The future development of quantum technologies relies on creating and manipulating
quantum systems of increasing complexity, with key applications in computation, simulation …

Fuelled by increasing computer power and algorithmic advances, machine learning
techniques have become powerful tools for finding patterns in data. Quantum systems …

Solving linear systems of equations is a frequently encountered problem in machine
learning and optimization. Given a matrix A and a vector b the task is to find the vector x such …

Recovering an unknown Hamiltonian from measurements is an increasingly important task
for certification of noisy quantum devices and simulators. Recent works have succeeded in …

We introduce a new method called rejection filtering that we use to perform adaptive
Bayesian phase estimation. Our approach has several advantages: it is classically efficient …

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

We study the problem of learning the Hamiltonian of a many-body quantum system from
experimental data. We show that the rate of learning depends on the amount of control …

Hamiltonian learning, as an important quantum machine learning technique, provides a
significant approach for determining an accurate quantum system. This paper establishes …

Benchmarking is how the performance of a computing system is determined. Surprisingly,
even for classical computers this is not a straightforward process. One must choose the …

Efforts to scale-up quantum computation have reached a point where the principal limiting
factor is not the number of qubits, but the entangling gate infidelity. However, the highly …