Classical information loading is an essential task for many processing quantum algorithms, constituting a cornerstone in the field of quantum machine learning. In particular, the embedding techniques based on Hamiltonian simulation techniques enable the loading of matrices into quantum computers.…
Demonstrations of quantum advantage for certain sampling problems has generated considerable excitement for quantum computing and has further spurred the development of circuit-model quantum computers, which represent quantum programs as a sequence of quantum gates acting on a finite number…
Quantum computing makes use of quantum resources provided by the underlying quantum nature of matter to enhance classical computation. However, current Noisy Intermediate-Scale Quantum (NISQ) era in quantum computing is characterized by the use of quantum processors comprising from a…
Counterfactual quantum key distribution protocols allow two sides to establish a common secret key using an insecure channel and authenticated public communication. As opposed to many other quantum key distribution protocols, part of the quantum state used to establish each…
The Quantum Approximate Optimisation Algorithm was proposed as a heuristic method for solving combinatorial optimisation problems on near-term quantum computers and may be among the first algorithms to perform useful computations in the post-supremacy, noisy, intermediate scale era of quantum…
Prediction of financial crashes in a complex financial network is known to be an NP-hard problem, i.e., a problem which cannot be solved efficiently with a classical computer. We experimentally explore a novel approach to this problem by using a…