Separability-entanglement classifier via machine learning

Lu, Sirui; Huang, Shilin; Li, Keren; Li, Jun; Chen, Jianxin; Lu, Dawei; Ji, Zhengfeng; Yi, Shen; Zhou, D. L.; Zeng, Bei

doi:10.1103/physreva.98.012315

Cited by 107 publications

(74 citation statements)

References 37 publications

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“…In the mathematical model, the qubit always returns a binary decision regardless of the number of reservoirs acting as the input information channels as implied in Eqs. (5) and (6). Though generalizing the proposal to larger number of input channels are straightforward due to the additivity of the quantum dynamical maps and the convexity of the density matrix, nevertheless, we give an example for three reservoir states as input information channels for further analysis as depicted in Fig.…”

Section: Three Input Channelsmentioning

confidence: 99%

“…Classification of data is of central importance to important implementations such as medical diagnosis, pattern recognition and machine learning. Due to the well-known advantages of quantum computation, studies about the quantum equivalent of machine learning algorithms have been reached to a remarkable level [1,2,3,4,5,6,7]. In contrast to the circuit model of quantum computation in which the system of interest is assumed to be perfectly isolated from the environmental degrees of freedom, one could imagine a quantum classifier as an open quantum system.…”

Section: Introductionmentioning

confidence: 99%

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A steady state quantum classifier

2019

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We report that under some specific conditions a single qubit model weakly interacting with information environments can be referred to as a quantum classifier. We exploit the additivity and the divisibility properties of the completely positive (CP) quantum dynamical maps in order to obtain an open quantum classifier. The steady state response of the system with respect to the input parameters was numerically investigated and it's found that the response of the open quantum dynamics at steady state acts non-linearly with respect to the input data parameters. We also demonstrate the linear separation of the quantum data instances that reflects the success of the functionality of the proposed model both for ideal and experimental conditions. Superconducting circuits were pointed out as the physical model to implement the theoretical model with possible imperfections.

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Section: Three Input Channelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A steady state quantum classifier

2019

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“…As we can see, most of the threshold values are improved by Proposition 1, but few are not. Recently in [27], by using machine learning techniques, the threshold for state ρ UPB is reported to be 0.8649. However, a huge number of random extreme points within the separable region is needed by the method in Ref.…”

Section: Appendix C: Applications Of Propositionmentioning

confidence: 99%

“…However, a huge number of random extreme points within the separable region is needed by the method in Ref. [27], which is only useful for the particular state tested.…”

Section: Appendix C: Applications Of Propositionmentioning

confidence: 99%

Convex Optimization over Classes of Multiparticle Entanglement

Shang

Gühne

2018

Phys. Rev. Lett.

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A well-known strategy to characterize multiparticle entanglement utilizes the notion of stochastic local operations and classical communication (SLOCC), but characterizing the resulting entanglement classes is difficult. Given a multiparticle quantum state, we first show that Gilbert's algorithm can be adapted to prove separability or membership in a certain entanglement class. We then present two algorithms for convex optimization over SLOCC classes. The first algorithm uses a simple gradient approach, while the other one employs the accelerated projected-gradient method. For demonstration, the algorithms are applied to the likelihood-ratio test using experimental data on bound entanglement of a noisy four-photon Smolin state [Phys. Rev. Lett. 105, 130501 (2010)].

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“…On the other hand, machine learning (ML) is designed to discover hidden data correlations, and it is widely used in classification problems [23]. It has been recently introduced in quantum information tasks to mitigate crosstalks in multi-qubit readout [24], to enhance quantum metrology [25,26], to identify quantum phases of matter and phase transitions [27][28][29], to identify entanglement [30][31][32], and even to determine existence of quantum advantage [33], to name a few. In particular, ML shows success in efficient interpretation of quantum state tomography (QST), by being robust to partial QST and state-preparation-and-measurement (SPAM) errors [32,[34][35][36].…”

mentioning

confidence: 99%

Repetitive readout enhanced by machine learning

Liu

Chen

Liu

et al. 2020

Mach. Learn.: Sci. Technol.

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Single-shot readout is a key component for scalable quantum information processing. However, many solid-state qubits with favorable properties lack the single-shot readout capability. One solution is to use the repetitive quantum-non-demolition readout technique, where the qubit is correlated with an ancilla, which is subsequently read out. The readout fidelity is therefore limited by the back-action on the qubit from the measurement. Traditionally, a threshold method is taken, where only the total photon count is used to discriminate qubit state, discarding all the information of the back-action hidden in the time trace of repetitive readout measurement. Here we show by using machine learning (ML), one obtains higher readout fidelity by taking advantage of the time trace data. ML is able to identify when back-action happened, and correctly read out the original state. Since the information is already recorded (but usually discarded), this improvement in fidelity does not consume additional experimental time, and could be directly applied to preparation-by-measurement and quantum metrology applications involving repetitive readout. IntroductionSingle-shot readout is a key component for scalable quantum information processing [1, 2], for its close connection to state initialization and fault-tolerant quantum error correction [3]. Indeed, it is one of the main deciding factors in the selection of potential qubits. Single-shot readout has been achieved in various physical qubit systems, ranging from neutral atoms [4][5][6], to trapped ions [7], superconducting qubit [8], and solid-state defect centers [9][10][11][12][13][14][15][16]. There are however situations where a candidate qubit has favorable coherence properties, but does not naturally come with single-shot readout capabilities. Examples include Al + ions [17,18] and roomtemperature nitrogen-vacancy (NV) centers in diamond [12][13][14][15][16], where a closed optical cycle for readout is either lacking, or experimentally challenging. A solution to this problem is through repetitive quantum-nondemolition (QND) measurements [18].In the repetitive QND protocol, a controlled-NOT (CNOT) gate is applied to correlate the qubit state to an ancilla, which is subsequently read out ( figure 1(a)). If the readout operator commutes with the qubit's intrinsic Hamiltonian, in other words, if the readout is QND, one can repeat the above process multiple times to increase signal-to-noise ratio, until the desired fidelity is reached.This protocol is also known as the repetitive readout technique widely adopted in NV research at roomtemperature, where the nuclear spin state (here the 14 N or a 13 C) is repetitively read out with the help of the NV electronic spin [12,19]. In its implementations so far, the spin state was determined by comparing the total photon number collected through all the repetitive readouts with a previously established threshold ( figure 1(b)). The detected photon count numbers are thus divided into two classes, referred to as bright (dark) state of the...

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Separability-entanglement classifier via machine learning

Cited by 107 publications

References 37 publications

A steady state quantum classifier

A steady state quantum classifier

Convex Optimization over Classes of Multiparticle Entanglement

Repetitive readout enhanced by machine learning

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