Quandary provides optimal control for open and closed quantum systems.
This repository contains example python codes and configurations for Quandary.
See Quandary for instructions on running through python.
Gate optimization CNOT:
- Optimizes for a CNOT gate on two coupled qubits each modelled with 2 energy levels.
- T = 200ns, time step size = 0.1ns
- 'cnot.cfg': Runs a closed-system (Schroedinger eq.) optimization using random initial control parameters. Can be run on up to 4 cores (one for each initial basis state)
- 'cnot_FWD_optimized.cfg': Evaluates the fidelity of the control parameters stored in 'params_optimized.dat' by forward simulation (Schroedinger's equation)
- 'cnot_FWD_optimized_withnoise.cfg': Same as above, but simulates with Lindblads master equation (with decoherence).
Gate optimization SWAP02:
- Considers a qudid modelled with 3 essential energy levels and one guard level
- Optimizes for a SWAP02 gate that swaps the |0> with the |1> state.
- Schroedinger solver (-> closed-system optimization)
- Can be run on up to 3 compute cores (one for each initial condition)
State-to-state:
- Optimized for pulses that transfer the ground state of a 2-level qubit with one guard level to the maximally mixed state [1/sqrt(2), 1/sqrt(2)].
- Schroedinger's solver (closed-system optimization)
- Can run on one core (one initial condition)
Quandary is distributed under the terms of the MIT license. All new contributions must be made under this license. See LICENSE, and NOTICE, for details.
SPDX-License-Identifier: MIT