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[1803.10908] Matrix Product Operators for Sequence to Sequence Learning
The method of choice to study one-dimensional strongly interacting many body quantum systems is based on matrix product states and operators. Such method allows to explore the most relevant, and numerically manageable, portion of an exponentially large space. It also allows to describe accurately correlations between distant parts of a system, an important ingredient to account for the context in machine learning tasks. Here we introduce a machine learning model in which matrix product operators are trained to implement sequence to sequence prediction, i.e. given a sequence at a time step, it allows one to predict the next sequence. We then apply our algorithm to cellular automata (for which we show exact analytical solutions in terms of matrix product operators), and to nonlinear coupled maps. We show advantages of the proposed algorithm when compared to conditional random fields and bidirectional long short-term memory neural network. To highlight the flexibility of the algorithm, we also show that it can readily perform classification tasks.
representation  machine-learning  to-understand  matrices  quantum-computing  classification  algorithms 
yesterday by Vaguery
Graduate Student Solves Quantum Verification Problem | Quanta Magazine
In 2016, while working on a different problem, Mahadev and Vazirani made an advance that would later prove crucial. In collaboration with Paul Christiano, a computer scientist now at OpenAI, a company in San Francisco, they developed a way to use cryptography to get a quantum computer to build what we’ll call a “secret state” — one whose description is known to the classical verifier, but not to the quantum computer itself. ...

In 2017, Mahadev figured out how to build the trapdoor functions at the core of the secret-state method by using a type of cryptography called Learning With Errors (LWE). Using these trapdoor functions, she was able to create a quantum version of “blind” computation, by which cloud-computing users can mask their data so the cloud computer can’t read it, even while it is computing on it. And shortly after that, Mahadev, Vazirani and Christiano teamed up with Vidick and Zvika Brakerski (of the Weizmann Institute of Science in Israel) to refine these trapdoor functions still further, using the secret-state method to develop a foolproof way for a quantum computer to generate provably random numbers.
Quantum-computing  Urmila-Mahadev 
5 days ago by quant18
Quantum Computing in the NISQ era and beyond – Quantum
Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future. Quantum computers with 50-100 qubits may be able to perform tasks which surpass the capabilities of today's classical digital computers, but noise in quantum gates will limit the size of quantum circuits that can be executed reliably. NISQ devices will be useful tools for exploring many-body quantum physics, and may have other useful applications, but the 100-qubit quantum computer will not change the world right away - we should regard it as a significant step toward the more powerful quantum technologies of the future. Quantum technologists should continue to strive for more accurate quantum gates and, eventually, fully fault-tolerant quantum computing.
surveys  quantum-computing 
9 weeks ago by arsyed

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