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SUMMARY:G. Passarelli - Quantum annealing with dissipation
DTSTART;VALUE=DATE-TIME:20190917T153000Z
DTEND;VALUE=DATE-TIME:20190917T155000Z
DTSTAMP;VALUE=DATE-TIME:20260809T134950Z
UID:indico-contribution-285@cern.ch
DESCRIPTION:Quantum annealing is a branch of quantum computation aimed at 
 solving optimization problems. The optimization problem is encoded in a qu
 antum Hamiltonian\, whose ground state is the wanted solution. Standard qu
 antum annealing works as follows. The qubit system is prepared in the grou
 nd state of a simple Hamiltonian. Then\, the qubits are evolved in time us
 ing a time-dependent Hamiltonian that\, at the final time\, coincides with
  the one used to encode the optimization problem. If the evolution is slow
  compared to the minimal level spacing\, then the adiabatic theorem of qua
 ntum mechanics ensures macroscopic occupation of the target ground state a
 t the end of the evolution.\n\nThe algorithm is limited by small spectral 
 gaps\, occurring in the presence of quantum phase transitions\, and is rea
 lized in the presence of dissipation\, due to the interaction with the env
 ironment. While disruptive in general\, low-temperature environments can s
 ometimes favor quantum annealing\, improving the success probability. This
  beneficial effect can be further enhanced using pauses at specific times 
 during the dynamics\, favoring thermal relaxation towards the ground state
 .\n\nMy talk will present some results concerning quantum annealing in the
  presence of dissipation\, with and without pauses\, for the p-spin model\
 , a prototypical Ising system often used as a benchmark for quantum anneal
 ing. This model is hardly studied experimentally due to its full connectiv
 ity and the presence of p-body interactions. However\, its rotational symm
 etry allows for an easy numerical analysis in the weak coupling limit. I w
 ill show that dissipation and pauses can improve the success probability o
 f the quantum annealing of this model.\n\nhttps://indico.unina.it/event/24
 /contributions/285/
LOCATION:
URL:https://indico.unina.it/event/24/contributions/285/
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