International Journal of Quantum Information最新文献

We address the problem of coherent state discrimination in the presence of phase diffusion. We investigate the role of the HYbrid Near-Optimum Receiver (HYNORE) we proposed in [J. Opt. Soc. Am. B40 (2023) 705] in the task of mitigating the noise impact. We prove the HYNORE to be a robust receiver, outperforming the Displacement Photon-Number-Resolving (DPNR) receiver and beating the standard quantum limit in particular regimes. We introduce the maximum tolerable phase noise $\sigma$_max as a figure of merit for the receiver robustness and show that HYNORE increases its value with respect to the DPNR receiver.

Cornerstones of the Cellular Automaton Interpretation of Quantum Mechanics are its ontological states that evolve by permutations, in this way never creating would-be quantum mechanical superposition states. We review and illustrate this with a classical Ising spin chain. It is shown that it can be related to the Weyl equation in the continuum limit. Yet, the model of discrete spins or bits unavoidably becomes a model of qubits by generating superpositions, if only slightly deformed. We study modifications of its signal velocity which, however, do not relate to mass terms. To incorporate the latter, we consider the Dirac equation in 1+1 dimensions and sketch an underlying discrete deterministic “necklace of necklaces” automaton that qualifies as ontological.

Quantum digital signature, as an extension of classical digital signature, has become an important research content in quantum cryptography. Quantum blind signature combines the advantages of classical blind signature and quantum signature, which can ensure the unconditional security of the scheme based on the realization of the blinded signature of the message, and can be effectively applied in many real-world scenarios. This paper uses the four-particle $\chi$ state as a communication channel, combined with quantum teleportation technology to propose a new quantum multi-party blind signature protocol, which has the following characteristics: First, the Toeplitz hash function based on the linear shift register is introduced to blind the message, and the length of the blinded message can be adjusted according to the actual demand to increase the flexibility of the scheme; Second, through multi-party participation, the blind signature of multi-bit messages can be realized, and the signature efficiency can be improved. Compared with other quantum blind signatures, the signature efficiency has been greatly improved; Finally, using the four-particle $\chi$ state as a quantum channel can make the scheme use fewer resources to transmit data and increases the security of the scheme. Through security analysis, it can be seen that the scheme has blindness, nonrepudiation and unforgeability.

For a three-level system monitored by an ancilla, we show that the quantum Zeno effect can be employed to control quantum jump for error correction. Further, we show that we can realize cNOT gate, and effect dense coding and teleportation using a three-level system with an ancilla. We believe that this work paves the way to generalize the control of a qudit.

In this study, we simulated the algorithmic performance of a small neutral atom quantum computer and compared its performance when operating with all-to-all versus nearest-neighbor connectivity. This comparison was made using a suite of algorithmic benchmarks developed by the Quantum Economic Development Consortium. Circuits were simulated with a noise model consistent with experimental data from [Nature604, 457 (2022)]. We find that all-to-all connectivity improves simulated circuit fidelity by $10\%$–$15\%$, compared to nearest-neighbor connectivity.

This paper proposed a quantum state tomography approach based on the extreme learning machine (ELM), which is available in the reconstruction of quantum states via a lightweight neural network. The key step of the proposed tomography approach is to employ the ELM to approximate the complex mapping between the measurement values sequence and the real density matrix. After obtaining the output of the ELM-based estimator, a matrix transformation technique is used to make the network outputs satisfy quantum state constraints. Compared with deep learning-based tomography approaches, our proposed ELM-based approach enables both high-fidelity and high-efficiency quantum state tomography with only one training process under the condition of very few numbers of training samples, network layers and hidden layer nodes. In addition, the proposed tomography approach is robust to noisy measurement values, since the ELM-based estimator is quite lightweight. Simulations on the tomography of eigenstates, superposition states and mixed states are presented to verify our theoretical findings. Also, the superiority of the ELM-based tomography approach is demonstrated in comparison with that based on the radial basis function network, convolutional neural network and maximum likelihood estimation approach.