| Testing the nested light-cone Bethe equations of the AdS 5 × S 5 superstring University of Calgary | Publication | 2007-05-01 | A. Hentschel, J. Plefka, P. Sundin |
| Ordered measurements of permutationally-symmetric qubit strings University of Calgary | Publication | 2011-02-01 | A. Hentschel, B. C. Sanders |
| Ordered measurements of permutation-invariant qubit strings University of Calgary | Publication | 2011-01-01 | A. Hentschel, B. C. Sanders |
| Learned feedback control mechanisms for a quantum systemThe talk presents an example based introduction on how machine learning can be applied to a quantum system. It starts with illustrating the main concepts of quantum information science and points out essential differences between classical and quantum systems, which are important in designing AI systems.
The main part of the talk is dedicated to the possible application of gravitational wave detection. It is illustrated how machine learning could potentially be used to control a quantum system, which is in our case, an interferometer for gravitational wave detection. It will be shown that in this context, the learning problem is related to learning a decision tree and how evolutionary algorithms could be applied in this particular case. University of Calgary | Presentation | 2008-08-12 | A. Hentschel, B. C. Sanders |
| Machine learning for real-world quantum-enhanced phase estimationOne of the most immediate practical applications of quantum information processing is performing precise quantum measurements. Important examples include the measurement of time with atomic clocks, spatial displacements with optical interferometry, and super-resolved imaging beyond the diffraction limit. Heisenberg\\\'s uncertainty principle provides a fundamental bound on the amount of information a measurement can extract. Measurement schemes employing adaptive feedback constitute a promising strategy for reaching the Heisenberg limit. However, devising adaptive measurement procedures is complicated and often involves clever guesswork.\\r\\n\\r\\nI present an automated technique, based on machine-learning that replaces guesswork by a logical, fully automatic, programmable routine. I explain our method using the example of interferometric phase estimation, which has applications such as atomic clocks and gravitational wave detection. Our algorithm autonomously learns to perform phase estimation based on experimental trial runs, which can be either simulated or performed using a real world experiment. The algorithm does not require prior knowledge about the experiment and is effective even if the quantum system is a black box. Our new technique is robust against loss and decoherence. Furthermore, our algorithm learns to account for systematic experimental imperfections and random noise, thereby making time-consuming error modelling and extensive calibration dispensable. We show that our method outperforms the best known adaptive scheme for interferometric phase estimation. University of Calgary | Presentation | 2010-10-07 | A. Hentschel |
| Artificial intelligence in a quantum worldThe talk presents one of the very first attempts to address the interdisciplinary challenge of combining the concept of artificial intelligence with the potentials of quantum information.
Some
The principled approach to machine learning is through a probabilistic viewpoint. A widely used formalism is Bayesian inference. This technique of belief revision is adapting the degree of belief in a hypothesis depending on the results of ongoing observations. University of Calgary | Presentation | 2008-06-19 | A. Hentschel, B. C. Sanders |
| Using machine learning for measuring and controlling quantum systemSuccess in reliably measuring and controlling quantum systems triggered many technological and scientific breakthroughs recently:
atomic clocks determine time via precise measurements of atomic oscillations; a pure quantum effect is used in current hard drives to read out the stored data; and gravitational wave detectors use interferometers to search for tiny deformations in space caused by gravitational waves.
At the fundamental level, measurement precision is limited by Heisenberg's uncertainty principle but even reaching a precision close to the Heisenberg bound is far beyond existing technology because of source and detector limitations. Adaptive measurement strategies are promising because they can greatly reduce the technological requirements.
However, finding good adaptive protocols, even for simple quantum systems, is very hard and often involves clever guesswork.
Fortunately, the area of artificial intelligence suggests a promising approach.
The advantage of machine learning is that the program learns from its
own performance and tries to devise better problem-solving strategies for the future.
In my talk, I will describe a way that machine learning can be used to devise adaptive quantum measurement and quantum control protocols. I will explain our technique using the example of measuring an interferometric phase shift, which is important for applications such as gravitational wave detection, where the wave imposes an unknown phase difference between the two arms of a Mach-Zehnder Interferometer.
University of Calgary | Presentation | 2009-06-25 | A. Hentschel, B. C. Sanders |
| An efficient algorithm for optimizing adaptive quantum metrology processes University of Calgary | Publication | 2011-01-01 | A. Hentschel, B. C. Sanders |
