Abstract Machine learning has achieved dramatic success in a broad spectrum of
applications. Its interplay with quantum physics may lead to unprecedented perspectives for …

Gate-model quantum computers promise to solve currently intractable computational
problems if they can be operated at scale with long coherence times and high-fidelity logic …

Controlling and programming quantum devices to process quantum information by the unit
of quantum dit, ie, qudit, provides the possibilities for noise-resilient quantum …

Quantum-photonic chips, which integrate quantum light sources alongside active and
passive optical elements, as well as single-photon detectors, show great potential for …

The efficient calculation of Hamiltonian spectra, a problem often intractable on classical
machines, can find application in many fields, from physics to chemistry. We introduce the …

Quantum phase estimation (QPE) is the workhorse behind any quantum algorithm and a
promising method for determining ground state energies of strongly correlated quantum …

It is usually assumed that a quantum computation is performed by applying gates in a
specific order. One can relax this assumption by allowing a control quantum system to switch …

Machine learning, a branch of artificial intelligence, learns from previous experience to
optimize performance, which is ubiquitous in various fields such as computer sciences …

Quantum phase estimation is a fundamental subroutine in many quantum algorithms,
including Shor's factorization algorithm and quantum simulation. However, so far results …

We propose a hybrid quantum algorithm based on the Harrow-Hassidim-Lloyd (HHL)
algorithm for solving a system of linear equations. In this paper, we show that our hybrid …