policies, which is adopted due to the optimal policy being non-trivial to devise. Applying a learning algorithm based on differential evolution enables us to attain imprecision for adaptive interferometric phase estimation, which turns out to be SQL when non-entangled particles are used in the scheme.

This meetup is a gathering place for professionals who use or (or want to learn about using) machine learning to solve messy optimization problems where "hard coded" solutions like deterministic grammars and rules-based systems just don't cut it. If you have a working expertise in machine learning, suspect that you may have a problem where you need to develop a program that "learns" from the data to provide an adequate solution, or you just want to learn more about machine learning and how it might benefit you, this is the place to be! Regular meetup topics will run the gauntlet of supervised, unsupervised, and reinforcement learning approaches and range from natural language processing to computer vision and everything in between.

The dreaded time of day when traffic conditions seem bent on making you late. As your car slowly creeps in line behind countless others stuck at a stop light, you think to yourself, "Why aren't these lights changing faster?" Traffic control scientists have long tried to solve this signaling problem. Unfortunately, the complexity of traffic situations has made the job extremely hard. A recent study suggests that machines can learn how to plan traffic signals just right to reduce wait times and make traffic queues shorter.

The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work we propose an approach for the systematic treatment of machine learning, from the perspective of quantum information. Our approach is general and covers all three main branches of machine learning: supervised, unsupervised and reinforcement learning. While quantum improvements in supervised and unsupervised learning have been reported, reinforcement learning has received much less attention.

A curated list of resources dedicated to reinforcement learning. We are looking for more contributors and maintainers!

Rush hour--the dreaded time of day when traffic conditions seem bent on making you late. As your car slowly creeps in line behind countless others stuck at a stop light, you think to yourself, "Why aren't these lights changing faster?" Traffic control scientists have long tried to solve this signaling problem. Unfortunately, the complexity of traffic situations makes the job extremely hard. A recent study suggests that machines can learn how to plan traffic signals just right to reduce wait times and make traffic queues shorter.

In March this year, Microsoft CEO Satya Nadella talked about the industry trend of using human language more pervasively for interaction with computing devices, a trend he called "conversation as a platform." He also announced several bot initiatives, including the company's bot framework. In April, Facebook launched its Messenger platform with bots. Then, in May, Google announced its attempt to develop AI-powered bots, called Google Assistant. Since then, bots have been widely regarded as a new user interface (UI) to fundamentally change how computing will be experienced by people.

Most learning algorithms are not invariant to the scale of the function that is being approximated. We propose to adaptively normalize the targets used in learning. This is useful in value-based reinforcement learning, where the magnitude of appropriate value approximations can change over time when we update the policy of behavior. Our main motivation is prior work on learning to play Atari games, where the rewards were all clipped to a predetermined range. This clipping facilitates learning across many different games with a single learning algorithm, but a clipped reward function can result in qualitatively different behavior. Using the adaptive normalization we can remove this domain-specific heuristic without diminishing overall performance.

Basically I want to have some hands on experience through small projects. You may want to start with OpenAI gym. Personally, I find the n-armed bandit problem quite illustrative. Here's a great blog post code to get you started. Definitely this, it is as simple as it gets.

In March this year, Microsoft CEO Satya Nadella talked about the industry trend of using human language more pervasively for interaction with computing devices, a trend he called "conversation as a platform." He also announced several bot initiatives, including the company's bot framework. In April, Facebook launched its Messenger platform with bots. Then, in May, Google announced its attempt to develop AI-powered bots, called Google Assistant. Since then, bots have been widely regarded as a new user interface (UI) to fundamentally change how computing will be experienced by people.