QuForge is a Python-based library designed to efficiently simulate quantum circuits with qudits, leveraging sparse matrix representations and accelerating devices like GPUs and TPUs to enable scalable and differentiable quantum computing research.
무작위 심플렉틱 상태는 모든 차수에 대해 무작위 유니터리 상태와 통계적으로 구분할 수 없다.
Ensembles of symplectic random states are unconditionally indistinguishable from ensembles of unitary random states, as their moments match to all orders.
Quantum machine learning techniques can be leveraged to efficiently compile quantum dynamics into shallow variational quantum circuits, outperforming standard Trotterization methods in both accuracy and resource cost.
本文提出了一種利用平均平方失真時間準則來優化部分隔離量子諧振子記憶系統的方法。
부분적으로 격리된 양자 조화 진동자 메모리 시스템의 평균 제곱 상관 시간을 최대화하여 양자 메모리 성능을 향상시킬 수 있다.
部分的に隔離された量子調和振動子メモリシステムの平均二乗減衰時間を最大化することで、その量子メモリ性能を向上させることができる。
Partially isolated subsystems of open quantum harmonic oscillators can be optimized to improve their performance as quantum memories by maximizing their mean square decoherence time.
양자 컴퓨팅을 활용하여 기능적 자기공명영상(fMRI) 데이터의 지역적 및 전역적 특징을 효율적으로 추출하는 새로운 방법론을 제안한다.
A novel quantum-based approach named Hierarchical Quantum Control Gates (HQCG) that can efficiently extract both local and global features from high-dimensional fMRI signals, outperforming classical methods.