Quantum Physics

Quantum key distribution (QKD) has been developed for decades and several different QKD protocols have been proposed. But two difficulties limit the implementation of most QKD protocols. First, the involved participants are required to have heavy quantum capabilities, such as quantum joint operation, quantum register, and so on. Second, a hypothetical authenticated classical channel is used in most of the existing QKD protocols and this assumed channel does not exist in reality. To solve both the above limitations at the same time, this study proposes three lightweight authenticated QKD protocols with key recycling and shows these proposed protocols are robust under the collective attack.

We give strong analytic and numerical evidence that, under mild measurement assumptions, two qubits cannot both be recycled to generate Bell nonlocality between multiple independent observers on each side. This is surprising, as under the same assumptions it is possible to recycle just one of the qubits an arbitrarily large number of times [P. J. Brown and R. Colbeck, Phys. Rev. Lett. 125, 090401 (2020)]. We derive corresponding 'one-sided monogamy relations' that rule out two-sided recycling for a wide range of parameters, based on a general tradeoff relation between the strengths and maximum reversibilities of qubit measurements. We also show if the assumptions are relaxed to allow sufficiently biased measurement selections, then there is a narrow range of measurement strengths that allows two-sided recycling for two observers on each side, and propose an experimental test. Our methods may be readily applied to other types of quantum correlations, such as steering and entanglement, and hence to general information protocols involving sequential measurements.

Within the framework of the Lindblad master equation, we propose a general methodology to describe the effects of the environment on a system in dilute gas phase. The phenomenological parameters characterizing the transitions between rovibrational states of the system induced by collisions can be extracted from experimental transition kinetic constants, relying on Energy Gap fitting laws. As the availability of this kind of experimental data can be limited, the present work relied on experimental line broadening coefficients, however still using Energy Gap fitting laws. The 3 μ m infrared spectral range of acetylene was chosen to illustrate the proposed approach. The method shows fair agreement with available experimental data while being computationally inexpensive. The results are discussed in the context of state laser quantum control.

The hierarchy of nonlocality and entanglement in multipartite systems is one of the fundamental problems in quantum physics. Existing studies on this topic to date were limited to the entanglement classification according to the numbers of particles enrolled. Equivalence under stochastic local operations and classical communication provides a more detailed classification, e. g. the genuine three-qubit entanglement being divided into W and GHZ classes. We construct two families of local models for the three-qubit Greenberger-Horne-Zeilinger (GHZ)-symmetric states, whose entanglement classes have a complete description. The key technology of construction the local models in this work is the GHZ symmetrization on tripartite extensions of the optimal local-hidden-state models for Bell diagonal states. Our models show that entanglement and nonlocality are inequivalent for all the entanglement classes (biseparable, W, and GHZ) in three-qubit systems.

We realize a suite of logical operations on a distance-two logical qubit stabilized using repeated error detection cycles. Logical operations include initialization into arbitrary states, measurement in the cardinal bases of the Bloch sphere, and a universal set of single-qubit gates. For each type of operation, we observe higher performance for fault-tolerant variants over non-fault-tolerant variants, and quantify the difference through detailed characterization. In particular, we demonstrate process tomography of logical gates, using the notion of a logical Pauli transfer matrix. This integration of high-fidelity logical operations with a scalable scheme for repeated stabilization is a milestone on the road to quantum error correction with higher-distance superconducting surface codes.

Moderate-size quantum computers are now publicly accessible over the cloud, opening the exciting possibility of performing dynamical simulations of quantum systems. However, while rapidly improving, these devices have short coherence times, limiting the depth of algorithms that may be successfully implemented. Here we demonstrate that, despite these limitations, it is possible to implement long-time, high fidelity simulations on current hardware. Specifically, we simulate an XY-model spin chain on the Rigetti and IBM quantum computers, maintaining a fidelity of at least 0.9 for over 600 time steps. This is a factor of 150 longer than is possible using the iterated Trotter method. Our simulations are performed using a new algorithm that we call the fixed state Variational Fast Forwarding (fsVFF) algorithm. This algorithm decreases the circuit depth and width required for a quantum simulation by finding an approximate diagonalization of a short time evolution unitary. Crucially, fsVFF only requires finding a diagonalization on the subspace spanned by the initial state, rather than on the total Hilbert space as with previous methods, substantially reducing the required resources.

The coherent-state qubit is a promising candidate for optical quantum information processing due to its nearly-deterministic nature of the Bell-state measurement (BSM). However, its non-orthogonality incurs difficulties such as failure of the BSM. One may use a large amplitude ( α ) for the coherent state to minimize the failure probability, but the qubit then becomes more vulnerable to dephasing by photon loss. We propose a hardware-efficient concatenated BSM (CBSM) scheme with modified parity encoding using coherent states with reasonably small amplitudes ( |α|�? ), which simultaneously suppresses both failures and dephasing in the BSM procedure. We numerically show that the CBSM scheme achieves a success probability arbitrarily close to unity for appropriate values of α and sufficiently low photon loss rates (e.g., �?% ). Furthermore, we verify that the quantum repeater scheme exploiting the CBSM scheme for quantum error correction enables one to carry out efficient long-range quantum communication over 1000 km. We show that the performance is comparable to those of other up-to-date methods or even outperforms them for some cases. Finally, we present methods to prepare logical qubits under modified parity encoding and implement elementary logical operations, which consist of several physical-level ingredients such as generation of Schrödinger's cat state and elementary gates under coherent-state basis. Our work demonstrates that the encoded coherent-state qubits in free-propagating fields provide an alternative route to fault-tolerant information processing, especially long-range quantum communication.

A crucial subroutine in quantum computing is to load the classical data of N complex numbers into the amplitude of a superposed n=??log 2 N??-qubit state. It has been proven that any algorithm universally implements this subroutine would need at least O(N) constant weight operations. However, the proof assumes that only n qubits are used, whereas the circuit depth could be reduced by extending the space and allowing ancillary qubits. Here we investigate this space-time tradeoff in quantum state preparation with classical data. We propose quantum algorithms with O( n 2 ) circuit depth to encode any N complex numbers using only single-, two-qubit gates and local measurements with ancillary qubits. Different variances are proposed with different space and time complexities. In particular, we present a scheme with O( N 2 ) ancillary qubits, O( n 2 ) circuit depth, and O( n 2 ) average runtime, which exponentially improves the conventional bound. While the algorithm requires more ancillary qubits, it consists of quantum circuit blocks that only simultaneously act on a constant number of qubits and at most O(n) qubits are entangled. We also prove a fundamental lower bound O(n) for the minimum circuit depth and runtime with arbitrary number of ancillary qubits, aligning with our scheme with O( n 2 ) . The algorithms are expected to have wide applications in both near-term and universal quantum computing.

Determining the dynamics of the expectation values for operators acting on a quantum many-body (QMB) system is a challenging task. Matrix product states (MPS) have traditionally been the "go-to" models for these systems because calculating expectation values in this representation can be done with relative simplicity and high accuracy. However, such calculations can become computationally costly when extended to long times. Here, we present a solution for efficiently extending the computation of expectation values to long time intervals. We utilize a multi-layer perceptron (MLP) model as a tool for regression on MPS expectation values calculated within the regime of short time intervals. With this model, the computational cost of generating long-time dynamics is significantly reduced, while maintaining a high accuracy. These results are demonstrated with operators relevant to quantum spin models in one spatial dimension.

Machine learning is a powerful tool in finding hidden data patterns for quantum information processing. Here, we introduce this method into the optical readout of electron-spin states in diamond via single-photon collection and demonstrate improved readout precision at room temperature. The traditional method of summing photon counts in a time gate loses all the timing information crudely. We find that changing the gate width can only optimize the contrast or the state variance, not both. In comparison, machine learning adaptively learns from time-resolved fluorescence data, and offers the optimal data processing model that elaborately weights each time bin to maximize the extracted information. It is shown that our method can repair the processing result from imperfect data, reducing 7% in spin readout error while optimizing the contrast. Note that these improvements only involve recording photon time traces and consume no additional experimental time, they are thus robust and free. Our machine learning method implies a wide range of applications in precision measurement and optical detection of states.