Quantum Programming News
Ultrafast chemical reactions are difficult to simulate because they involve entangled, many-body wavefunctions whose computational complexity grows rapidly with molecular size. In photochemistry, the breakdown of the Born-Oppenheimer approximation further complicates the problem by entangling
Atos has just introduced Q-score, a new quantum metrics, applicable to all programmable quantum processors. Atos’ Q-score measures a quantum system’s effectiveness at handling real-life problems, those which cannot be solved by traditional computers, rather
A team at Amazon has presented a comprehensive architectural analysis for a fault-tolerant quantum computer based on cat codes concatenated with outer quantum error-correcting codes. For the physical hardware, they have proposed a system of
The UK startup Riverlane has just announced that the initial version of its quantum operating system Deltaflow.OS, called ‘Deltaflow-on-ARTIQ’, is freely available to the public. This quantum operating system is both hardware and platform agnostic. In
In 2003, with “Playing Pool with π“, G. Galperin invented an extraordinary method to learn the digits of π by counting the collisions of billiard balls. Adam R. Brown at Google has demonstrated an exact isomorphism between Galperin’s
Parametrized quantum optical circuits are a class of quantum circuits in which the carriers of quantum information are photons and the gates are optical transformations. Classically optimizing these circuits is challenging due to the infinite
Researchers at Berkeley present a quantum eigenstate filtering algorithm that allows to efficiently prepare a target eigenstate of a given Hamiltonian to high precision under reasonable assumptions. They have applied this algorithm to the quantum
Convolutional Neural Network (CNN) is a popular model in computer vision and has the advantage of making good use of the correlation information of data. However, CNN is challenging to learn efficiently if the given
Pioneer theoretical works suggest that quantum Generative Adversarial Networks (GANs) may exhibit a potential exponential advantage over classical GANs. However, it remains elusive whether quantum GANs implemented on near-term quantum devices can actually solve real-world
Platforms of Rydberg atoms have been proposed as promising candidates to solve some combinatorial NP-hard optimization problems. A team of scientists at Atos Quantum Lab has computed quantitative requirements on the system sizes and noise
