Results 191 to 200 of about 55,151 (279)

A fault-tolerant neutral-atom architecture for universal quantum computation. [PDF]

Nature
Bluvstein D, Geim AA, Li SH, Evered SJ, Bonilla Ataides JP, Baranes G, Gu A, Manovitz T, Xu M, Kalinowski M, Majidy S, Kokail C, Maskara N, Trapp EC, Stewart LM, Hollerith S, Zhou H, Gullans MJ, Yelin SF, Greiner M, Vuletić V, Cain M, Lukin MD.   +22 more
europepmc   +1 more source

A Hybrid Semi‐Inverse Variational and Machine Learning Approach for the Schrödinger Equation

Advanced Physics Research, EarlyView.
A hybrid semi‐inverse variational and machine‐learning framework is presented for solving the Schrödinger equation with complex quantum potentials. Physics‐based variational solutions generate high‐quality training data, enabling Random Forest and Neural Network models to deliver near‐perfect energy predictions.
Khalid Reggab, Houssam Eddine Hailouf, Kingsley Onyebuchi Obodo, Mireille Megnidio‐Tchoukouegno, Mohammed Benali Kanoun, Souraya Goumri‐Said   +5 more
wiley   +1 more source

Artificial intelligence for quantum computing. [PDF]

Nat Commun
Alexeev Y, Farag MH, Patti TL, Wolf ME, Ares N, Aspuru-Guzik A, Benjamin SC, Cai Z, Cao S, Chamberland C, Chandani Z, Fedele F, Hamamura I, Harrigan N, Kim JS, Kyoseva E, Lietz JG, Lubowe T, McCaskey A, Melko RG, Nakaji K, Peruzzo A, Rao P, Schmitt B, Stanwyck S, Tubman NM, Wang H, Costa T.   +27 more
europepmc   +1 more source

T cell plasticity in systemic lupus erythematosus revealed by large‐scale T cell receptor repertoire and transcriptome studies

Arthritis &Rheumatology, Accepted Article.
Objective We aimed to characterize CD4+ T cell plasticity in human SLE by leveraging TCR repertoire features as markers of prior lineage states, integrating TCR and transcriptomic profiling to delineate plasticity patterns and evaluate their association with clinical disease activity.
Yasuo Nagafuchi, Masahiro Nakano, Kaitlyn A. Lagattuta, Mineto Ota, Hiroaki Hatano, Haruka Takahashi, Takahiro Itamiya, Hajime Inokuchi, Soumya Raychaudhuri, Tomohisa Okamura, Keishi Fujio, Kazuyoshi Ishigaki   +11 more
wiley   +1 more source

Determining the Upper-Bound on the Code Distance of Quantum Stabilizer Codes Through the Monte Carlo Method Based on Fully Decoupled Belief Propagation. [PDF]

Entropy (Basel)
Liang Z, Wang Z, Yi Z, Yang F, Wang X.
europepmc   +1 more source

Addressing Small Data Challenges in Biopharmaceutical Development and Manufacturing: A Mini Review of Multi‐Fidelity Techniques

Biotechnology and Bioengineering, EarlyView.
ABSTRACT The growing demand for biopharmaceutical products reflects their effectiveness in medical treatments. However, developing new biopharmaceuticals remains a major bottleneck, often taking up to a decade before market approval. Machine learning (ML) models have the potential to accelerate this process, but their success depends on access to large
Mohammad Golzarijalal, Uwe Aickelin, Ellen Otte   +2 more
wiley   +1 more source

Rate-Adaptive Information Reconciliation for CV-QKD Systems at Low Signal-to-Noise Ratios. [PDF]

Entropy (Basel)
Fu H, Dai J, Feng Y, Hai H, Ge H, Huang P, Jiang XQ.   +6 more
europepmc   +1 more source

Green Innovation and Firm Efficiency: The Role of Institutional Quality in Italian High‐Tech Manufacturing Sectors

Business Strategy and the Environment, EarlyView.
ABSTRACT The aim of this research is to verify whether institutional quality affects the relationship between green innovation and firm efficiency within the high‐tech manufacturing sectors. To estimate jointly the parameters of a stochastic frontier and the coefficients of a model explaining technical inefficiency, we employed the one‐step estimation ...
Mariarosaria Agostino, Sandro Rondinella, Marianna Succurro   +2 more
wiley   +1 more source

An integrated optical hardware for realization of quantum error correction operators. [PDF]

Sci Rep
Armaghani S, Rostami A.
europepmc   +1 more source

Machine Learning Paradigm for Advanced Battery Electrolyte Development

Carbon Energy, EarlyView.
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su, Mengfan Pei, Keyu Chen, Xigao Jian, Fangyuan Hu   +4 more
wiley   +1 more source