We propose how to realize high-fidelity quantum storage using a hybrid quantum architecture including two coupled flux qubits and a nitrogen-vacancy center ensemble (NVE). One of the flux qubits is considered as the quantum-computing processor and the NVE serves as the quantum memory. By separating the computing and memory units, the influence of the quantum-computing process on the quantum memory can be effectively eliminated, and hence the quantum storage of an arbitrary quantum state of the computing qubit could be achieved with high fidelity. Furthermore, the present proposal is robust with respect to fluctuations of the system parameters, and it is experimentally feasible with currently available technology.

We investigate the optimal control problem for a non-Markovian open, dissipative quantum system. Optimal control using the Pontryagin maximum principle is specifically derived. The influences of ohmic reservoir with Lorentz-Drude regularization are numerically studied in a two-level system under the following three conditions: ${\omega }_0⪡{\omega }_c$, ${\omega }_0\approx {\omega }_c$, or ${\omega }_0⪢{\omega }_c$, where ${\omega }_0$ is the characteristic frequency of the quantum system of interest, and ${\omega }_c$ the cutoff frequency of the ohmic reservoir. The optimal control process shows its remarkable influences on the decoherence dynamics. The temperature is a key factor in the decoherence dynamics. We analyze the optimal decoherence control in high temperature, intermediate temperature, and low temperature reservoirs, respectively. It implies that designing some engineered reservoirs with the controlled coupling and state of the environment can slow down the decoherence rate and delay the decoherence time. Moreover, we compare the non-Markovian optimal decoherence control with the Markovian one and find that with non-Markovian the engineered artificial reservoirs are better than with the Markovian approximation in controlling the open, dissipative quantum system’s decoherence.

Quantum computing enables quantum neural networks (QNNs) to have great potential to surpass artificial neural networks. The powerful generalization of neural networks is attributed to nonlinear activation functions. Although various models related to QNNs have been developed, they are facing the challenge of merging the nonlinear, dissipative dynamics of neural computing into the linear, unitary quantum system. In this paper, we establish different quantum circuits to approximate nonlinear functions and then propose a generalizable framework to realize any nonlinear quantum neuron. We present two quantum neuron examples based on the proposed framework. The quantum resources required to construct a single quantum neuron are polynomial in function of the input size. Finally, both IBM Quantum Experience results and numerical simulations illustrate the effectiveness of the proposed framework.

Along with the continuous breakthroughs and innovations in blockchain technology, various blockchain application scenarios are emerging, such as healthcare, e‐voting, and Internet of Things. However, in general, these applications need to be supported by blockchain systems with different characteristics. Different blockchain systems have significant differences in architecture, security, efficiency, and so on, which makes the exchange of information between applications have certain barriers. Therefore, the research of blockchain cross‐chain technology brings hope for breaking the information barriers between blockchain systems. This paper describes the basic principles and latest research of three mainstream cross‐chain technologies; then analyzes three innovative cross‐chain solutions of cross‐chain routing, smart contracts, and multi‐chain consensus; furthermore, discusses the application and security risks of cross‐chain technologies, and finally summarizes and outlooks on cross‐chain technologies.

We consider the feedback stabilization of Rabi oscillations in a superconducting qubit which is coupled to a microwave readout cavity. The signal is readout by homodyne detection of the in-phase quadrature amplitude of the weak-measurement output. By multiplying the time-delayed Rabi reference, one can extract the signal, with maximum signal-to-noise ratio, from the noise current. We further track and stabilize the Rabi oscillations by using Lyapunov feedback control to properly adjust the input Rabi drives. Theoretical and simulation results illustrate the effectiveness of the proposed control law. DOI: http://dx.doi.org/10.1103/PhysRevA.88.063823 ©2013 American Physical Society