2,845 research outputs found
Quantum Compression and Quantum Learning via Information Theory
This thesis consists of two parts: quantum compression and quantum learning theory. A common theme between these problems is that we study them through the lens of information theory.
We first study the task of visible compression of an ensemble of quantum states with entanglement assistance in the one-shot setting. The protocols achieving the best compression use many more qubits of shared entanglement than the number of qubits in the states in the ensemble. Other compression protocols, with potentially higher communication cost, have entanglement cost bounded by the number of qubits in the given states. This motivates the question as to whether entanglement is truly necessary for compression, and if so, how much of it is needed. We show that an ensemble given by Jain, Radhakrishnan, and Sen (ICALP'03) cannot be compressed by more than a constant number of qubits without shared entanglement, while in the presence of shared entanglement, the communication cost of compression can be arbitrarily smaller than the entanglement cost.
Next, we study the task of quantum state redistribution, the most general version of compression of quantum states. We design a protocol for this task with communication cost in terms of a measure of distance from quantum Markov chains. More precisely, the distance is defined in terms of quantum max-relative entropy and quantum hypothesis testing entropy. Our result is the first to connect quantum state redistribution and Markov chains and gives an operational interpretation for a possible one-shot analogue of quantum conditional mutual information. The communication cost of our protocol is lower than all previously known ones and asymptotically achieves the well-known rate of quantum conditional mutual information.
In the last part, we focus on quantum algorithms for learning Boolean functions using quantum examples. We consider two commonly studied models of learning, namely, quantum PAC learning and quantum agnostic learning. We reproduce the optimal lower bounds by Arunachalam and de Wolf (JMLR’18) for the sample complexity of either of these models using information theory and spectral analysis. Our proofs are simpler than the previous ones and the techniques can be possibly extended to similar scenarios
Asymptotic Compressibility of Entanglement and Classical Communication in Distributed Quantum Computation
We consider implementations of a bipartite unitary on many pairs of unknown
input states by local operation and classical communication assisted by shared
entanglement. We investigate to what extent the entanglement cost and the
classical communication cost can be compressed by allowing nonzero but
vanishing error in the asymptotic limit of infinite pairs. We show that a lower
bound on the minimal entanglement cost, the forward classical communication
cost, and the backward classical communication cost per pair is given by the
Schmidt strength of the unitary. We also prove that an upper bound on these
three kinds of the cost is given by the amount of randomness that is required
to partially decouple a tripartite quantum state associated with the unitary.
In the proof, we construct a protocol in which quantum state merging is used.
For generalized Clifford operators, we show that the lower bound and the upper
bound coincide. We then apply our result to the problem of distributed
compression of tripartite quantum states, and derive a lower and an upper bound
on the optimal quantum communication rate required therein.Comment: Section II and VIII adde
Identifying the Information Gain of a Quantum Measurement
We show that quantum-to-classical channels, i.e., quantum measurements, can
be asymptotically simulated by an amount of classical communication equal to
the quantum mutual information of the measurement, if sufficient shared
randomness is available. This result generalizes Winter's measurement
compression theorem for fixed independent and identically distributed inputs
[Winter, CMP 244 (157), 2004] to arbitrary inputs, and more importantly, it
identifies the quantum mutual information of a measurement as the information
gained by performing it, independent of the input state on which it is
performed. Our result is a generalization of the classical reverse Shannon
theorem to quantum-to-classical channels. In this sense, it can be seen as a
quantum reverse Shannon theorem for quantum-to-classical channels, but with the
entanglement assistance and quantum communication replaced by shared randomness
and classical communication, respectively. The proof is based on a novel
one-shot state merging protocol for "classically coherent states" as well as
the post-selection technique for quantum channels, and it uses techniques
developed for the quantum reverse Shannon theorem [Berta et al., CMP 306 (579),
2011].Comment: v2: new result about non-feedback measurement simulation, 45 pages, 4
figure
Strong converse theorems using R\'enyi entropies
We use a R\'enyi entropy method to prove strong converse theorems for certain
information-theoretic tasks which involve local operations and quantum or
classical communication between two parties. These include state
redistribution, coherent state merging, quantum state splitting, measurement
compression with quantum side information, randomness extraction against
quantum side information, and data compression with quantum side information.
The method we employ in proving these results extends ideas developed by Sharma
[arXiv:1404.5940], which he used to give a new proof of the strong converse
theorem for state merging. For state redistribution, we prove the strong
converse property for the boundary of the entire achievable rate region in the
-plane, where and denote the entanglement cost and quantum
communication cost, respectively. In the case of measurement compression with
quantum side information, we prove a strong converse theorem for the classical
communication cost, which is a new result extending the previously known weak
converse. For the remaining tasks, we provide new proofs for strong converse
theorems previously established using smooth entropies. For each task, we
obtain the strong converse theorem from explicit bounds on the figure of merit
of the task in terms of a R\'enyi generalization of the optimal rate. Hence, we
identify candidates for the strong converse exponents for each task discussed
in this paper. To prove our results, we establish various new entropic
inequalities, which might be of independent interest. These involve conditional
entropies and mutual information derived from the sandwiched R\'enyi
divergence. In particular, we obtain novel bounds relating these quantities, as
well as the R\'enyi conditional mutual information, to the fidelity of two
quantum states.Comment: 40 pages, 5 figures; v4: Accepted for publication in Journal of
Mathematical Physic
One-shot lossy quantum data compression
We provide a framework for one-shot quantum rate distortion coding, in which
the goal is to determine the minimum number of qubits required to compress
quantum information as a function of the probability that the distortion
incurred upon decompression exceeds some specified level. We obtain a one-shot
characterization of the minimum qubit compression size for an
entanglement-assisted quantum rate-distortion code in terms of the smooth
max-information, a quantity previously employed in the one-shot quantum reverse
Shannon theorem. Next, we show how this characterization converges to the known
expression for the entanglement-assisted quantum rate distortion function for
asymptotically many copies of a memoryless quantum information source. Finally,
we give a tight, finite blocklength characterization for the
entanglement-assisted minimum qubit compression size of a memoryless isotropic
qubit source subject to an average symbol-wise distortion constraint.Comment: 36 page
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