Ansatz Learning for Quantum Circuit Optimization

The goal of quantum circuit optimization is to reduce the number of operations and the critical path depth of computation in quantum circuits. Techniques such as unitary synthesis and quantum circuit instantiation have proven to be effective methods of optimizing quantum programs. These bottom-up optimization techniques begin with a blank circuit and add gates until the original target circuit or unitary is implemented to within some very small approximation error. The run time of such algorithms typically scale exponentially with the number of qubits or width of the circuits. Much of this run time is dedicated to finding the placement of gates within the circuit. This project explores the use of machine learning in identifying good quantum circuit ans ¨atze for the purposes of quantum circuit optimization.