Kavli Affiliate: K. Birgitta Whaley
| First 5 Authors: Zhibo Yang, Robert L. Kosut, K. Birgitta Whaley, ,
| Summary:
We develop a Hamiltonian switching ansatz for bipartite control that is
inspired by the Quantum Approximate Optimization Algorithm (QAOA), to mitigate
environmental noise on qubits. We illustrate the approach with application to
the protection of quantum gates performed on i) a central spin qubit coupling
to bath spins through isotropic Heisenberg interactions, ii) superconducting
transmon qubits coupling to environmental two-level-systems (TLS) through
dipole-dipole interactions, and iii) qubits coupled to both TLS and a Lindblad
bath. The control field is classical and acts only on the system qubits. We use
reinforcement learning with policy gradient (PG) to optimize the Hamiltonian
switching control protocols, using a fidelity objective defined with respect to
specific target quantum gates. We use this approach to demonstrate effective
suppression of both coherent and dissipative noise, with numerical studies
achieving target gate implementations with fidelities over 0.9999 (four nines)
in the majority of our test cases and showing improvement beyond this to values
of 0.999999999 (nine nines) upon a subsequent optimization by Gradient Ascent
Pulse Engineering (GRAPE). We analyze how the control depth, total evolution
time, number of environmental TLS, and choice of optimization method affect the
fidelity achieved by the optimal protocols and reveal some critical behaviors
of bipartite control of quantum gates.
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