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Scalable quantum computing relies crucially on high-fidelity entangling
operations. Here we demonstrate that four coupled qubits can operate as a
high-fidelity two-qubit entangling gate that swaps two target qubits and adds a
relative sign on the $\lvert 11 \rangle$ state (ZSWAP). The gate operation is
controlled by the state of two ancilla (control) qubits. The system is readily
implementable with superconducting qubits, using capacitively coupled qubits

We experimentally demonstrate a variation on a Sisyphus cooling technique
that was proposed for cooling antihydrogen. In our implementation, atoms are
selectively excited to an electronic state whose energy is spatially modulated
by an optical lattice, and the ensuing spontaneous decay completes one Sisyphus
cooling cycle. We characterize the cooling efficiency of this technique on a
continuous beam of Sr, and compare it with the case of a Zeeman slower. We

This paper proposes a brain-inspired approach to quantum machine learning
with the goal of circumventing many of the complications of other approaches.
The fact that quantum processes are unitary presents both opportunities and
challenges. A principal opportunity is that a large number of computations can
be carried out in parallel in linear superposition, that is, quantum
parallelism. The challenge is that the process is linear, and most approaches
to machine learning depend significantly on nonlinear processes. Fortunately,

In these lecture notes we give a technical overview of tangent-space methods
for matrix product states in the thermodynamic limit. We introduce the manifold
of uniform matrix product states, show how to compute different types of
observables, and discuss the concept of a tangent space. We explain how to
variationally optimize ground-state approximations, implement real-time
evolution and describe elementary excitations for a given model Hamiltonian.
Also, we explain how matrix product states approximate fixed points of