Variational quantum algorithms for trace norms and their applications

The trace norm of matrices plays an important role in quantum information and quantum computing. How to quantify it in today's noisy intermediate scale quantum(NISQ) devices is a crucial task for information processing. In this paper, we present three variational quantum algorithms on NISQ devices to estimate the trace norms corresponding to different situations.Compared with the previous methods, our means greatly reduce the requirement for quantum resources. Numerical experiments are provided to illustrate the effectiveness of our algorithms.     

A NISQ Method to Simulate Hermitian Matrix Evolution

As a universal quantum computer requires millions of error-corrected qubits, one of the current goals is to exploit the power of noisy intermediate-scale quantum (NISQ) devices. Based on a NISQ module–layered circuit, we propose a heuristic protocol to simulate Hermitian matrix evolution, which is widely applied as the core for many quantum algorithms. The two embedded methods, with their own advantages, only require shallow circuits and basic quantum gates. Capable to being deployed in near future quantum devices, we hope it provides an experiment-friendly way, contributing to the exploitation of power of current devices.     

Experimental Realization of Arbitrary Accuracy Iterative Phase Estimation Algorithms on Ensemble Quantum Computers

We report the first experimental demonstration of a nuclear phase estimation algorithms. Using feedback and iterations, magnetic resonance （NMR） realization of iterative we experimentally obtain the phase with 6 bits of precision on a two-qubit NMR quantum computer. Furthermore, we experimentally demonstrate the effect of gate noise on the iterative phase estimation algorithm. Our experimental results show that errors of measurements of the phase depend strongly on the precision of coupling gates. This experiment can be used as a benchmark for multi-qubit realizations of quantum information processing and precision measurements.     

Effective Field Theory of Random Quantum Circuits

Quantum circuits have been widely used as a platform to simulate generic quantum many-body systems. In particular, random quantum circuits provide a means to probe universal features of many-body quantum chaos and ergodicity. Some such features have already been experimentally demonstrated in noisy intermediate-scale quantum (NISQ) devices. On the theory side, properties of random quantum circuits have been studied on a case-by-case basis and for certain specific systems, and a hallmark of quantum chaos—universal Wigner–Dyson level statistics—has been derived. This work develops an effective field theory for a large class of random quantum circuits. The theory has the form of a replica sigma model and is similar to the low-energy approach to diffusion in disordered systems. The method is used to explicitly derive the universal random matrix behavior of a large family of random circuits. In particular, we rederive the Wigner–Dyson spectral statistics of the brickwork circuit model by Chan, De Luca, and Chalker [Phys. Rev. X 8, 041019 (2018)] and show within the same calculation that its various permutations and higher-dimensional generalizations preserve the universal level statistics. Finally, we use the replica sigma model framework to rederive the Weingarten calculus, which is a method of evaluating integrals of polynomials of matrix elements with respect to the Haar measure over compact groups and has many applications in the study of quantum circuits. The effective field theory derived here provides both a method to quantitatively characterize the quantum dynamics of random Floquet systems (e.g., calculating operator and entanglement spreading) and a path to understanding the general fundamental mechanism behind quantum chaos and thermalization in these systems.     

An overview of quantum error mitigation formulas

Minimizing the effect of noise is essential for quantum computers. The conventional method to protect qubits against noise is through quantum error correction. However, for current quantum hardware in the so-called noisy intermediate-scale quantum (NISQ) era, noise presents in these systems and is too high for error correction to be beneficial. Quantum error mitigation is a set of alternative methods for minimizing errors, including error extrapolation, probabilistic error cancellation, measurement error mitigation, subspace expansion, symmetry verification, virtual distillation, etc. The requirement for these methods is usually less demanding than error correction. Quantum error mitigation is a promising way of reducing errors on NISQ quantum computers. This paper gives a comprehensive introduction to quantum error mitigation. The state-of-art error mitigation methods are covered and formulated in a general form, which provides a basis for comparing, combining and optimizing different methods in future work.     

The reservoir learning power across quantum many-body localization transition

Harnessing the quantum computation power of the present noisy-intermediate-size-quantum devices has received tremendous interest in the last few years. Here we study the learning power of a one-dimensional long-range randomly-coupled quantum spin chain, within the framework of reservoir computing. In time sequence learning tasks, we find the system in the quantum many-body localized (MBL) phase holds long-term memory, which can be attributed to the emergent local integrals of motion. On the other hand, MBL phase does not provide sufficient nonlinearity in learning highly-nonlinear time sequences, which we show in a parity check task. This is reversed in the quantum ergodic phase, which provides sufficient nonlinearity but compromises memory capacity. In a complex learning task of Mackey–Glass prediction that requires both sufficient memory capacity and nonlinearity, we find optimal learning performance near the MBL-to-ergodic transition. This leads to a guiding principle of quantum reservoir engineering at the edge of quantum ergodicity reaching optimal learning power for generic complex reservoir learning tasks. Our theoretical finding can be tested with near-term NISQ quantum devices.     

Purification in entanglement distribution with deep quantum neural network

Entanglement distribution is important in quantum communication. Since there is no information with value in this process, purification is a good choice to solve channel noise. In this paper, we simulate the purification circuit under true environment on Cirq, which is a noisy intermediate-scale quantum (NISQ) platform. Besides, we apply quantum neural network (QNN) to the state after purification. We find that combining purification and quantum neural network has good robustness towards quantum noise. After general purification, quantum neural network can improve fidelity significantly without consuming extra states. It also helps to obtain the advantage of entangled states with higher dimension under amplitude damping noise. Thus, the combination can bring further benefits to purification in entanglement distribution.     

Development of High Performance Quantum Image Algorithm on Constrained Least Squares Filtering Computation

Recent development of computer technology may lead to the quantum image algorithms becoming a hotspot. Quantum information and computation give some advantages to our quantum image algorithms, which deal with the limited problems that cannot be solved by the original classical image algorithm. Image processing cry out for applications of quantum image. Most works on quantum images are theoretical or sometimes even unpolished, although real-world experiments in quantum computer have begun and are multiplying. However, just as the development of computer technology helped to drive the Technology Revolution, a new quantum image algorithm on constrained least squares filtering computation was proposed from quantum mechanics, quantum information, and extremely powerful computer. A quantum image representation model is introduced to construct an image model, which is then used for image processing. Prior knowledge is employed in order to reconstruct or estimate the point spread function, and a non-degenerate estimate is obtained based on the opposite processing. The fuzzy function against noises is solved using the optimal measure of smoothness. On the constraint condition, determine the minimum criterion function and estimate the original image function. For some motion blurs and some kinds of noise pollutions, such as Gaussian noises, the proposed algorithm is able to yield better recovery results. Additionally, it should be noted that, when there is a noise attack with very low noise intensity, the model based on the constrained least squares filtering can still deliver good recovery results, with strong robustness. Subsequently, discuss the simulation analysis of the complexity of implementing quantum circuits and image filtering, and demonstrate that the algorithm has a good effect on fuzzy recovery, when the noise density is small.     

F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits

Generative modelling is an important unsupervised task in machine learning. In this work, we study a hybrid quantum-classical approach to this task, based on the use of a quantum circuit born machine. In particular, we consider training a quantum circuit born machine using f-divergences. We first discuss the adversarial framework for generative modelling, which enables the estimation of any f-divergence in the near term. Based on this capability, we introduce two heuristics which demonstrably improve the training of the born machine. The first is based on f-divergence switching during training. The second introduces locality to the divergence, a strategy which has proved important in similar applications in terms of mitigating barren plateaus. Finally, we discuss the long-term implications of quantum devices for computing f-divergences, including algorithms which provide quadratic speedups to their estimation. In particular, we generalise existing algorithms for estimating the Kullback–Leibler divergence and the total variation distance to obtain a fault-tolerant quantum algorithm for estimating another f-divergence, namely, the Pearson divergence.     

Quantum stochastic differential equation for squeezed light

In this paper, the quantum stochastic differential equation (QSDE) is derived which is based on explanatory for interaction of open quantum system with squeezed quantum noise. This equation describes the stochastic evolution of unitary operator and is used to compute the evolution of quantum observable and output field. Our QSDE has complete form with respect to previous QSDE for squeezed light, because it bears three fundamental quantum noises for its evolution and the scattering between quantum channels is included. Meanwhile, when squeezed noise reduces to vacuum noise, our QSDE reveals the famous Hudson-Parthasarathy QSDE. Our equations may have application for quantum network analysis of squeezed noise interferometer for gravitational wave detection.     

Exact Equivalence between Quantum Adiabatic Algorithm and Quantum Circuit Algorithm

We present a rigorous proof that quantum circuit algorithm can be transformed into quantum adiabatic algorithm with the exact same time complexity. This means that from a quantum circuit algorithm of L gates we can construct a quantum adiabatic algorithm with time complexity of O(L). Additionally, our construction shows that one may exponentially speed up some quantum adiabatic algorithms by properly choosing an evolution path.     

Semiconductor Emitters in Entropy Sources for Quantum Random Number Generation

Random number generation (RNG) is needed for a myriad of applications ranging from secure communication encryption to numerical simulations to sports and games. However, generating truly random numbers can be elusive. Pseudorandom bit generation using computer algorithms provides a high random bit generation rate. Nevertheless, the reliance on predefined algorithms makes it deterministic and predictable once initial conditions are known. Relying on physical phenomena (such as measuring electrical noise or even rolling dice) can achieve a less predictable sequence of bits. Furthermore, if the physical phenomena originate from quantum effects, they can be truly random and completely unpredictable due to quantum indeterminacy. Traditionally, physical RNG is significantly slower than pseudorandom techniques. To meet the demand for high-speed RNG with perfect unpredictability, semiconductor light sources are adopted as parts of the sources of randomness, i.e., entropy sources, in quantum RNG (QRNG) systems. The high speed of their noise, the high efficiency, and the small scale of these devices make them ideal for chip-scale QRNG. Here, the applications and recent advances of QRNG are reviewed using semiconductor emitters. Finally, the performance of these emitters is compared and discuss their potential in future technologies.     

A Multi-Classification Hybrid Quantum Neural Network Using an All-Qubit Multi-Observable Measurement Strategy

Quantum machine learning is a promising application of quantum computing for data classification. However, most of the previous research focused on binary classification, and there are few studies on multi-classification. The major challenge comes from the limitations of near-term quantum devices on the number of qubits and the size of quantum circuits. In this paper, we propose a hybrid quantum neural network to implement multi-classification of a real-world dataset. We use an average pooling downsampling strategy to reduce the dimensionality of samples, and we design a ladder-like parameterized quantum circuit to disentangle the input states. Besides this, we adopt an all-qubit multi-observable measurement strategy to capture sufficient hidden information from the quantum system. The experimental results show that our algorithm outperforms the classical neural network and performs especially well on different multi-class datasets, which provides some enlightenment for the application of quantum computing to real-world data on near-term quantum processors.     

Portable Implementation of a Quantum Thermal Bath for Molecular Dynamics Simulations

Recently, Dammak and coworkers (Phys. Rev. Lett. 103:190601, 2009) proposed that the quantum statistics of vibrations in condensed systems at low temperature could be simulated by running molecular dynamics simulations in the presence of a colored noise with an appropriate power spectral density. In the present contribution, we show how this method can be implemented in a flexible manner and at a low computational cost by synthesizing the corresponding noise ‘on the fly’. The proposed algorithm is tested for a simple harmonic chain as well as for a more realistic model of aluminium crystal. The energy and Debye-Waller factor are shown to be in good agreement with those obtained from harmonic approximations based on the phonon spectrum of the systems. The limitations of the method associated with anharmonic effects are also briefly discussed. Some perspectives for disordered materials and heat transfer are considered.     

Quantum Private Comparison Based on Quantum Search Algorithm

We propose two quantum private comparison protocols based on quantum search algorithm with the help of a semi-honest third party. Our protocols utilize the properties of quantum search algorithm, the unitary operations, and the single-particle measurements. The security of our protocols is discussed with respect to both the outsider attack and the participant attack. There is no information leaked about the private information and the comparison result, even the third party cannot know these information.     

Quantum synchronization

Using the methods of quantum trajectories we study numerically a quantum dissipative system with periodic driving which exhibits synchronization phenomenon in the classical limit. The model allows to analyze the effects of quantum fluctuations on synchronization and establish the regimes where the synchronization is preserved in a quantum case (quantum synchronization). Our results show that at small values of Planck constant ħ the classical devil's staircase remains robust with respect to quantum fluctuations while at large ħ values synchronization plateaus are destroyed. Quantum synchronization in our model has close similarities with Shapiro steps in Josephson junctions and it can be also realized in experiments with cold atoms.     

量子相干

量子相干不仅是量子力学中的一个基本概念,同时也是重要的量子信息处理的物理资源.随着基于资源理论框架的量子相干度量方案的提出,量子相干度的量化研究成为近年来人们关注的一个热点问题.量子相干作为一种物理资源也十分脆弱,极容易受到环境噪声的影响而产生退相干,因此开放系统中的量子相干演化和保持也是人们广泛关注的课题.另外,量子相干在量子多体系统、量子热动力学、量子生物学等领域也有着潜在的应用价值.本文介绍量子相干度量的资源理论框架和基于该框架定义的相对熵相干性、l1范数相干性、基于量子纠缠的相干性、基于凸顶结构的相干性和相干鲁棒性等量子相干度量函数,概述开放系统中量子相干演化的动力学行为、典型信道的量子相干产生和破坏能力以及量子相干的冻结等现象,同时例举量子相干在Deutsch-Jozsa算法、Grover算法以及量子多体系统相变问题研究等方面的重要应用.量子相干研究仍处于快速发展之中,期望本综述能为该领域的发展带来启示.     

Quantum Image Location

Quantum image processing has been a hot topic as a consequence of the development of quantum computation. Many quantum image processing algorithms have been proposed, whose efficiency are theoretically higher than their corresponding classical algorithms. However, most of the quantum schemes do not consider the problem of measurement. If users want to get the results, they must measure the final state many times to get all the pixels’ values. Moreover, executing the algorithm one time, users can only measure the final state one time. In order to measure it many times, users must execute the algorithms many times. If the measurement process is taken into account, whether or not the algorithms are really efficient needs to be reconsidered. In this paper, we try to solve the problem of measurement and give a quantum image location algorithm. This scheme modifies the probability of pixels to make the target pixel to be measured with higher probability. Furthermore, it only has linear complexity.     

Quantum speedup in adaptive boosting of binary classification

In classical machine learning,a set of weak classifiers can be adaptively combined for improving the overall performance,a technique called adaptive boosting(or AdaBoost).However,constructing a combined classifier for a large data set is typically resource consuming.Here we propose a quantum extension of AdaBoost,demonstrating a quantum algorithm that can output the optimal strong classifier with a quadratic speedup in the number of queries of the weak classifiers.Our results also include a generalization of the standard AdaBoost to the cases where the output of each classifier may be probabilistic.We prove that the query complexity of the non-deterministic classifiers is the same as those of deterministic classifiers,which may be of independent interest to the classical machine-learning community.Additionally,once the optimal classifier is determined by our quantum algorithm,no quantum resources are further required.This fact may lead to applications on near term quantum devices.     

On Models of Nonlinear Evolution Paths in Adiabatic Quantum Algorithms

In this paper,we study two different nonlinear interpolating paths in adiabatic evolution algorithms for solving a particular class of quantum search problems where both the initial and final Hamiltonian are one-dimensional projector Hamiltonians on the corresponding ground state.If the overlap between the initial state and final state of the quantum system is not equal to zero,both of these models can provide a constant time speedup over the usual adiabatic algorithms by increasing some another corresponding "complexity".But when the initial state has a zero overlap with the solution state in the problem,the second model leads to an infinite time complexity of the algorithm for whatever interpolating functions being applied while the first one can still provide a constant running time.However,inspired by a related reference,a variant of the first model can be constructed which also fails for the problem when the overlap is exactly equal to zero if we want to make up the "intrinsic" fault of the second model - an increase in energy.Two concrete theorems are given to serve as explanations why neither of these two models can improve the usual adiabatic evolution algorithms for the phenomenon above.These just tell us what should be noted when using certain nonlinear evolution paths in adiabatic quantum algorithms for some special kind of problems.