Ricard Puig
@q_pvricard
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Ph.D. candidate in physics at @EPFL. working on quantum machine learning, quantum simulation and quantum metrology.
Lausanne, Switzerland
Joined June 2018
Have you ever wondered how much work can be offloaded from quantum computers when simulating an expectation landscape of a parametrized quantum circuit🤔? In our new work “Efficient quantum-enhanced classical simulation for patches of quantum landscapes” we tackle this question.
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New paper out! We present a general bound to study regions with gradients in BP landscapes and study how precise warm starting strategies have to get! For more details follow Hela's great thread
New paper on arxiv 🔥We present a unifying characterization of “patches” in BP landscapes with substantial gradients for a broad family of circuits including physically motivated ansatz that could not be analyzed before !
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This paper was published yesterday in PRX Quantum!
Is a barren plateau landscape trainable? If we start close to the actual solution, then probably yes!? Here we study variational quantum simulation to explore whether warm starts can be a possible solution to exponential concentration.
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RT @PRX_Quantum: Initialization closer to a solution in variational quantum simulation yields guarantees on initial trainability, but no gu…
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RT @qZoeHolmes: Nature is great at cooling things but getting a quantum computer to do it is hard. Give us an approx ground state, we pres…
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Thank to all my collaborators for making this possible Pavel Sekatski, Paolo Andrea Erdman, Paolo Abiuso, John Calsamiglia and MartĂ Perarnau-Llobet
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Big thanks to all the coauthors @LerchSacha @QuantumManuel @acl_angrisani @TysonRayJones @MvsCerezo @s_thanasilp @qZoeHolmes
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