852 research outputs found
Quantum Inference on Bayesian Networks
Performing exact inference on Bayesian networks is known to be #P-hard.
Typically approximate inference techniques are used instead to sample from the
distribution on query variables given the values of evidence variables.
Classically, a single unbiased sample is obtained from a Bayesian network on
variables with at most parents per node in time
, depending critically on , the probability the
evidence might occur in the first place. By implementing a quantum version of
rejection sampling, we obtain a square-root speedup, taking
time per sample. We exploit the Bayesian
network's graph structure to efficiently construct a quantum state, a q-sample,
representing the intended classical distribution, and also to efficiently apply
amplitude amplification, the source of our speedup. Thus, our speedup is
notable as it is unrelativized -- we count primitive operations and require no
blackbox oracle queries.Comment: 8 pages, 3 figures. Submitted to PR
Detection of entangled states supported by reinforcement learning
Discrimination of entangled states is an important element of quantum
enhanced metrology. This typically requires low-noise detection technology.
Such a challenge can be circumvented by introducing nonlinear readout process.
Traditionally, this is realized by reversing the very dynamics that generates
the entangled state, which requires a full control over the system evolution.
In this work, we present nonlinear readout of highly entangled states by
employing reinforcement learning (RL) to manipulate the spin-mixing dynamics in
a spin-1 atomic condensate. The RL found results in driving the system towards
an unstable fixed point, whereby the (to be sensed) phase perturbation is
amplified by the subsequent spin-mixing dynamics. Working with a condensate of
10900 {87}^Rb atoms, we achieve a metrological gain of 6.97 dB beyond the
classical precision limit. Our work would open up new possibilities in
unlocking the full potential of entanglement caused quantum enhancement in
experiments
Artificial Intelligence and Machine Learning for Quantum Technologies
In recent years, the dramatic progress in machine learning has begun to impact many areas of science and technology significantly. In the present perspective article, we explore how quantum technologies are benefiting from this revolution. We showcase in illustrative examples how scientists in the past few years have started to use machine learning and more broadly methods of artificial intelligence to analyze quantum measurements, estimate the parameters of quantum devices, discover new quantum experimental setups, protocols, and feedback strategies, and generally improve aspects of quantum computing, quantum communication, and quantum simulation. We highlight open challenges and future possibilities and conclude with some speculative visions for the next decade
Modularized and Scalable Compilation for Double Quantum Dot Quatum Computing
Any quantum program on a realistic quantum device must be compiled into an
executable form while taking into account the underlying hardware constraints.
Stringent restrictions on architecture and control imposed by physical
platforms make this very challenging. In this paper, based on the quantum
variational algorithm, we propose a novel scheme to train an Ansatz circuit and
realize high-fidelity compilation of a set of universal quantum gates for
singlet-triplet qubits in semiconductor double quantum dots, a fairly heavily
constrained system. Furthermore, we propose a scalable architecture for a
modular implementation of quantum programs in this constrained systems and
validate its performance with two representative demonstrations, Grover's
algorithm for the database searching (static compilation) and a variant of
variational quantum eigensolver for the Max-Cut optimization (dynamic
compilation). Our methods are potentially applicable to a wide range of
physical devices. This work constitutes an important stepping-stone for
exploiting the potential for advanced and complicated quantum algorithms on
near-term devices.Comment: 10 pages, 4 figure
Hybrid quantum-classical unsupervised data clustering based on the Self-Organizing Feature Map
Unsupervised machine learning is one of the main techniques employed in
artificial intelligence. Quantum computers offer opportunities to speed up such
machine learning techniques. Here, we introduce an algorithm for quantum
assisted unsupervised data clustering using the self-organizing feature map, a
type of artificial neural network. We make a proof-of-concept realization of
one of the central components on the IBM Q Experience and show that it allows
us to reduce the number of calculations in a number of clusters. We compare the
results with the classical algorithm on a toy example of unsupervised text
clustering
Optimized Compilation of Aggregated Instructions for Realistic Quantum Computers
Recent developments in engineering and algorithms have made real-world
applications in quantum computing possible in the near future. Existing quantum
programming languages and compilers use a quantum assembly language composed of
1- and 2-qubit (quantum bit) gates. Quantum compiler frameworks translate this
quantum assembly to electric signals (called control pulses) that implement the
specified computation on specific physical devices. However, there is a
mismatch between the operations defined by the 1- and 2-qubit logical ISA and
their underlying physical implementation, so the current practice of directly
translating logical instructions into control pulses results in inefficient,
high-latency programs. To address this inefficiency, we propose a universal
quantum compilation methodology that aggregates multiple logical operations
into larger units that manipulate up to 10 qubits at a time. Our methodology
then optimizes these aggregates by (1) finding commutative intermediate
operations that result in more efficient schedules and (2) creating custom
control pulses optimized for the aggregate (instead of individual 1- and
2-qubit operations). Compared to the standard gate-based compilation, the
proposed approach realizes a deeper vertical integration of high-level quantum
software and low-level, physical quantum hardware. We evaluate our approach on
important near-term quantum applications on simulations of superconducting
quantum architectures. Our proposed approach provides a mean speedup of
, with a maximum of . Because latency directly affects the
feasibility of quantum computation, our results not only improve performance
but also have the potential to enable quantum computation sooner than otherwise
possible.Comment: 13 pages, to apper in ASPLO
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