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Installing ProjectQ on supercomputers
We’ve added a guide on installing ProjectQ, a powerful Python quantum-computer simulation framework, on the ARCHER and ARCUS-B supercomputers. Read it here!
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How can we cope with noisy quantum computers?
In recent years, quantum computation has become a hot word in the scientific world and has gradually enterered public view. While quantum computer prototypes have been developed with amazing speed, one should never ignore that they are formed with imperfect controls. Quantum error correction offers a solution: each ‘logical qubit’ is really stored using a group of physical qubits in a specially protected code state – if one of the physical qubits becomes corrupt, it is Read more…
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Useful SLURM commands
We’ve begun compiling a list of useful SLURM commands, which you can view here.
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Sweep parameters in SLURM via python
We’ve added a python script for generating a SLURM submission script which launches multiple jobs to sweep a given set of parameters. Read about it here or view it on github here. Stay tuned for a unix cmd tool, and an in-browser tool.
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Output from C and Python to Mathematica
We’ve added tools for outputting Python and C data to a form Mathematica can read. Read about them here, or view them on Github here and here.
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Linux Memory Profilers in C and Python
We’ve added tools for profiling the memory usage of C and Python program processes in linux. Read about them here, or view them on Github here and here.
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QuNaT ported
We’ve brought over the news posts from qunat.org – the previous home of the research group. You can read these posts after this one.
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Use small modules? OK, but how small?
Many researchers believe that the best way to make a quantum computer is to interlink many small modules. But what does “small” mean? In particular, will be quantum machine be more powerful (or perhaps, more resistant to errors) if it each module is itself a powerful device with 100’s of qubits? Or can we focus on making very simple and small modules that are individually weak, and yet collectively they will be just as powerful? In Read more…
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Quantum annealing
The process called quantum annealing is a hot topic! We’ve posted a preprint which extents recent ideas from an Innsbruck team, for realising such a technology in a powerfully flexible form. The word ‘annealing’ usually refers to a process where a metal is heated and slowly cooled (see image). But in quantum annealing a system is slowly moved from one cold state (actually, the ground state, which is the coldest possible!) to another, and then all its components (‘spins’) are measured. The Read more…
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Quantum Learning with Nokia & Lockheed Martin
A new project on Quantum Optimisation and Machine Learning “QuOpaL” is now underway. Based at the University of Oxford, it’s a joint endeavour between the University, Nokia and Lockheed Martin. The aim of the project is to understand the potential for quantum technology to enhance optimisation and machine learning tasks – these are some of the hardest and most important applications in computer science today. The project will be based here in the QuNaT group and Read more…