Zobrazeno 1 - 10
of 209
pro vyhledávání: '"P Cerezo M"'
Autor:
Lerch, Sacha, Puig, Ricard, Rudolph, Manuel S., Angrisani, Armando, Jones, Tyson, Cerezo, M., Thanasilp, Supanut, Holmes, Zoë
Understanding the capabilities of classical simulation methods is key to identifying where quantum computers are advantageous. Not only does this ensure that quantum computers are used only where necessary, but also one can potentially identify subro
Externí odkaz:
http://arxiv.org/abs/2411.19896
Efficiently learning expectation values of a quantum state using classical shadow tomography has become a fundamental task in quantum information theory. In a classical shadows protocol, one measures a state in a chosen basis $\mathcal{W}$ after it h
Externí odkaz:
http://arxiv.org/abs/2410.23481
The Quantum Approximate Optimization Algorithm (QAOA) has been proposed as a method to obtain approximate solutions for combinatorial optimization tasks. In this work, we study the underlying algebraic properties of three QAOA ans\"atze for the maxim
Externí odkaz:
http://arxiv.org/abs/2410.05187
Autor:
Ijaz, Aroosa, Alderete, C. Huerta, Sauvage, Frédéric, Cincio, Lukasz, Cerezo, M., Goh, Matthew L.
Quantum sensing is one of the most promising applications for quantum technologies. However, reaching the ultimate sensitivities enabled by the laws of quantum mechanics can be a challenging task in realistic scenarios where noise is present. While s
Externí odkaz:
http://arxiv.org/abs/2410.00197
A unitary state $t$-design is an ensemble of pure quantum states whose moments match up to the $t$-th order those of states uniformly sampled from a $d$-dimensional Hilbert space. Typically, unitary state $t$-designs are obtained by evolving some ref
Externí odkaz:
http://arxiv.org/abs/2409.16500
Autor:
Angrisani, Armando, Schmidhuber, Alexander, Rudolph, Manuel S., Cerezo, M., Holmes, Zoë, Huang, Hsin-Yuan
We present a classical algorithm for estimating expectation values of arbitrary observables on most quantum circuits across all circuit architectures and depths, including those with all-to-all connectivity. We prove that for any architecture where e
Externí odkaz:
http://arxiv.org/abs/2409.01706
Quantum Convolutional Neural Networks (QCNNs) are widely regarded as a promising model for Quantum Machine Learning (QML). In this work we tie their heuristic success to two facts. First, that when randomly initialized, they can only operate on the i
Externí odkaz:
http://arxiv.org/abs/2408.12739
The spectral gap of local random quantum circuits is a fundamental property that determines how close the moments of the circuit's unitaries match those of a Haar random distribution. When studying spectral gaps, it is common to bound these quantitie
Externí odkaz:
http://arxiv.org/abs/2408.11201
We introduce a framework for simulating, on an $(n+1)$-qubit quantum computer, the action of a Gaussian Bosonic (GB) circuit on a state over $2^n$ modes. Specifically, we encode the initial bosonic state's expectation values over quadrature operators
Externí odkaz:
http://arxiv.org/abs/2407.06290
Parametrized and random unitary (or orthogonal) $n$-qubit circuits play a central role in quantum information. As such, one could naturally assume that circuits implementing symplectic transformation would attract similar attention. However, this is
Externí odkaz:
http://arxiv.org/abs/2405.10264