no code implementations • 4 Sep 2023 • Aakash Ravindra Shinde, Charu Jain, Amir Kalev
We find that a straightforward adaptation of a classical post-training method, known as neuron dropout, to the quantum setting leads to a significant and undesirable consequence: a substantial decrease in success probability of the QCNN.
1 code implementation • 14 Apr 2021 • Junhyung Lyle Kim, George Kollias, Amir Kalev, Ken X. Wei, Anastasios Kyrillidis
Despite being a non-convex method, \texttt{MiFGD} converges \emph{provably} close to the true density matrix at an accelerated linear rate, in the absence of experimental and statistical noise, and under common assumptions.
no code implementations • 9 Nov 2020 • Marco Paini, Amir Kalev, Dan Padilha, Brendan Ruck
We introduce an approximate description of an $N$-qubit state, which contains sufficient information to estimate the expectation value of any observable to a precision that is upper bounded by the ratio of a suitably-defined seminorm of the observable to the square root of the number of the system's identical preparations $M$, with no explicit dependence on $N$.
no code implementations • 23 Oct 2019 • Marco Paini, Amir Kalev
We introduce an approximate description of an $N$-qubit state, which contains sufficient information to estimate the expectation value of any observable with precision independent of $N$.
Quantum Physics
no code implementations • 6 Jun 2018 • Kelly Geyer, Anastasios Kyrillidis, Amir Kalev
Surprisingly, recent work argues that the choice of $r \leq n$ is not pivotal: even setting $U \in \mathbb{R}^{n \times n}$ is sufficient for factored gradient descent to find the rank-$r$ solution, which suggests that operating over the factors leads to an implicit regularization.
