16,676 research outputs found
Quantum Deep Hedging
Quantum machine learning has the potential for a transformative impact across
industry sectors and in particular in finance. In our work we look at the
problem of hedging where deep reinforcement learning offers a powerful
framework for real markets. We develop quantum reinforcement learning methods
based on policy-search and distributional actor-critic algorithms that use
quantum neural network architectures with orthogonal and compound layers for
the policy and value functions. We prove that the quantum neural networks we
use are trainable, and we perform extensive simulations that show that quantum
models can reduce the number of trainable parameters while achieving comparable
performance and that the distributional approach obtains better performance
than other standard approaches, both classical and quantum. We successfully
implement the proposed models on a trapped-ion quantum processor, utilizing
circuits with up to qubits, and observe performance that agrees well with
noiseless simulation. Our quantum techniques are general and can be applied to
other reinforcement learning problems beyond hedging
Neural Decoder for Topological Codes using Pseudo-Inverse of Parity Check Matrix
Recent developments in the field of deep learning have motivated many
researchers to apply these methods to problems in quantum information. Torlai
and Melko first proposed a decoder for surface codes based on neural networks.
Since then, many other researchers have applied neural networks to study a
variety of problems in the context of decoding. An important development in
this regard was due to Varsamopoulos et al. who proposed a two-step decoder
using neural networks. Subsequent work of Maskara et al. used the same concept
for decoding for various noise models. We propose a similar two-step neural
decoder using inverse parity-check matrix for topological color codes. We show
that it outperforms the state-of-the-art performance of non-neural decoders for
independent Pauli errors noise model on a 2D hexagonal color code. Our final
decoder is independent of the noise model and achieves a threshold of .
Our result is comparable to the recent work on neural decoder for quantum error
correction by Maskara et al.. It appears that our decoder has significant
advantages with respect to training cost and complexity of the network for
higher lengths when compared to that of Maskara et al.. Our proposed method can
also be extended to arbitrary dimension and other stabilizer codes.Comment: 12 pages, 12 figures, 2 tables, submitted to the 2019 IEEE
International Symposium on Information Theor
Supervised Quantum Learning without Measurements
We propose a quantum machine learning algorithm for efficiently solving a
class of problems encoded in quantum controlled unitary operations. The central
physical mechanism of the protocol is the iteration of a quantum time-delayed
equation that introduces feedback in the dynamics and eliminates the necessity
of intermediate measurements. The performance of the quantum algorithm is
analyzed by comparing the results obtained in numerical simulations with the
outcome of classical machine learning methods for the same problem. The use of
time-delayed equations enhances the toolbox of the field of quantum machine
learning, which may enable unprecedented applications in quantum technologies
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