1,165 research outputs found
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
Synthesizing Quantum-Circuit Optimizers
Near-term quantum computers are expected to work in an environment where each
operation is noisy, with no error correction. Therefore, quantum-circuit
optimizers are applied to minimize the number of noisy operations. Today,
physicists are constantly experimenting with novel devices and architectures.
For every new physical substrate and for every modification of a quantum
computer, we need to modify or rewrite major pieces of the optimizer to run
successful experiments. In this paper, we present QUESO, an efficient approach
for automatically synthesizing a quantum-circuit optimizer for a given quantum
device. For instance, in 1.2 minutes, QUESO can synthesize an optimizer with
high-probability correctness guarantees for IBM computers that significantly
outperforms leading compilers, such as IBM's Qiskit and TKET, on the majority
(85%) of the circuits in a diverse benchmark suite.
A number of theoretical and algorithmic insights underlie QUESO: (1) An
algebraic approach for representing rewrite rules and their semantics. This
facilitates reasoning about complex symbolic rewrite rules that are beyond the
scope of existing techniques. (2) A fast approach for probabilistically
verifying equivalence of quantum circuits by reducing the problem to a special
form of polynomial identity testing. (3) A novel probabilistic data structure,
called a polynomial identity filter (PIF), for efficiently synthesizing rewrite
rules. (4) A beam-search-based algorithm that efficiently applies the
synthesized symbolic rewrite rules to optimize quantum circuits.Comment: Full version of PLDI 2023 pape
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