27 research outputs found
Efficient motional-mode characterization for high-fidelity trapped-ion quantum computing
To achieve high-fidelity operations on a large-scale quantum computer, the
parameters of the physical system must be efficiently characterized with high
accuracy. For trapped ions, the entanglement between qubits are mediated by the
motional modes of the ion chain, and thus characterizing the motional-mode
parameters becomes essential. In this paper, we develop and explore physical
models that accurately predict both magnitude and sign of the Lamb-Dicke
parameters when the modes are probed {\it in parallel}. We further devise an
advanced characterization protocol that shortens the characterization time by
more than an order of magnitude, when compared to that of the conventional
method that only uses mode spectroscopy. We discuss potential ramifications of
our results to the development of a scalable trapped-ion quantum computer,
viewed through the lens of system-level resource trade offs.Comment: 18 pages, 8 figure
A generative modeling approach for benchmarking and training shallow quantum circuits
Hybrid quantum-classical algorithms provide ways to use noisy
intermediate-scale quantum computers for practical applications. Expanding the
portfolio of such techniques, we propose a quantum circuit learning algorithm
that can be used to assist the characterization of quantum devices and to train
shallow circuits for generative tasks. The procedure leverages quantum hardware
capabilities to its fullest extent by using native gates and their qubit
connectivity. We demonstrate that our approach can learn an optimal preparation
of the Greenberger-Horne-Zeilinger states, also known as "cat states". We
further demonstrate that our approach can efficiently prepare approximate
representations of coherent thermal states, wave functions that encode
Boltzmann probabilities in their amplitudes. Finally, complementing proposals
to characterize the power or usefulness of near-term quantum devices, such as
IBM's quantum volume, we provide a new hardware-independent metric called the
qBAS score. It is based on the performance yield in a specific sampling task on
one of the canonical machine learning data sets known as Bars and Stripes. We
show how entanglement is a key ingredient in encoding the patterns of this data
set; an ideal benchmark for testing hardware starting at four qubits and up. We
provide experimental results and evaluation of this metric to probe the trade
off between several architectural circuit designs and circuit depths on an
ion-trap quantum computer.Comment: 16 pages, 9 figures. Minor revisions. As published in npj Quantum
Informatio
Resource-Optimized Fermionic Local-Hamiltonian Simulation on Quantum Computer for Quantum Chemistry
The ability to simulate a fermionic system on a quantum computer is expected
to revolutionize chemical engineering, materials design, nuclear physics, to
name a few. Thus, optimizing the simulation circuits is of significance in
harnessing the power of quantum computers. Here, we address this problem in two
aspects. In the fault-tolerant regime, we optimize the \rzgate and \tgate
gate counts along with the ancilla qubit counts required, assuming the use of a
product-formula algorithm for implementation. We obtain a savings ratio of two
in the gate counts and a savings ratio of eleven in the number of ancilla
qubits required over the state of the art. In the pre-fault tolerant regime, we
optimize the two-qubit gate counts, assuming the use of the variational quantum
eigensolver (VQE) approach. Specific to the latter, we present a framework that
enables bootstrapping the VQE progression towards the convergence of the
ground-state energy of the fermionic system. This framework, based on
perturbation theory, is capable of improving the energy estimate at each cycle
of the VQE progression, by about a factor of three closer to the known
ground-state energy compared to the standard VQE approach in the test-bed,
classically-accessible system of the water molecule. The improved energy
estimate in turn results in a commensurate level of savings of quantum
resources, such as the number of qubits and quantum gates, required to be
within a pre-specified tolerance from the known ground-state energy. We also
explore a suite of generalized transformations of fermion to qubit operators
and show that resource-requirement savings of up to more than , in small
instances, is possible