139 research outputs found
Synthesis and Optimization of Reversible Circuits - A Survey
Reversible logic circuits have been historically motivated by theoretical
research in low-power electronics as well as practical improvement of
bit-manipulation transforms in cryptography and computer graphics. Recently,
reversible circuits have attracted interest as components of quantum
algorithms, as well as in photonic and nano-computing technologies where some
switching devices offer no signal gain. Research in generating reversible logic
distinguishes between circuit synthesis, post-synthesis optimization, and
technology mapping. In this survey, we review algorithmic paradigms ---
search-based, cycle-based, transformation-based, and BDD-based --- as well as
specific algorithms for reversible synthesis, both exact and heuristic. We
conclude the survey by outlining key open challenges in synthesis of reversible
and quantum logic, as well as most common misconceptions.Comment: 34 pages, 15 figures, 2 table
CryptoQFL: Quantum Federated Learning on Encrypted Data
Recent advancements in Quantum Neural Networks (QNNs) have demonstrated
theoretical and experimental performance superior to their classical
counterparts in a wide range of applications. However, existing centralized
QNNs cannot solve many real-world problems because collecting large amounts of
training data to a common public site is time-consuming and, more importantly,
violates data privacy. Federated Learning (FL) is an emerging distributed
machine learning framework that allows collaborative model training on
decentralized data residing on multiple devices without breaching data privacy.
Some initial attempts at Quantum Federated Learning (QFL) either only focus on
improving the QFL performance or rely on a trusted quantum server that fails to
preserve data privacy. In this work, we propose CryptoQFL, a QFL framework that
allows distributed QNN training on encrypted data. CryptoQFL is (1) secure,
because it allows each edge to train a QNN with local private data, and encrypt
its updates using quantum \homo~encryption before sending them to the central
quantum server; (2) communication-efficient, as CryptoQFL quantize local
gradient updates to ternary values, and only communicate non-zero values to the
server for aggregation; and (3) computation-efficient, as CryptoQFL presents an
efficient quantum aggregation circuit with significantly reduced latency
compared to state-of-the-art approaches
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