Leakage Benchmarking for Universal Gate Sets.

Errors are common issues in quantum computing platforms, among which leakage is one of the most-challenging to address. This is because leakage, i.e.

Quantum Instruction Set Design for Performance.

A quantum instruction set is where quantum hardware and software meet. We develop characterization and compilation techniques for non-Clifford gates to accurately evaluate its designs. Applying these techniques to our fluxonium processor, we show that replacing the iSWAP gate by its square root SQiSW leads to a significant performance boost at almost no cost.

Fluxonium: An Alternative Qubit Platform for High-Fidelity Operations.

Superconducting qubits provide a promising path toward building large-scale quantum computers. The simple and robust transmon qubit has been the leading platform, achieving multiple milestones. However, fault-tolerant quantum computing calls for qubit operations at error rates significantly lower than those exhibited in the state of the art.

Efficient parallelization of tensor network contraction for simulating quantum computation.

We develop an algorithmic framework for contracting tensor networks and demonstrate its power by classically simulating quantum computation of sizes previously deemed out of reach. Our main contribution, index slicing, is a method that efficiently parallelizes the contraction by breaking it down into much smaller and identically structured subtasks, which can then be executed in parallel without dependencies. We benchmark our algorithm on a class of random quantum circuits, achieving greater than 10 times acceleration over the original estimate of the simulation cost.