Hybrid quantum–classical systems make it possible to utilize existing quantum computers to
their fullest extent. Within this framework, parameterized quantum circuits can be regarded …

Quantum neural network architectures that have little to no inductive biases are known to
face trainability and generalization issues. Inspired by a similar problem, recent …

As quantum computers are based on the laws of quantum mechanics, they are capable of
solving certain problems faster than their classical counterparts. However, quantum …

Quantum computers have the potential to solve certain problems faster than classical
computers. However, loading data into a quantum computer is not trivial. To load the data, it …

We present QUEST, a procedure to systematically generate approximations for quantum
circuits to reduce their CNOT gate count. Our approach employs circuit partitioning for …

We present an algorithm for compiling arbitrary unitaries into a sequence of gates native to a
quantum processor. As CNOT gates are error-prone for the foreseeable Noisy-Intermediate …

One of the main quests in quantum metrology is to attain the ultimate precision limit with
given resources, where the resources are not only of the number of queries, but more …

Quantum amplitude estimation (QAE) can achieve a quadratic speedup for applications
classically solved by Monte Carlo simulation. A key requirement to realize this advantage is …

We present a topology aware quantum synthesis algorithm designed to produce short
circuits and to scale well in practice. The main contribution is a novel representation of …

We present a quantum algorithm to solve systems of linear equations of the form Ax= b,
where A is a tridiagonal Toeplitz matrix and b results from discretizing an analytic function …