1,241 research outputs found
On machine creativity and the notion of free will
We discuss the possibility of freedom of action in embodied systems that are,
with no exception and at all scales of their body, subject to physical law. We
relate the discussion to a model of an artificial agent that exhibits a
primitive notion of creativity and freedom in dealing with its environment,
which is part of a recently introduced scheme of information processing called
projective simulation. This provides an explicit proposal on how we can
reconcile our understanding of universal physical law with the idea that higher
biological entities can acquire a notion of freedom that allows them to
increasingly detach themselves from a strict causal embedding into the
surrounding world.Comment: 7 pages, 2 figure
Quantum computing via measurements only
A quantum computer promises efficient processing of certain computational
tasks that are intractable with classical computer technology. While basic
principles of a quantum computer have been demonstrated in the laboratory,
scalability of these systems to a large number of qubits, essential for
practical applications such as the Shor algorithm, represents a formidable
challenge. Most of the current experiments are designed to implement sequences
of highly controlled interactions between selected particles (qubits), thereby
following models of a quantum computer as a (sequential) network of quantum
logic gates. Here we propose a different model of a scalable quantum computer.
In our model, the entire resource for the quantum computation is provided
initially in form of a specific entangled state (a so-called cluster state) of
a large number of qubits. Information is then written onto the cluster,
processed, and read out form the cluster by one-particle measurements only. The
entangled state of the cluster thus serves as a universal substrate for any
quantum computation. Cluster states can be created efficiently in any system
with a quantum Ising-type interaction (at very low temperatures) between
two-state particles in a lattice configuration.Comment: 4 pages, 2 figure
Intra-molecular refrigeration in enzymes
We present a simple mechanism for intra-molecular refrigeration, where parts
of a molecule are actively cooled below the environmental temperature. We
discuss the potential role and applications of such a mechanism in biology, in
particular in enzymatic reactions.Comment: 6 pages, 5 figure
Algorithmic complexity of quantum states
In this paper we give a definition for the Kolmogorov complexity of a pure
quantum state. In classical information theory the algorithmic complexity of a
string is a measure of the information needed by a universal machine to
reproduce the string itself. We define the complexity of a quantum state by
means of the classical description complexity of an (abstract) experimental
procedure that allows us to prepare the state with a given fidelity. We argue
that our definition satisfies the intuitive idea of complexity as a measure of
``how difficult'' it is to prepare a state. We apply this definition to give an
upper bound on the algorithmic complexity of a number of states.Comment: 24 pages, no figure
Quantum machine learning with glow for episodic tasks and decision games
We consider a general class of models, where a reinforcement learning (RL)
agent learns from cyclic interactions with an external environment via
classical signals. Perceptual inputs are encoded as quantum states, which are
subsequently transformed by a quantum channel representing the agent's memory,
while the outcomes of measurements performed at the channel's output determine
the agent's actions. The learning takes place via stepwise modifications of the
channel properties. They are described by an update rule that is inspired by
the projective simulation (PS) model and equipped with a glow mechanism that
allows for a backpropagation of policy changes, analogous to the eligibility
traces in RL and edge glow in PS. In this way, the model combines features of
PS with the ability for generalization, offered by its physical embodiment as a
quantum system. We apply the agent to various setups of an invasion game and a
grid world, which serve as elementary model tasks allowing a direct comparison
with a basic classical PS agent.Comment: 20 pages, 14 figure
Entanglement purification and quantum error correction
We give a review on entanglement purification for bipartite and multipartite
quantum states, with the main focus on theoretical work carried out by our
group in the last couple of years. We discuss entanglement purification in the
context of quantum communication, where we emphasize its close relation to
quantum error correction. Various bipartite and multipartite entanglement
purification protocols are discussed, and their performance under idealized and
realistic conditions is studied. Several applications of entanglement
purification in quantum communication and computation are presented, which
highlights the fact that entanglement purification is a fundamental tool in
quantum information processing.Comment: review article; 48 pages, 18 figures; V2:published versio
Computational Model for the One-Way Quantum Computer: Concepts and Summary
The one-way quantum computer (QCc) is a universal scheme of quantum
computation consisting only of one-qubit measurements on a particular entangled
multi-qubit state, the cluster state. The computational model underlying the
QCc is different from the quantum logic network model and it is based on
different constituents. It has no quantum register and does not consist of
quantum gates. The QCc is nevertheless quantum mechanical since it uses a
highly entangled cluster state as the central physical resource. The scheme
works by measuring quantum correlations of the universal cluster state.Comment: 9 pages, 4 figure
Machine learning \& artificial intelligence in the quantum domain
Quantum information technologies, and intelligent learning systems, are both
emergent technologies that will likely have a transforming impact on our
society. The respective underlying fields of research -- quantum information
(QI) versus machine learning (ML) and artificial intelligence (AI) -- have
their own specific challenges, which have hitherto been investigated largely
independently. However, in a growing body of recent work, researchers have been
probing the question to what extent these fields can learn and benefit from
each other. QML explores the interaction between quantum computing and ML,
investigating how results and techniques from one field can be used to solve
the problems of the other. Recently, we have witnessed breakthroughs in both
directions of influence. For instance, quantum computing is finding a vital
application in providing speed-ups in ML, critical in our "big data" world.
Conversely, ML already permeates cutting-edge technologies, and may become
instrumental in advanced quantum technologies. Aside from quantum speed-up in
data analysis, or classical ML optimization used in quantum experiments,
quantum enhancements have also been demonstrated for interactive learning,
highlighting the potential of quantum-enhanced learning agents. Finally, works
exploring the use of AI for the very design of quantum experiments, and for
performing parts of genuine research autonomously, have reported their first
successes. Beyond the topics of mutual enhancement, researchers have also
broached the fundamental issue of quantum generalizations of ML/AI concepts.
This deals with questions of the very meaning of learning and intelligence in a
world that is described by quantum mechanics. In this review, we describe the
main ideas, recent developments, and progress in a broad spectrum of research
investigating machine learning and artificial intelligence in the quantum
domain.Comment: Review paper. 106 pages. 16 figure
Quantum mixing of Markov chains for special distributions
The preparation of the stationary distribution of irreducible,
time-reversible Markov chains is a fundamental building block in many heuristic
approaches to algorithmically hard problems. It has been conjectured that
quantum analogs of classical mixing processes may offer a generic quadratic
speed-up in realizing such stationary distributions. Such a speed-up would also
imply a speed-up of a broad family of heuristic algorithms.
However, a true quadratic speed up has thus far only been demonstrated for
special classes of Markov chains. These results often presuppose a regular
structure of the underlying graph of the Markov chain, and also a regularity in
the transition probabilities.
In this work, we demonstrate a true quadratic speed-up for a class of Markov
chains where the restriction is only on the form of the stationary
distribution, rather than directly on the Markov chain structure itself. In
particular, we show efficient mixing can be achieved when it is beforehand
known that the distribution is monotonically decreasing relative to a known
order on the state space. Following this, we show that our approach extends to
a wider class of distributions, where only a fraction of the shape of the
distribution is known to be monotonic. Our approach is built on the
Szegedy-type quantization of transition operators.Comment: 15 page
Estimation of coherent error sources from stabilizer measurements
In the context of measurement-based quantum computation a way of maintaining
the coherence of a graph state is to measure its stabilizer operators. Aside
from performing quantum error correction, it is possible to exploit the
information gained from these measurements to characterize and then counteract
a coherent source of errors; that is, to determine all the parameters of an
error channel that applies a fixed - but unknown - unitary operation to the
physical qubits. Such a channel is generated, e.g., by local stray fields that
act on the qubits. We study the case in which each qubit of a given graph state
may see a different error channel and we focus on channels given by a rotation
on the Bloch sphere around either the x, y or z axis, for which analytical
results can be given in a compact form. The possibility of reconstructing the
channels at all qubits depends non-trivially on the topology of the graph
state. We prove via perturbation methods that the reconstruction process is
robust and supplement the analytic results with numerical evidence.Comment: 18 pages, 3 figures, 3 table
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