Hi everyone,
We will have a special group meeting tomorrow. This will feature Salvatore, Ryan, and Gian presenting the APS talks they will be giving next week. The schedule is as follows.
2.30pm - 2.50pm Salvatore
2.50pm - 3.10pm Ryan
3.10pm - 3.30pm Gian Giacomo
Please see below for titles and abstracts of their talks.
Cheers,
Jennifer
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Annealing of Embedded Spin Glasses
Salvatore Mandra
We discuss recent results on thermal and quantum annealing
of random spin glasses on fully-connected graphs and on fully-connected bipartite
graphs. After the description of the embedding of their classical Hamiltonian onto
Chimera graphs, we discuss what are the optimal embedding parameters, in relation
with the signatures of the quantum phase transitions occurring during the annealing.
Finally, we compare numerical simulations and analytical expectations for both
the embedded spin glass models with results on the non-embedded models and with
runs on D-Wave Machine installed at NASA Ames.
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The Chemical Basis of Trotter Errors in Quantum Simulations of Chemistry
Ryan Babbush
Although the simulation of quantum chemistry is one of the most anticipated applications of quantum computing, the scaling of known upper bounds on the complexity of these algorithms is daunting. Prior work has bounded errors due to Trotterization in terms of the norm of the error operator and analyzed scaling with respect to the number of spin-orbitals. However, we find that these error bounds can be loose by up to sixteen orders of magnitude for some molecules. Furthermore, numerical results for small systems fail to reveal any clear correlation between ground state error and number of spin-orbitals. We instead argue that chemical properties, such as the maximum nuclear charge in a molecule and the filling fraction of orbitals, can be decisive for determining the cost of a quantum simulation. Our analysis motivates several strategies to use classical processing to further reduce the required Trotter step size and to estimate the necessary number of steps, without requiring additional quantum resources.
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Dimensionality reduction for adiabatic quantum optimizer in presence of local disorder
Gian Guerreschi
Adiabatic quantum optimization (AQO) is a procedure to solve a vast class of optimization problems by slowly changing the Hamiltonian of a quantum system. The evolution time necessary for the algorithm to be successful scales inversely with the minimum energy gap encountered during the dynamics. Unfortunately, the direct calculation of such gap is strongly limited by the exponential growth in dimensionality of quantum systems. Although many special-purpose methods have been devised to reduce the effective dimensionality of the Hilbert space, they are strongly limited to particular classes of problems with evident symmetries. Here, we propose and implement a reduction method that does not rely on any explicit symmetry and which requires, under certain but quite general conditions, only a polynomial amount of classical resources. A natural and important application is the analysis of AQO in presence of local disorder. In this respect, we show that AQO, even when affected by random noise, can still be faster than any classical algorithm.