Circuit partitioning is a powerful technique in the Noisy Intermediate Scale Quantum era. This method allows the evaluation of many-qubit algorithms with fewer qubits by writing the expectation...Show moreCircuit partitioning is a powerful technique in the Noisy Intermediate Scale Quantum era. This method allows the evaluation of many-qubit algorithms with fewer qubits by writing the expectation value of an observable as a weighted sum of products of inner products which are obtained by evaluating smaller circuits. However the number of terms in this sum generally scales exponentially with the depth of the circuit. The recently introduced Subset Partition Model only evaluates a subset of these terms. Here we explore the application of the Subset Partition Model for Variational Quantum Eigensolvers. Based on our investigation, we conjecture that this model may not be generally applicable to this Variational Quantum Algorithm due to the breaking of the Variational Principle. In addition, optimizing Partitioned Quantum Circuits is challenging due to the Barren Plateau problem that arises for even small circuit sizes. To address this problem, this thesis proposes a novel optimization algorithm that takes advantage of the additional structure on the cost function induced by circuit partitioning. By utilizing this new algorithm, we demonstrate that we can alleviate the barren plateau problem in the optimization of Partitioned Quantum Circuits.Show less
