| Quantum computation holds the potential to bring forth a new age of science and engineering when currently intractable problems can be routinely solved on a quantum computer. Unfortunately, it is very difficult to design a quantum computer that is robust to noise. In this thesis, we present two tools that leverage classical computational power to attack this problem. The first tool, CodeQuest, is an algorithm that computes the optimal quantum subsystem code that can be implemented using an input choice of Pauli measurement; it proves particularly useful for searching through large sets of choices of measurements to see if any of them result in a useful code. The second tool, Matrix Product States, is an ansatz that one can use to compute the lowest-lying eigenspectra of a Hamiltonian by employing the variational method. For each of these tools we describe both the theory behind them and the practice of using them to advance the science of designing robust quantum computers. |
