Tencent Technology (shenzhen) Company Limited

Tencent Technology Company Limited

Changyu Hsieh

Shengyu Zhang

Yuqin Chen

Yicong Zheng

Kaili Ma

Yu Wei Chen

Hongyao Tang

Guangyong Chen

Shixin Zhang

Songlian Ou

Jianye Hao

Yaodong Yang

This disclosure discloses a method and apparatus for determining a quantum circuit. The method may include sampling an initial circuit unit pool according to an initial sampling manner to obtain initial K groups of circuit units and constructing and generating initial K candidate quantum circuits. The method may further include determining a performance evaluation index corresponding to the initial K candidate quantum circuits and updating the initial sampling manner and a circuit unit in the initial circuit unit pool based on the performance evaluation index, to obtain an updated sampling manner and an updated circuit unit pool. The method may further include sampling the updated circuit unit pool according to the updated sampling manner to obtain updated K groups of circuit units and constructing and generating updated K candidate quantum circuits. The method may further include determining a target quantum circuit from the updated K candidate quantum circuits.

Method and apparatus for determining quantum circuit, and storage medium

This disclosure describes a quantum noise process analysis method, device, and storage medium, in the field of quantum processing technologies. The method may include performing quantum process tomography (QPT) on a quantum noise process of a target quantum system, to obtain dynamical maps of the quantum noise process, wherein the QPT involves at least one measurement of the target quantum. The method further includes extracting transfer tensor maps (TTMs) of the quantum noise process from the dynamical maps; and analyzing the quantum noise process according to the TTMs. The TTM is used for representing a dynamical evolution of the quantum noise process to reflect the law of evolution of the dynamical maps of the quantum noise process over time. As a result, richer and more comprehensive information about the quantum noise process can be obtained by analyzing the quantum noise process based on the TTM of the quantum noise process than by pure QPT, thereby achieving a more accurate and more comprehensive analysis of the quantum noise process.

Quantum noise process analysis method and apparatus, device, and storage medium

A data classification method and system, and a classifier training method and system are disclosed in the embodiments of the present disclosure, belonging to the field of artificial intelligence (AI), cloud technologies, and quantum technologies. The method includes: providing to-be-classified data to a quantum computer; performing feature mapping on the to-be-classified data by using a quantum circuit to obtain a quantum state of the to-be-classified data; determining an estimation result according to a boundary vector of a classifier, the quantum state of the to-be-classified data, and a quantum state of index information corresponding to the boundary vector; transmitting the estimation result to a classical computer. The quantum state of the index information refers to a superposition of feature maps of training data used by the classifier during training; and determining a classification result corresponding to the to-be-classified data according to the estimation result.

Data classification method using quantum computer, quantum computer system, and classifier training method of quantum computer system

This application provides a quantum noise process analysis method, system, storage medium, and electronic device, which are applied in the field of quantum information processing technology. The method includes: preparing quantum initial states; respectively inputting the quantum initial states into a plurality of first circuits to obtain a plurality of first quantum output states; determining a first dynamical map eigenspectrum according to a functional correspondence between the plurality of first quantum output states and the quantum initial states; respectively inputting the quantum initial states into a plurality of second circuits to obtain a plurality of second quantum output states; determining a second dynamical map eigenspectrum according to a functional correspondence between the plurality of second quantum output states and the quantum initial states; and determining a dynamical map eigenspectrum of a quantum noise process according to the first dynamical map eigenspectrum and the second dynamical map eigenspectrum.

Quantum noise process analysis method, system, storage medium, and electronic device

Embodiments of this application relate to a retrosynthesis processing method and apparatus, an electronic device, and a computer-readable storage medium. A retrosynthesis processing method is performed by a computer device. The method includes determining molecular representation information of a target molecule. The method includes inputting the molecular representation information into a target neural network. The method includes performing, via the target neural network, retrosynthesis processing on the target molecule based on the molecular representation information of the target molecule, to obtain a respective retrosynthesis reaction of the target molecule for each step of the retrosynthesis processing. The target neural network is obtained by training a predetermined neural network according to a sample cost dictionary that is generated by concurrently performing retrosynthesis reaction training on each of a plurality of sample molecules, and the respective retrosynthesis reaction is performed according to a preset retrosynthesis reaction architecture.

Electronic device, and computer-readable storage medium

A data search method based on a quantum simulated algorithm includes: creating a first search path according to a plurality of pieces of candidate data; obtaining a first processing function corresponding to the first search path, the first processing function comprising the plurality of coefficients, output values of the first processing function being determined by the first search path; traversing at least two value sets of the first search path; obtaining the output values of the first processing function based on a quantum simulated algorithm; respectively using, when the output values of the first processing function converge to a converging value, a plurality of values in a value set corresponding to the converging value as the values of the plurality of coefficients, to obtain a second search path; and searching for target data by using the second search path.

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Shenzhen, China

Changyu Hsieh