267 research outputs found
Approximate Approximation on a Quantum Annealer
Many problems of industrial interest are NP-complete, and quickly exhaust
resources of computational devices with increasing input sizes. Quantum
annealers (QA) are physical devices that aim at this class of problems by
exploiting quantum mechanical properties of nature. However, they compete with
efficient heuristics and probabilistic or randomised algorithms on classical
machines that allow for finding approximate solutions to large NP-complete
problems. While first implementations of QA have become commercially available,
their practical benefits are far from fully explored. To the best of our
knowledge, approximation techniques have not yet received substantial
attention. In this paper, we explore how problems' approximate versions of
varying degree can be systematically constructed for quantum annealer programs,
and how this influences result quality or the handling of larger problem
instances on given set of qubits. We illustrate various approximation
techniques on both, simulations and real QA hardware, on different seminal
problems, and interpret the results to contribute towards a better
understanding of the real-world power and limitations of current-state and
future quantum computing.Comment: Proceedings of the 17th ACM International Conference on Computing
Frontiers (CF 2020
Time-Sliced Quantum Circuit Partitioning for Modular Architectures
Current quantum computer designs will not scale. To scale beyond small
prototypes, quantum architectures will likely adopt a modular approach with
clusters of tightly connected quantum bits and sparser connections between
clusters. We exploit this clustering and the statically-known control flow of
quantum programs to create tractable partitioning heuristics which map quantum
circuits to modular physical machines one time slice at a time. Specifically,
we create optimized mappings for each time slice, accounting for the cost to
move data from the previous time slice and using a tunable lookahead scheme to
reduce the cost to move to future time slices. We compare our approach to a
traditional statically-mapped, owner-computes model. Our results show strict
improvement over the static mapping baseline. We reduce the non-local
communication overhead by 89.8\% in the best case and by 60.9\% on average. Our
techniques, unlike many exact solver methods, are computationally tractable.Comment: Appears in CF'20: ACM International Conference on Computing Frontier
Trainable Variational Quantum-Multiblock ADMM Algorithm for Generation Scheduling
The advent of quantum computing can potentially revolutionize how complex
problems are solved. This paper proposes a two-loop quantum-classical solution
algorithm for generation scheduling by infusing quantum computing, machine
learning, and distributed optimization. The aim is to facilitate employing
noisy near-term quantum machines with a limited number of qubits to solve
practical power system optimization problems such as generation scheduling. The
outer loop is a 3-block quantum alternative direction method of multipliers
(QADMM) algorithm that decomposes the generation scheduling problem into three
subproblems, including one quadratically unconstrained binary optimization
(QUBO) and two non-QUBOs. The inner loop is a trainable quantum approximate
optimization algorithm (T-QAOA) for solving QUBO on a quantum computer. The
proposed T-QAOA translates interactions of quantum-classical machines as
sequential information and uses a recurrent neural network to estimate
variational parameters of the quantum circuit with a proper sampling technique.
T-QAOA determines the QUBO solution in a few quantum-learner iterations instead
of hundreds of iterations needed for a quantum-classical solver. The outer
3-block ADMM coordinates QUBO and non-QUBO solutions to obtain the solution to
the original problem. The conditions under which the proposed QADMM is
guaranteed to converge are discussed. Two mathematical and three generation
scheduling cases are studied. Analyses performed on quantum simulators and
classical computers show the effectiveness of the proposed algorithm. The
advantages of T-QAOA are discussed and numerically compared with QAOA which
uses a stochastic gradient descent-based optimizer.Comment: 11 page
Quantum system characterization with limited resources
The construction and operation of large scale quantum information devices
presents a grand challenge. A major issue is the effective control of coherent
evolution, which requires accurate knowledge of the system dynamics that may
vary from device to device. We review strategies for obtaining such knowledge
from minimal initial resources and in an efficient manner, and apply these to
the problem of characterization of a qubit embedded into a larger state
manifold, made tractable by exploiting prior structural knowledge. We also
investigate adaptive sampling for estimation of multiple parameters
Inferring Quantum Network Topology using Local Measurements
Statistical correlations that can be generated across the nodes in a quantum
network depend crucially on its topology. However, this topological information
might not be known a priori, or it may need to be verified. In this paper, we
propose an efficient protocol for distinguishing and inferring the topology of
a quantum network. We leverage entropic quantities -- namely, the von Neumann
entropy and the measured mutual information -- as well as measurement
covariance to uniquely characterize the topology. We show that the entropic
quantities are sufficient to distinguish two networks that prepare GHZ states.
Moreover, if qubit measurements are available, both entropic quantities and
covariance can be used to infer the network topology. We show that the protocol
can be entirely robust to noise and can be implemented via quantum variational
optimization. Numerical experiments on both classical simulators and quantum
hardware show that covariance is generally more reliable for accurately and
efficiently inferring the topology, whereas entropy-based methods are often
better at identifying the absence of entanglement in the low-shot regime
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