150 research outputs found
A hybrid algorithm framework for small quantum computers with application to finding Hamiltonian cycles
Recent works have shown that quantum computers can polynomially speed up
certain SAT-solving algorithms even when the number of available qubits is
significantly smaller than the number of variables. Here we generalise this
approach. We present a framework for hybrid quantum-classical algorithms which
utilise quantum computers significantly smaller than the problem size. Given an
arbitrarily small ratio of the quantum computer to the instance size, we
achieve polynomial speedups for classical divide-and-conquer algorithms,
provided that certain criteria on the time- and space-efficiency are met. We
demonstrate how this approach can be used to enhance Eppstein's algorithm for
the cubic Hamiltonian cycle problem, and achieve a polynomial speedup for any
ratio of the number of qubits to the size of the graph.Comment: 20+2 page
Simple proof of confidentiality for private quantum channels in noisy environments
Complete security proofs for quantum communication protocols can be
notoriously involved, which convolutes their verification, and obfuscates the
key physical insights the security finally relies on. In such cases, for the
majority of the community, the utility of such proofs may be restricted. Here
we provide a simple proof of confidentiality for parallel quantum channels
established via entanglement distillation based on hashing, in the presence of
noise, and a malicious eavesdropper who is restricted only by the laws of
quantum mechanics. The direct contribution lies in improving the linear
confidentiality levels of recurrence-type entanglement distillation protocols
to exponential levels for hashing protocols. The proof directly exploits the
security relevant physical properties: measurement-based quantum computation
with resource states and the separation of Bell-pairs from an eavesdropper. The
proof also holds for situations where Eve has full control over the input
states, and obtains all information about the operations and noise applied by
the parties. The resulting state after hashing is private, i.e., disentangled
from the eavesdropper. Moreover, the noise regimes for entanglement
distillation and confidentiality do not coincide: Confidentiality can be
guaranteed even in situation where entanglement distillation fails. We extend
our results to multiparty situations which are of special interest for secure
quantum networks.Comment: 5 + 11 pages, 0 + 4 figures, A. Pirker and M. Zwerger contributed
equally to this work, replaced with accepted versio
Long-range big quantum-data transmission
We introduce an alternative type of quantum repeater for long-range quantum
communication with improved scaling with the distance. We show that by
employing hashing, a deterministic entanglement distillation protocol with
one-way communication, one obtains a scalable scheme that allows one to reach
arbitrary distances, with constant overhead in resources per repeater station,
and ultrahigh rates. In practical terms, we show that also with moderate
resources of a few hundred qubits at each repeater station, one can reach
intercontinental distances. At the same time, a measurement-based
implementation allows one to tolerate high loss, but also operational and
memory errors of the order of several percent per qubit. This opens the way for
long-distance communication of big quantum data.Comment: revised manuscript including new result
Quantum learning unravels quantum system
A quantum computer has a decisive advantage in analyzing quantum experiment resultsQuantum Matter and Optic
Geometry of quantum observables and thermodynamics of small systems
The concept of ergodicity---the convergence of the temporal averages of
observables to their ensemble averages---is the cornerstone of thermodynamics.
The transition from a predictable, integrable behavior to ergodicity is one of
the most difficult physical phenomena to treat; the celebrated KAM theorem is
the prime example. This Letter is founded on the observation that for many
classical and quantum observables, the sum of the ensemble variance of the
temporal average and the ensemble average of temporal variance remains constant
across the integrability-ergodicity transition.
We show that this property induces a particular geometry of quantum
observables---Frobenius (also known as Hilbert-Schmidt) one---that naturally
encodes all the phenomena associated with the emergence of ergodicity: the
Eigenstate Thermalization effect, the decrease in the inverse participation
ratio, and the disappearance of the integrals of motion. As an application, we
use this geometry to solve a known problem of optimization of the set of
conserved quantities---regardless of whether it comes from symmetries or from
finite-size effects---to be incorporated in an extended thermodynamical theory
of integrable, near-integrable, or mesoscopic systems
Quantum-accessible reinforcement learning beyond strictly epochal environments
In recent years, quantum-enhanced machine learning has emerged as a
particularly fruitful application of quantum algorithms, covering aspects of
supervised, unsupervised and reinforcement learning. Reinforcement learning
offers numerous options of how quantum theory can be applied, and is arguably
the least explored, from a quantum perspective. Here, an agent explores an
environment and tries to find a behavior optimizing some figure of merit. Some
of the first approaches investigated settings where this exploration can be
sped-up, by considering quantum analogs of classical environments, which can
then be queried in superposition. If the environments have a strict periodic
structure in time (i.e. are strictly episodic), such environments can be
effectively converted to conventional oracles encountered in quantum
information. However, in general environments, we obtain scenarios that
generalize standard oracle tasks. In this work we consider one such
generalization, where the environment is not strictly episodic, which is mapped
to an oracle identification setting with a changing oracle. We analyze this
case and show that standard amplitude-amplification techniques can, with minor
modifications, still be applied to achieve quadratic speed-ups, and that this
approach is optimal for certain settings. This results constitutes one of the
first generalizations of quantum-accessible reinforcement learning.Comment: 8+9 pages, 2 figure
Three-dimensional Gross-Pitaevskii solitary waves in optical lattices: stabilization using the artificial quartic kinetic energy induced by lattice shaking
In this Letter, we show that a three-dimensional Bose-Einstein solitary wave
can become stable if the dispersion law is changed from quadratic to quartic.
We suggest a way to realize the quartic dispersion, using shaken optical
lattices. Estimates show that the resulting solitary waves can occupy as little
as -th of the Brillouin zone in each of the three directions and
contain as many as atoms, thus representing a \textit{fully
mobile} macroscopic three-dimensional object.Comment: 8 pages, 1 figure, accepted in Phys. Lett.
Flow Ambiguity: A Path Towards Classically Driven Blind Quantum Computation
Blind quantum computation protocols allow a user to delegate a computation to
a remote quantum computer in such a way that the privacy of their computation
is preserved, even from the device implementing the computation. To date, such
protocols are only known for settings involving at least two quantum devices:
either a user with some quantum capabilities and a remote quantum server or two
or more entangled but noncommunicating servers. In this work, we take the first
step towards the construction of a blind quantum computing protocol with a
completely classical client and single quantum server. Specifically, we show
how a classical client can exploit the ambiguity in the flow of information in
measurement-based quantum computing to construct a protocol for hiding critical
aspects of a computation delegated to a remote quantum computer. This ambiguity
arises due to the fact that, for a fixed graph, there exist multiple choices of
the input and output vertex sets that result in deterministic measurement
patterns consistent with the same fixed total ordering of vertices. This allows
a classical user, computing only measurement angles, to drive a
measurement-based computation performed on a remote device while hiding
critical aspects of the computation.Comment: (v3) 14 pages, 6 figures. expands introduction and definition of
flow, corrects typos to increase readability; contains a new figure to
illustrate example run of CDBQC protocol; minor changes to match the
published version.(v2) 12 pages, 5 figures. Corrects motivation for
quantities used in blindness analysi
Application of quantum-inspired generative models to small molecular datasets
Quantum and quantum-inspired machine learning has emerged as a promising and
challenging research field due to the increased popularity of quantum
computing, especially with near-term devices. Theoretical contributions point
toward generative modeling as a promising direction to realize the first
examples of real-world quantum advantages from these technologies. A few
empirical studies also demonstrate such potential, especially when considering
quantum-inspired models based on tensor networks. In this work, we apply
tensor-network-based generative models to the problem of molecular discovery.
In our approach, we utilize two small molecular datasets: a subset of
molecules from the QM9 dataset and a small in-house dataset of validated
antioxidants from TotalEnergies. We compare several tensor network models
against a generative adversarial network using different sample-based metrics,
which reflect their learning performances on each task, and multiobjective
performances using relevant molecular metrics per task. We also combined
the output of the models and demonstrate empirically that such a combination
can be beneficial, advocating for the unification of classical and
quantum(-inspired) generative learning.Comment: First versio
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