Near-term quantum computers are expected to work in an environment where each
operation is noisy, with no error correction. Therefore, quantum-circuit optimizers are applied …

Parameterized circuit instantiation is a common technique encountered in the generation of
circuits for a large class of hybrid quantum-classical algorithms. Despite being supported by …

We propose a scalable, hierarchical qubit map** and routing algorithm that harnesses the
power of circuit synthesis. First, we decompose large circuits into subcircuits small enough to …

Quantum Approximate Optimization Algorithm (QAOA) is one of the leading candidates for
demonstrating the quantum advantage using near-term quantum computers. Unfortunately …

Quantum Computing (QC) promises computational speedup over classic computing.
However, noise exists in near-term quantum computers. Quantum software testing (for …

Quantum process learning is emerging as an important tool to study quantum systems.
While studied extensively in coherent frameworks, where the target and model system can …

Quantum programs are written in high-level languages, whereas quantum hardware can
only execute low-level native gates. To run programs on quantum systems, each high-level …

The past decade witnessed exciting advances in quantum computing (QC) platforms. For
example, superconducting qubit systems of over a hundred qubits have been developed …

This paper presents the Pauli-based Circuit Optimization, Analysis, and Synthesis Toolchain
(PCOAST), a framework for quantum circuit optimizations based on the commutative …

Designing performant and noise-robust circuits for Quantum Machine Learning (QML) is
challenging---the design space scales exponentially with circuit size, and there are few well …