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QUANTUM CHIP OF QUBITS

Resumen de: WO2025133438A1

A quantum chip (101, 102, 103, 104) of qubits comprising a number of at least two superimposed layers, wherein a plurality of unit cells (1-9) is located at vertices of a lattice structure, said lattice comprises said vertices and defines a plurality of edges connecting neighboring vertices and a plurality of plaquettes, each plaquette being surrounded by a plurality of vertices, wherein each unit cell (1-9) of the plurality of unit cells (1-9) comprises a plurality of data qubits (1a, b, c, d, e, f - 9a, b, c, d, e, f) having connectivity between themselves, wherein each pair of neighboring unit cells (1-9) on the lattice has connectivity between at least one data qubit of one unit cell of the pair and at least one data qubit of another unit cell of the pair, said quantum chip (101, 102, 103, 104) further comprising for at least one plaquette (10, 20, 30, 40) of said lattice at least one syndrome qubit (11, 21, 31, 41), wherein there is connectivity between said at least one syndrome qubit (11, 21, 31, 41) and at least one data qubit of the unit cells at the vertices surrounding said plaquette (10, 20, 30, 40), wherein the connectivity of the quantum chip (101, 102, 103, 104) is distributed over at least two of the layers, and the data and syndrome qubits (1a, b, c, d, e, f - 9a, b, c, d, e, f; 11, 21, 31, 41) are located on at least one of the layers.

QUANTUM ASSISTED DATA-DRIVEN SIMULATION METHODS AND SYSTEMS

Resumen de: WO2025137660A1

Implementations involve generating quantum hybrid neural networks on a computing platform, by leveraging the computational advantages of both quantum and classical neural networks to enhance data processing capabilities. A hybrid neural network may include a quantum neural network and a classical neural network. The quantum neural network may process input data using a quantum processor to produce intermediate data. Subsequently, the classical neural network may process this intermediate data as its own inputs to generate output data. The hybrid neural network may then be stored back onto the electronic storage device for immediate or later use or training.

QUANTUM MEASUREMENT METHODS AND SYSTEMS

Resumen de: WO2025158256A1

Methods and apparatus for measuring quantum systems involve: applying first optical pulses to the quantum system and monitoring for a first resulting photon after each first optical pulse; in response to detecting one or more first resulting photons, performing a π operation on the quantum system; and, after performing the π operation, applying second optical pulses to the quantum system and monitoring for a second resulting photon after each second optical pulse. The first and second optical pulses comprise a wavelength corresponding to a transition from a first basis state of the quantum system to an excited state. A measurement result is determined based on results of the monitoring.

SYSTEM AND METHOD FOR TRAINING COMPOSITE PULSES FOR HIGH-FIDELITY ATOMIC QUBIT CONTROL

Resumen de: KR20260156114A

본 개시는 원자 큐비트의 정밀 제어를 위한 합성 펄스의 학습 방법과 시스템을 제공한다. 본 개시에 따르면, 컴퓨팅 시스템은, 인공지능을 기반으로 원자 배열 내 복수의 큐비트들의 큐비트들에 각각 초점을 맞춘 레이저 빔들에 대응하는 임의 큐비트 회전들을 위한 합성 펄스들을 생성하고, 합성 펄스들을 이용하여 레이저 빔들로 큐비트들의 각각을 로컬 제어한다.

アナログフォトニックコンピューティングシステム及び方法

Nº publicación: JP2026535985A 28/10/2026

Solicitante:

エスアールアイインターナショナル

Resumen de: WO2026010639A2

A system includes an encoding device configured to encode a continuous variable, a coefficient device including an addressable modulation element configured to modulate the encoded continuous variable, and an addressable detector configured to detect the encoded and modulated continuous variable.