13 research outputs found
Jigsaw @ AMI and HaSpeeDe2: Fine-Tuning a Pre-Trained Comment-Domain BERT Model
The Google Jigsaw team produced submissions for two of the EVALITA 2020 (Basile et al. 2020) shared tasks, based in part on the technology that powers the publicly available PerspectiveAPI comment evaluation service. We present a basic description of our submitted results and a review of the types of errors that our system made in these shared tasks
qTorch: The Quantum Tensor Contraction Handler
Classical simulation of quantum computation is necessary for studying the
numerical behavior of quantum algorithms, as there does not yet exist a large
viable quantum computer on which to perform numerical tests. Tensor network
(TN) contraction is an algorithmic method that can efficiently simulate some
quantum circuits, often greatly reducing the computational cost over methods
that simulate the full Hilbert space. In this study we implement a tensor
network contraction program for simulating quantum circuits using multi-core
compute nodes. We show simulation results for the Max-Cut problem on 3- through
7-regular graphs using the quantum approximate optimization algorithm (QAOA),
successfully simulating up to 100 qubits. We test two different methods for
generating the ordering of tensor index contractions: one is based on the tree
decomposition of the line graph, while the other generates ordering using a
straight-forward stochastic scheme. Through studying instances of QAOA
circuits, we show the expected result that as the treewidth of the quantum
circuit's line graph decreases, TN contraction becomes significantly more
efficient than simulating the whole Hilbert space. The results in this work
suggest that tensor contraction methods are superior only when simulating
Max-Cut/QAOA with graphs of regularities approximately five and below. Insight
into this point of equal computational cost helps one determine which
simulation method will be more efficient for a given quantum circuit. The
stochastic contraction method outperforms the line graph based method only when
the time to calculate a reasonable tree decomposition is prohibitively
expensive. Finally, we release our software package, qTorch (Quantum TensOR
Contraction Handler), intended for general quantum circuit simulation.Comment: 21 pages, 8 figure
Exponentially more precise quantum simulation of fermions in second quantization
We introduce novel algorithms for the quantum simulation of fermionic systems which are dramatically more efficient than those based on the Lie–Trotter–Suzuki decomposition. We present the first application of a general technique for simulating Hamiltonian evolution using a truncated Taylor series to obtain logarithmic scaling with the inverse of the desired precision. The key difficulty in applying algorithms for general sparse Hamiltonian simulation to fermionic simulation is that a query, corresponding to computation of an entry of the Hamiltonian, is costly to compute. This means that the gate complexity would be much higher than quantified by the query complexity. We solve this problem with a novel quantum algorithm for on-the-fly computation of integrals that is exponentially faster than classical sampling. While the approaches presented here are readily applicable to a wide class of fermionic models, we focus on quantum chemistry simulation in second quantization, perhaps the most studied application of Hamiltonian simulation. Our central result is an algorithm for simulating an N spin–orbital system that requires N t5 ( ) ~ gates. This approach is exponentially faster in the inverse precision and at least cubically faster in N than all previous approaches to chemistry simulation in the literature.Chemistry and Chemical Biolog
Quantum Simulation of Electronic Structure with Linear Depth and Connectivity
As physical implementations of quantum architectures emerge, it is increasingly important to consider the cost of algorithms for practical connectivities between qubits. We show that by using an arrangement of gates that we term the fermionic swap network, we can simulate a Trotter step of the electronic structure Hamiltonian in exactly N depth and with N2/2 two-qubit entangling gates, and prepare arbitrary Slater determinants in at most N/2 depth, all assuming only a minimal, linearly connected architecture. We conjecture that no explicit Trotter step of the electronic structure Hamiltonian is possible with fewer entangling gates, even with arbitrary connectivities. These results represent significant practical improvements on the cost of most Trotter-based algorithms for both variational and phase-estimation-based simulation of quantum chemistry.Chemistry and Chemical Biolog
OpenFermion: The Electronic Structure Package for Quantum Computers
Quantum simulation of chemistry and materials is predicted to be an important
application for both near-term and fault-tolerant quantum devices. However, at
present, developing and studying algorithms for these problems can be difficult
due to the prohibitive amount of domain knowledge required in both the area of
chemistry and quantum algorithms. To help bridge this gap and open the field to
more researchers, we have developed the OpenFermion software package
(www.openfermion.org). OpenFermion is an open-source software library written
largely in Python under an Apache 2.0 license, aimed at enabling the simulation
of fermionic models and quantum chemistry problems on quantum hardware.
Beginning with an interface to common electronic structure packages, it
simplifies the translation between a molecular specification and a quantum
circuit for solving or studying the electronic structure problem on a quantum
computer, minimizing the amount of domain expertise required to enter the
field. The package is designed to be extensible and robust, maintaining high
software standards in documentation and testing. This release paper outlines
the key motivations behind design choices in OpenFermion and discusses some
basic OpenFermion functionality which we believe will aid the community in the
development of better quantum algorithms and tools for this exciting area of
research.Comment: 22 page
EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020
Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)