Estimating the difference between quantum data is crucial in computing. However, as typical characterizations of similarity, trace distance and fidelity are believed to be exponentially-hard evaluate general. In this work, we introduce hybrid quantum-classical algorithms for these two measures on near-term devices where no assumption input state required. First, Variational Trace Distance Estimation (VTDE) algorithm. We particular provide technique extract desired spectrum information any...

Abstract Quantum entanglement is a key resource in quantum technology, and its quantification vital task the current noisy intermediate-scale (NISQ) era. This paper combines hybrid quantum-classical computation quasi-probability decomposition to propose two variational algorithms, called detection (VED) logarithmic negativity estimation (VLNE), for detecting quantifying on near-term devices, respectively. VED makes use of positive map criterion works as follows. Firstly, it decomposes into...

The manipulation of quantum states through linear maps beyond operations has many important applications in various areas information processing. Current methods simulate unphysical by sampling physical according to classically determined probability distributions. In this work, we show that using measurement instead leads lower simulation costs for general Hermitian-preserving maps. Remarkably, establish the equality between cost and well-known diamond norm, thus closing a previously known...

Abstract Distributed quantum information processing is essential for building networks and enabling more extensive computations. In this regime, several spatially separated parties share a multipartite system, the most natural set of operations Local Operations Classical Communication (LOCC). As pivotal part in theory practice, LOCC has led to many vital protocols such as teleportation. However, designing practical challenging due LOCC’s intractable structure limitations by near-term...

Unitary transformations formulate the time evolution of quantum states. How to learn a unitary transformation efficiently is fundamental problem in machine learning. The most natural and leading strategy train learning model based on dataset. Although presence more training data results better models, using too much reduces efficiency training. In this work, we solve minimum size sufficient datasets for exactly, which reveals power limitation data. First, prove that dataset with pure states...

Accurately estimating high-order moments of quantum states is an elementary precondition for many crucial tasks in computing, such as entanglement spectroscopy, entropy estimation, spectrum and predicting nonlinear features from states. But reality, inevitable noise prevents us accessing the desired value. In this paper, we address issue by systematically analyzing feasibility efficiency extracting noisy We first show that there exists a protocol capable accomplishing task if only underlying...

Weak values of quantum observables are a powerful tool for investigating phenomena. Some methods measuring weak in the laboratory require interactions and postselection, while others deterministic, but statistics over number experiments that grows linearly with dimension measured system worst case all possible observables. Here we propose deterministic dimension-independent scheme estimating arbitrary The is based on controlled operations associates states mathematical expression value to...

An emerging direction of quantum computing is to establish meaningful applications in various fields artificial intelligence, including natural language processing (NLP). Although some efforts based on syntactic analysis have opened the door research Quantum NLP (QNLP), limitations such as heavy preprocessing and syntax-dependent network architecture make them impracticable larger real-world data sets. In this paper, we propose a new simple architecture, called self-attention neural (QSANN),...

Variational quantum algorithms have been acknowledged as a leading strategy to realize near-term advantages in meaningful tasks, including machine learning and combinatorial optimization. When applied tasks involving classical data, such generally begin with circuits for data encoding then train neural networks (QNNs) minimize target functions. Although QNNs widely studied improve these algorithms' performance on practical there is gap systematically understanding the influence of eventual...

Extracting classical information from quantum systems is an essential step of many algorithms. However, this could be corrupted as the are prone to noises, and its distortion under dynamics has not been adequately investigated. In work, we introduce a systematic framework study how well can retrieve noisy states. Given channel, fully characterize range recoverable information. This condition allows natural measure quantifying recoverability channel. Moreover, resolve minimum retrieving cost,...

We introduce the task of shadow process simulation, where goal is to simulate estimation expectation values arbitrary quantum observables at output a target physical process. When sender and receiver share random bits or other no-signaling resources, we show that performance simulation exceeds conventional protocols in variety scenarios including communication, noise data compression. Remarkably, find there exist provides increased statistical accuracy without any increase number required samples.

The spectral resolution of broadband Fourier-transform coherent anti-Stokes Raman spectroscopy is limited by the maximum optical path length difference that can be scanned within a short time in an interferometer. However, alternatives to exist which bypass this limitation with certain assumptions. We apply one such approach using interferometers delay line (low Fourier resolution) and large (high resolution). With method, we demonstrate closely spaced vibrational bands possible...

Quantum computing, as an emerging computing paradigm, is expected to tackle problems such quantum chemistry, optimization, information security, and artificial intelligence, which are intractable with using classical computing. hardware software continue develop rapidly, but they not realize universal computation in the next few years. Therefore, use of solve practical near term has become a hot topic field Exploration applications near-term great significance understanding capability...

Quantum entanglement is a key resource in quantum technology, and its quantification vital task the current Noisy Intermediate-Scale (NISQ) era. This paper combines hybrid quantum-classical computation quasi-probability decomposition to propose two variational algorithms, called Variational Entanglement Detection (VED) Logarithmic Negativity Estimation (VLNE), for detecting quantifying on near-term devices, respectively. VED makes use of positive map criterion works as follows. Firstly, it...

Most traditional network anomalies and attacks detection systems tend to employ supervised strategies, which require labeled training dataset that is arduous expensive obtain fails detect unknown jeopardizing the system security reliability. In this paper we present a new unsupervised approach based on abnormality weights rendering subspace clustering techniques without previously traffic or signatures. order examine capability of proposed approach, conducted several experiments both real...

We introduce the task of shadow process simulation, where goal is to reproduce expectation values arbitrary quantum observables at output a target physical process. When sender and receiver share classical random bits, we show that performance simulation exceeds conventional protocols in variety scenarios including communication, noise data compression. Remarkably, provides increased accuracy without any increase sampling cost. Overall, unified framework for protocols, probabilistic error...

Understanding the classical communication cost of simulating a quantum channel is fundamental problem in information theory, which becomes even more intriguing when considering role non-locality processing. This paper investigates bidirectional bipartite assisted by non-signalling correlations. Such correlations are permitted not only across spatial dimension between two parties but also along temporal simulation protocol. By introducing superchannels, we derive semidefinite programming...

The manipulation of quantum states through linear maps beyond operations has many important applications in various areas information processing. Current methods simulate unphysical by sampling physical operations, but a classical way. In this work, we show that using measurement place leads to lower simulation costs for general Hermitian-preserving maps. Remarkably, establish the equality between cost and well-known diamond norm, thus closing previously known gap assigning norm universal...

Accurately estimating high-order moments of quantum states is an elementary precondition for many crucial tasks in computing, such as entanglement spectroscopy, entropy estimation, spectrum estimation and predicting non-linear features from states. But reality, inevitable noise prevents us accessing the desired value. In this paper, we address issue by systematically analyzing feasibility efficiency extracting noisy We first show that there exists a protocol capable accomplishing task if...

Weak values of quantum observables are a powerful tool for investigating broad spectrum phenomena. For this reason, several methods to measure them in the laboratory have been proposed. Some these require weak interactions and postselection, while others deterministic, but statistics over number experiments growing exponentially with measured particles. Here we propose deterministic dimension-independent scheme estimating arbitrary observables. The scheme, based on coherently controlled SWAP...

Unitary transformations formulate the time evolution of quantum states. How to learn a unitary transformation efficiently is fundamental problem in machine learning. The most natural and leading strategy train learning model based on dataset. Although presence more training data results better models, using too much reduces efficiency training. In this work, we solve minimum size sufficient datasets for exactly, which reveals power limitation data. First, prove that dataset with pure states...

Extracting classical information from quantum systems is an essential step of many algorithms. However, this could be corrupted as the are prone to noises, and its distortion under dynamics has not been adequately investigated. In work, we introduce a systematic framework study how well can retrieve noisy states. Given channel, fully characterize range recoverable information. This condition allows natural measure quantifying recoverability channel. Moreover, resolve minimum retrieving cost,...