Quantum gates by coupled quantum dots and measurement procedure in Si MOSFET
Tetsufumi Tanamoto
备注:
3 pages, 2 figures, use revtex.sty. 'The Eleventh International Conference on Nonequilibrium Carrier Dynamics in Semiconductors (HCIS-11)',July 19-23, 1999
We investigated the quantum gates of coupled quantum dots, theoretically, when charging effects can be observed. We have shown that the charged states in the qubits can be observed by the channel current of the MOSFET structure.
A single electron spin in a double quantum dot in a magnetic field is considered in terms of a four-level system. By describing the electron motion between the potential minima by spin-conserving tunneling and spin flip caused by a spin-orbit coupling, we inversely engineer faster-than-adiabatic state manipulation operations based on the geometry of four-dimensional (4D) rotations. In particular, we show how to transport a qubit among the quantum dots performing simultaneously a required spin rotation.
Motivated by recent experiments of Zajac et al. [arXiv:1708.03530], we theoretically describe high-fidelity two-qubit gates using the exchange interaction between the spins in neighboring quantum dots subject to a magnetic field gradient. We use a combination of analytical calculations and numerical simulations to provide the optimal pulse sequences and parameter settings for the gate operation. We present a novel synchronization method which avoids detrimental spin flips during the gate operation and provide details about phase mismatches accumulated during the two-qubit gates which occur due…
In this paper, we present a machine learning framework to design high-fidelity multi-qubit gates for quantum processors based on quantum dots in silicon, with qubits encoded in the spin of single electrons. In this hardware architecture, the control landscape is vast and complex, so we use the deep reinforcement learning method to design optimal control pulses to achieve high fidelity multi-qubit gates. In our learning model, a simulator models the physical system of quantum dots and performs the time evolution of the system, and a deep neural network serves as the function approximator to lea…