225,870 research outputs found
Algorithm for low power XOR gate decomposition
Материалы XVII Междунар. науч.-техн. конф. студентов, аспирантов и молодых ученых, Гомель, 27–28 апр. 2017 г
Equivalent relaxations of optimal power flow
Several convex relaxations of the optimal power flow (OPF) problem have
recently been developed using both bus injection models and branch flow models.
In this paper, we prove relations among three convex relaxations: a
semidefinite relaxation that computes a full matrix, a chordal relaxation based
on a chordal extension of the network graph, and a second-order cone relaxation
that computes the smallest partial matrix. We prove a bijection between the
feasible sets of the OPF in the bus injection model and the branch flow model,
establishing the equivalence of these two models and their second-order cone
relaxations. Our results imply that, for radial networks, all these relaxations
are equivalent and one should always solve the second-order cone relaxation.
For mesh networks, the semidefinite relaxation is tighter than the second-order
cone relaxation but requires a heavier computational effort, and the chordal
relaxation strikes a good balance. Simulations are used to illustrate these
results.Comment: 12 pages, 7 figure
Convex Relaxation of Optimal Power Flow, Part I: Formulations and Equivalence
This tutorial summarizes recent advances in the convex relaxation of the
optimal power flow (OPF) problem, focusing on structural properties rather than
algorithms. Part I presents two power flow models, formulates OPF and their
relaxations in each model, and proves equivalence relations among them. Part II
presents sufficient conditions under which the convex relaxations are exact.Comment: Citation: IEEE Transactions on Control of Network Systems,
15(1):15-27, March 2014. This is an extended version with Appendices VIII and
IX that provide some mathematical preliminaries and proofs of the main
result
Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics
Quantum computing is powerful because unitary operators describing the
time-evolution of a quantum system have exponential size in terms of the number
of qubits present in the system. We develop a new "Singular value
transformation" algorithm capable of harnessing this exponential advantage,
that can apply polynomial transformations to the singular values of a block of
a unitary, generalizing the optimal Hamiltonian simulation results of Low and
Chuang. The proposed quantum circuits have a very simple structure, often give
rise to optimal algorithms and have appealing constant factors, while usually
only use a constant number of ancilla qubits. We show that singular value
transformation leads to novel algorithms. We give an efficient solution to a
certain "non-commutative" measurement problem and propose a new method for
singular value estimation. We also show how to exponentially improve the
complexity of implementing fractional queries to unitaries with a gapped
spectrum. Finally, as a quantum machine learning application we show how to
efficiently implement principal component regression. "Singular value
transformation" is conceptually simple and efficient, and leads to a unified
framework of quantum algorithms incorporating a variety of quantum speed-ups.
We illustrate this by showing how it generalizes a number of prominent quantum
algorithms, including: optimal Hamiltonian simulation, implementing the
Moore-Penrose pseudoinverse with exponential precision, fixed-point amplitude
amplification, robust oblivious amplitude amplification, fast QMA
amplification, fast quantum OR lemma, certain quantum walk results and several
quantum machine learning algorithms. In order to exploit the strengths of the
presented method it is useful to know its limitations too, therefore we also
prove a lower bound on the efficiency of singular value transformation, which
often gives optimal bounds.Comment: 67 pages, 1 figur
Low-cost error mitigation by symmetry verification
We investigate the performance of error mitigation via measurement of
conserved symmetries on near-term devices. We present two protocols to measure
conserved symmetries during the bulk of an experiment, and develop a zero-cost
post-processing protocol which is equivalent to a variant of the quantum
subspace expansion. We develop methods for inserting global and local symetries
into quantum algorithms, and for adjusting natural symmetries of the problem to
boost their mitigation against different error channels. We demonstrate these
techniques on two- and four-qubit simulations of the hydrogen molecule (using a
classical density-matrix simulator), finding up to an order of magnitude
reduction of the error in obtaining the ground state dissociation curve.Comment: Published versio
Optimal synthesis of general multi-qutrit quantum computation
Quantum circuits of a general quantum gate acting on multiple -level
quantum systems play a prominent role in multi-valued quantum computation. We
first propose a new recursive Cartan decomposition of semi-simple unitary Lie
group (arbitrary -qutrit gate). Note that the decomposition
completely decomposes an n-qutrit gate into local and non-local operations. We
design an explicit quantum circuit for implementing arbitrary two-qutrit gates,
and the cost of our construction is 21 generalized controlled X (GCX) and
controlled increment (CINC) gates less than the earlier best result of 26 GGXs.
Moreover, we extend the program to the -qutrit system, and the quantum
circuit of generic -qutrit gates contained
GGXs and CINCs is presented. Such asymptotically optimal structure is the best
known result so far.Comment: 16 pages, 14 figure
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