QMATH Masterclass: Quantum Learning Theory Dates: Monday, August 21, 2023 to Friday, August 25, 2023 Submitted by ahwerner on Fri, 28/04/2023 – 09:44. Submission deadline: Friday, June 30, 2023 Registration deadline: Friday, June 30, 2023 […]
The Series B round was led by Molten Ventures with additional participation from Altair and returning investors Cambridge Innovation Capital (CIC), Amadeus Capital Partners and the National Security Strategic Investment Fund (NSSIF). This follows a […]
Artificial intelligence has made incredible progress in the last decade, but in one crucial aspect, it still lags behind the theoretical computer science of the 1990s: namely, there is no essay describing five potential worlds […]
Machine learning models are incredibly powerful tools. They extract deeply hidden patterns in large data sets that our limited human brains can’t parse. These complex algorithms, then, need to be incomprehensible “black boxes,” because a […]
Quantum 7, 992 (2023). https://doi.org/10.22331/q-2023-04-27-992 Since linear-optical two-photon gates are inherently probabilistic, measurement-based implementations are particularly well suited for photonic platforms: a large highly-entangled photonic resource state, called a graph state, is consumed through measurements […]
Quantum 7, 991 (2023). https://doi.org/10.22331/q-2023-04-27-991 In classical physics, memoryless dynamics and Markovian statistics are one and the same. This is not true for quantum dynamics, first and foremost because quantum measurements are invasive. Going beyond […]
A newly discovered phenomenon dubbed “collectively induced transparency” (CIT) causes groups of atoms to abruptly stop reflecting light at specific frequencies. Click to rate this post! [Total: 0 Average: 0]You have already voted for this […]
Quantum 7, 990 (2023). https://doi.org/10.22331/q-2023-04-27-990 In the context of irreversible dynamics, associating to a physical process its intuitive reverse can result to be a quite ambiguous task. It is a standard choice to define the […]
Quantum 7, 989 (2023). https://doi.org/10.22331/q-2023-04-27-989 In the training of over-parameterized model functions via gradient descent, sometimes the parameters do not change significantly and remain close to their initial values. This phenomenon is called $textit{lazy training}$ […]
Quantum 7, 988 (2023). https://doi.org/10.22331/q-2023-04-27-988 Linear regression is a widely used technique to fit linear models and finds widespread applications across different areas such as machine learning and statistics. In most real-world scenarios, however, linear […]
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