4 research outputs found
Cutting multi-control quantum gates with ZX calculus
Circuit cutting, the decomposition of a quantum circuit into independent
partitions, has become a promising avenue towards experiments with larger
quantum circuits in the noisy-intermediate scale quantum (NISQ) era. While
previous work focused on cutting qubit wires or two-qubit gates, in this work
we introduce a method for cutting multi-controlled Z gates. We construct a
decomposition and prove the upper bound on the associated
sampling overhead, where is the number of cuts in the circuit. This bound
is independent of the number of control qubits but can be further reduced to
for the special case of CCZ gates. Furthermore, we
evaluate our proposal on IBM hardware and experimentally show noise resilience
due to the strong reduction of CNOT gates in the cut circuits
A Survey on Quantum Reinforcement Learning
Quantum reinforcement learning is an emerging field at the intersection of
quantum computing and machine learning. While we intend to provide a broad
overview of the literature on quantum reinforcement learning (our
interpretation of this term will be clarified below), we put particular
emphasis on recent developments. With a focus on already available noisy
intermediate-scale quantum devices, these include variational quantum circuits
acting as function approximators in an otherwise classical reinforcement
learning setting. In addition, we survey quantum reinforcement learning
algorithms based on future fault-tolerant hardware, some of which come with a
provable quantum advantage. We provide both a birds-eye-view of the field, as
well as summaries and reviews for selected parts of the literature.Comment: 62 pages, 16 figure
Uncovering Instabilities in Variational-Quantum Deep Q-Networks
Deep Reinforcement Learning (RL) has considerably advanced over the past
decade. At the same time, state-of-the-art RL algorithms require a large
computational budget in terms of training time to converge. Recent work has
started to approach this problem through the lens of quantum computing, which
promises theoretical speed-ups for several traditionally hard tasks. In this
work, we examine a class of hybrid quantum-classical RL algorithms that we
collectively refer to as variational quantum deep Q-networks (VQ-DQN). We show
that VQ-DQN approaches are subject to instabilities that cause the learned
policy to diverge, study the extent to which this afflicts reproduciblity of
established results based on classical simulation, and perform systematic
experiments to identify potential explanations for the observed instabilities.
Additionally, and in contrast to most existing work on quantum reinforcement
learning, we execute RL algorithms on an actual quantum processing unit (an IBM
Quantum Device) and investigate differences in behaviour between simulated and
physical quantum systems that suffer from implementation deficiencies. Our
experiments show that, contrary to opposite claims in the literature, it cannot
be conclusively decided if known quantum approaches, even if simulated without
physical imperfections, can provide an advantage as compared to classical
approaches. Finally, we provide a robust, universal and well-tested
implementation of VQ-DQN as a reproducible testbed for future experiments.Comment: Authors Maja Franz, Lucas Wolf, Maniraman Periyasamy contributed
equally (name order randomised). To be published in the Journal of The
Franklin Institut
Incremental Data-Uploading for Full-Quantum Classification
The data representation in a machine-learning model strongly influences its
performance. This becomes even more important for quantum machine learning
models implemented on noisy intermediate scale quantum (NISQ) devices. Encoding
high dimensional data into a quantum circuit for a NISQ device without any loss
of information is not trivial and brings a lot of challenges. While simple
encoding schemes (like single qubit rotational gates to encode high dimensional
data) often lead to information loss within the circuit, complex encoding
schemes with entanglement and data re-uploading lead to an increase in the
encoding gate count. This is not well-suited for NISQ devices. This work
proposes 'incremental data-uploading', a novel encoding pattern for high
dimensional data that tackles these challenges. We spread the encoding gates
for the feature vector of a given data point throughout the quantum circuit
with parameterized gates in between them. This encoding pattern results in a
better representation of data in the quantum circuit with a minimal
pre-processing requirement. We show the efficiency of our encoding pattern on a
classification task using the MNIST and Fashion-MNIST datasets, and compare
different encoding methods via classification accuracy and the effective
dimension of the model.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl