Why is it worth the hassle? Because this ability – to know, and therefore exploit, a quantum state – is crucial to quantum computing. (“This is partly due to the uncertainty principle and mostly just due to the nature of quantum mechanics itself,” noted Perimeter PhD student Barak Shoshany in an answer on Quora.) However, you can only extract some information at any one time. The state of a quantum system contains all the information about that system.

The team applied an artificial intelligence (AI) to a particular problem in quantum computing: working out the state of a quantum device using only snapshots of data gleaned from experimental measurements. This pas de deux recently took one more step thanks to a new paper published in Nature Physics by researchers at Perimeter Institute and the University of Waterloo, with collaborators at ETH Zurich, Microsoft Station Q, D-Wave Systems, and the Vector Institute for Artificial Intelligence.

It takes the best in classical computing to advance quantum capabilities, yet each leap in quantum computation demands more of its classical partner. The effort to advance these different technologies – which both fuel and rely on each other – resembles something like a dance. We use classical computation and simulations to develop and design today’s nascent quantum devices, which are then nested within a much larger framework of classical computers. In reality, though, the two are inextricably linked. In popular culture, quantum computing is often painted as an ultra-powerful technology that will first outrace, and then replace, its classical counterpart. Moreover, QBIP also demonstrated its economy regarding the required resources needed for its proper function and its great robustness (immunity to noise), among other advantages, in fact, without any exceptions.This article originally appeared in “ Inside the Perimeter” This study demonstrated the practical infeasibility in the implementation of FRQI and NEQR on a physical quantum computer (QPU), while QBIP has proven to be extremely successful on: a) the four main quantum simulators on the cloud, b) two QPUs, and c) optical circuits from three labs.

These analyses and demonstrations are automatically extended to all variants of FRQI and NEQR. All conspicuous technical items are considered in this comparison for a complete analysis: i) performance as Classical-to-Quantum (Cl2Qu) interface, ii) characteristics of the employed qubits, iii) sparsity of the used internal registers, iv) number and size of the required registers, v) quality in the outcomes recovering, vi) number of required gates and its consequent accumulated noise, vi) decoherence, and vii) fidelity. In this paper, three tecniques of internal image-representation in a quantum computer are compared: Flexible Representation of Quantum Images (FRQI), Novel Enhanced Quantum Representation of digital images (NEQR), and Quantum Boolean Image Processing (QBIP).

Compact machines quantum entangler