5,003 research outputs found
Quantum Reverse Shannon Theorem
Dual to the usual noisy channel coding problem, where a noisy (classical or
quantum) channel is used to simulate a noiseless one, reverse Shannon theorems
concern the use of noiseless channels to simulate noisy ones, and more
generally the use of one noisy channel to simulate another. For channels of
nonzero capacity, this simulation is always possible, but for it to be
efficient, auxiliary resources of the proper kind and amount are generally
required. In the classical case, shared randomness between sender and receiver
is a sufficient auxiliary resource, regardless of the nature of the source, but
in the quantum case the requisite auxiliary resources for efficient simulation
depend on both the channel being simulated, and the source from which the
channel inputs are coming. For tensor power sources (the quantum generalization
of classical IID sources), entanglement in the form of standard ebits
(maximally entangled pairs of qubits) is sufficient, but for general sources,
which may be arbitrarily correlated or entangled across channel inputs,
additional resources, such as entanglement-embezzling states or backward
communication, are generally needed. Combining existing and new results, we
establish the amounts of communication and auxiliary resources needed in both
the classical and quantum cases, the tradeoffs among them, and the loss of
simulation efficiency when auxiliary resources are absent or insufficient. In
particular we find a new single-letter expression for the excess forward
communication cost of coherent feedback simulations of quantum channels (i.e.
simulations in which the sender retains what would escape into the environment
in an ordinary simulation), on non-tensor-power sources in the presence of
unlimited ebits but no other auxiliary resource. Our results on tensor power
sources establish a strong converse to the entanglement-assisted capacity
theorem.Comment: 35 pages, to appear in IEEE-IT. v2 has a fixed proof of the Clueless
Eve result, a new single-letter formula for the "spread deficit", better
error scaling, and an improved strong converse. v3 and v4 each make small
improvements to the presentation and add references. v5 fixes broken
reference
Toward 6G TK Extreme Connectivity: Architecture, Key Technologies and Experiments
Sixth-generation (6G) networks are evolving towards new features and
order-of-magnitude enhancement of systematic performance metrics compared to
the current 5G. In particular, the 6G networks are expected to achieve extreme
connectivity performance with Tbps-scale data rate, Kbps/Hz-scale spectral
efficiency, and s-scale latency. To this end, an original three-layer 6G
network architecture is designed to realise uniform full-spectrum cell-free
radio access and provide task-centric agile proximate support for diverse
applications. The designed architecture is featured by super edge node (SEN)
which integrates connectivity, computing, AI, data, etc. On this basis, a
technological framework of pervasive multi-level (PML) AI is established in the
centralised unit to enable task-centric near-real-time resource allocation and
network automation. We then introduce a radio access network (RAN) architecture
of full spectrum uniform cell-free networks, which is among the most attractive
RAN candidates for 6G TK extreme connectivity. A few most promising key
technologies, i.e., cell-free massive MIMO, photonics-assisted Terahertz
wireless access and spatiotemporal two-dimensional channel coding are further
discussed. A testbed is implemented and extensive trials are conducted to
evaluate innovative technologies and methodologies. The proposed 6G network
architecture and technological framework demonstrate exciting potentials for
full-service and full-scenario applications.Comment: 15 pages, 12 figure
Entanglement cost and quantum channel simulation
This paper proposes a revised definition for the entanglement cost of a
quantum channel . In particular, it is defined here to be the
smallest rate at which entanglement is required, in addition to free classical
communication, in order to simulate calls to , such that the
most general discriminator cannot distinguish the calls to
from the simulation. The most general discriminator is one who tests the
channels in a sequential manner, one after the other, and this discriminator is
known as a quantum tester [Chiribella et al., Phys. Rev. Lett., 101, 060401
(2008)] or one who is implementing a quantum co-strategy [Gutoski et al., Symp.
Th. Comp., 565 (2007)]. As such, the proposed revised definition of
entanglement cost of a quantum channel leads to a rate that cannot be smaller
than the previous notion of a channel's entanglement cost [Berta et al., IEEE
Trans. Inf. Theory, 59, 6779 (2013)], in which the discriminator is limited to
distinguishing parallel uses of the channel from the simulation. Under this
revised notion, I prove that the entanglement cost of certain
teleportation-simulable channels is equal to the entanglement cost of their
underlying resource states. Then I find single-letter formulas for the
entanglement cost of some fundamental channel models, including dephasing,
erasure, three-dimensional Werner--Holevo channels, epolarizing channels
(complements of depolarizing channels), as well as single-mode pure-loss and
pure-amplifier bosonic Gaussian channels. These examples demonstrate that the
resource theory of entanglement for quantum channels is not reversible.
Finally, I discuss how to generalize the basic notions to arbitrary resource
theories.Comment: 28 pages, 7 figure
Quantum-classical generative models for machine learning
The combination of quantum and classical computational resources towards more effective algorithms is one of the most promising research directions in computer science. In such a hybrid framework, existing quantum computers can be used to their fullest extent and for practical applications. Generative modeling is one of the applications that could benefit the most, either by speeding up the underlying sampling methods or by unlocking more general models. In this work, we design a number of hybrid generative models and validate them on real hardware and datasets. The quantum-assisted Boltzmann machine is trained to generate realistic artificial images on quantum annealers. Several challenges in state-of-the-art annealers shall be overcome before one can assess their actual performance. We attack some of the most pressing challenges such as the sparse qubit-to-qubit connectivity, the unknown effective-temperature, and the noise on the control parameters. In order to handle datasets of realistic size and complexity, we include latent variables and obtain a more general model called the quantum-assisted Helmholtz machine. In the context of gate-based computers, the quantum circuit Born machine is trained to encode a target probability distribution in the wavefunction of a set of qubits. We implement this model on a trapped ion computer using low-depth circuits and native gates. We use the generative modeling performance on the canonical Bars-and-Stripes dataset to design a benchmark for hybrid systems. It is reasonable to expect that quantum data, i.e., datasets of wavefunctions, will become available in the future. We derive a quantum generative adversarial network that works with quantum data. Here, two circuits are optimized in tandem: one tries to generate suitable quantum states, the other tries to distinguish between target and generated states
Gaussian Quantum Information
The science of quantum information has arisen over the last two decades
centered on the manipulation of individual quanta of information, known as
quantum bits or qubits. Quantum computers, quantum cryptography and quantum
teleportation are among the most celebrated ideas that have emerged from this
new field. It was realized later on that using continuous-variable quantum
information carriers, instead of qubits, constitutes an extremely powerful
alternative approach to quantum information processing. This review focuses on
continuous-variable quantum information processes that rely on any combination
of Gaussian states, Gaussian operations, and Gaussian measurements.
Interestingly, such a restriction to the Gaussian realm comes with various
benefits, since on the theoretical side, simple analytical tools are available
and, on the experimental side, optical components effecting Gaussian processes
are readily available in the laboratory. Yet, Gaussian quantum information
processing opens the way to a wide variety of tasks and applications, including
quantum communication, quantum cryptography, quantum computation, quantum
teleportation, and quantum state and channel discrimination. This review
reports on the state of the art in this field, ranging from the basic
theoretical tools and landmark experimental realizations to the most recent
successful developments.Comment: 51 pages, 7 figures, submitted to Reviews of Modern Physic
Signal Processing and Learning for Next Generation Multiple Access in 6G
Wireless communication systems to date primarily rely on the orthogonality of
resources to facilitate the design and implementation, from user access to data
transmission. Emerging applications and scenarios in the sixth generation (6G)
wireless systems will require massive connectivity and transmission of a deluge
of data, which calls for more flexibility in the design concept that goes
beyond orthogonality. Furthermore, recent advances in signal processing and
learning have attracted considerable attention, as they provide promising
approaches to various complex and previously intractable problems of signal
processing in many fields. This article provides an overview of research
efforts to date in the field of signal processing and learning for
next-generation multiple access, with an emphasis on massive random access and
non-orthogonal multiple access. The promising interplay with new technologies
and the challenges in learning-based NGMA are discussed
Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives
© 1998-2012 IEEE. Future 5th generation networks are expected to enable three key services-enhanced mobile broadband, massive machine type communications and ultra-reliable and low latency communications (URLLC). As per the 3rd generation partnership project URLLC requirements, it is expected that the reliability of one transmission of a 32 byte packet will be at least 99.999% and the latency will be at most 1 ms. This unprecedented level of reliability and latency will yield various new applications, such as smart grids, industrial automation and intelligent transport systems. In this survey we present potential future URLLC applications, and summarize the corresponding reliability and latency requirements. We provide a comprehensive discussion on physical (PHY) and medium access control (MAC) layer techniques that enable URLLC, addressing both licensed and unlicensed bands. This paper evaluates the relevant PHY and MAC techniques for their ability to improve the reliability and reduce the latency. We identify that enabling long-term evolution to coexist in the unlicensed spectrum is also a potential enabler of URLLC in the unlicensed band, and provide numerical evaluations. Lastly, this paper discusses the potential future research directions and challenges in achieving the URLLC requirements
Reinforcement Learning in Different Phases of Quantum Control
The ability to prepare a physical system in a desired quantum state is
central to many areas of physics such as nuclear magnetic resonance, cold
atoms, and quantum computing. Yet, preparing states quickly and with high
fidelity remains a formidable challenge. In this work we implement cutting-edge
Reinforcement Learning (RL) techniques and show that their performance is
comparable to optimal control methods in the task of finding short,
high-fidelity driving protocol from an initial to a target state in
non-integrable many-body quantum systems of interacting qubits. RL methods
learn about the underlying physical system solely through a single scalar
reward (the fidelity of the resulting state) calculated from numerical
simulations of the physical system. We further show that quantum state
manipulation, viewed as an optimization problem, exhibits a spin-glass-like
phase transition in the space of protocols as a function of the protocol
duration. Our RL-aided approach helps identify variational protocols with
nearly optimal fidelity, even in the glassy phase, where optimal state
manipulation is exponentially hard. This study highlights the potential
usefulness of RL for applications in out-of-equilibrium quantum physics.Comment: A legend for the videos referred to in the paper is available on
https://mgbukov.github.io/RL_movies
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