Mind the $\tilde{\mathcal{O}}$: Asymptotically Better, but Still
Impractical, Quantum Distributed Algorithms
- URL: http://arxiv.org/abs/2304.02825v6
- Date: Tue, 1 Aug 2023 14:29:48 GMT
- Title: Mind the $\tilde{\mathcal{O}}$: Asymptotically Better, but Still
Impractical, Quantum Distributed Algorithms
- Authors: Phillip A. Kerger, David E. Bernal Neira, Zoe Gonzalez Izquierdo,
Eleanor G. Rieffel
- Abstract summary: We present two algorithms in the Quantum CONGEST-CLIQUE model of distributed computation that succeed with high probability.
The algorithms achieve a lower round and message complexity than any known algorithms in the classical CONGEST-CLIQUE model.
An existing framework for using distributed version of Grover's search algorithm to accelerate triangle finding lies at the core of the speedup.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The CONGEST and CONGEST-CLIQUE models have been carefully studied to
represent situations where the communication bandwidth between processors in a
network is severely limited. Messages of only $O(log(n))$ bits of information
each may be sent between processors in each round. The quantum versions of
these models allow the processors instead to communicate and compute with
quantum bits under the same bandwidth limitations. This leads to the following
natural research question: What problems can be solved more efficiently in
these quantum models than in the classical ones? Building on existing work, we
contribute to this question in two ways. Firstly, we present two algorithms in
the Quantum CONGEST-CLIQUE model of distributed computation that succeed with
high probability; one for producing an approximately optimal Steiner Tree, and
one for producing an exact directed minimum spanning tree, each of which uses
$\tilde{O}(n^{1/4})$ rounds of communication and $\tilde{O}(n^{9/4})$ messages,
where $n$ is the number of nodes in the network. The algorithms thus achieve a
lower asymptotic round and message complexity than any known algorithms in the
classical CONGEST-CLIQUE model. At a high level, we achieve these results by
combining classical algorithmic frameworks with quantum subroutines. An
existing framework for using distributed version of Grover's search algorithm
to accelerate triangle finding lies at the core of the asymptotic speedup.
Secondly, we carefully characterize the constants and logarithmic factors
involved in our algorithms as well as related algorithms, otherwise commonly
obscured by $\tilde{O}$ notation. The analysis shows that some improvements are
needed to render both our and existing related quantum and classical algorithms
practical, as their asymptotic speedups only help for very large values of $n$.
Related papers
- Sum-of-Squares inspired Quantum Metaheuristic for Polynomial Optimization with the Hadamard Test and Approximate Amplitude Constraints [76.53316706600717]
Recently proposed quantum algorithm arXiv:2206.14999 is based on semidefinite programming (SDP)
We generalize the SDP-inspired quantum algorithm to sum-of-squares.
Our results show that our algorithm is suitable for large problems and approximate the best known classicals.
arXiv Detail & Related papers (2024-08-14T19:04:13Z) - Quantum Complexity for Discrete Logarithms and Related Problems [7.077306859247421]
We establish a generic model of quantum computation for group-theoretic problems, which we call the quantum generic group model.
We show the quantum complexity lower bounds and almost matching algorithms of the DL and related problems in this model.
variations of Shor's algorithm can take advantage of classical computations to reduce the number of quantum group operations.
arXiv Detail & Related papers (2023-07-06T15:32:50Z) - Quantum Simulation of the First-Quantized Pauli-Fierz Hamiltonian [0.5097809301149342]
We show that a na"ive partitioning and low-order splitting formula can yield, through our divide and conquer formalism, superior scaling to qubitization for large $Lambda$.
We also give new algorithmic and circuit level techniques for gate optimization including a new way of implementing a group of multi-controlled-X gates.
arXiv Detail & Related papers (2023-06-19T23:20:30Z) - Efficient quantum linear solver algorithm with detailed running costs [0.0]
We introduce a quantum linear solver algorithm combining ideasdiabatic quantum computing with filtering techniques based on quantum signal processing.
Our protocol reduces the cost of quantum linear solvers over state-of-the-art close to an order of magnitude for early implementations.
arXiv Detail & Related papers (2023-05-19T00:07:32Z) - Quantum Algorithms for Sampling Log-Concave Distributions and Estimating
Normalizing Constants [8.453228628258778]
We develop quantum algorithms for sampling logconcave distributions and for estimating their normalizing constants.
We exploit quantum analogs of the Monte Carlo method and quantum walks.
We also prove a $1/epsilon1-o(1)$ quantum lower bound for estimating normalizing constants.
arXiv Detail & Related papers (2022-10-12T19:10:43Z) - A single $T$-gate makes distribution learning hard [56.045224655472865]
This work provides an extensive characterization of the learnability of the output distributions of local quantum circuits.
We show that for a wide variety of the most practically relevant learning algorithms -- including hybrid-quantum classical algorithms -- even the generative modelling problem associated with depth $d=omega(log(n))$ Clifford circuits is hard.
arXiv Detail & Related papers (2022-07-07T08:04:15Z) - Quantum Goemans-Williamson Algorithm with the Hadamard Test and
Approximate Amplitude Constraints [62.72309460291971]
We introduce a variational quantum algorithm for Goemans-Williamson algorithm that uses only $n+1$ qubits.
Efficient optimization is achieved by encoding the objective matrix as a properly parameterized unitary conditioned on an auxilary qubit.
We demonstrate the effectiveness of our protocol by devising an efficient quantum implementation of the Goemans-Williamson algorithm for various NP-hard problems.
arXiv Detail & Related papers (2022-06-30T03:15:23Z) - Quantum Resources Required to Block-Encode a Matrix of Classical Data [56.508135743727934]
We provide circuit-level implementations and resource estimates for several methods of block-encoding a dense $Ntimes N$ matrix of classical data to precision $epsilon$.
We examine resource tradeoffs between the different approaches and explore implementations of two separate models of quantum random access memory (QRAM)
Our results go beyond simple query complexity and provide a clear picture into the resource costs when large amounts of classical data are assumed to be accessible to quantum algorithms.
arXiv Detail & Related papers (2022-06-07T18:00:01Z) - Entanglement and coherence in Bernstein-Vazirani algorithm [58.720142291102135]
Bernstein-Vazirani algorithm allows one to determine a bit string encoded into an oracle.
We analyze in detail the quantum resources in the Bernstein-Vazirani algorithm.
We show that in the absence of entanglement, the performance of the algorithm is directly related to the amount of quantum coherence in the initial state.
arXiv Detail & Related papers (2022-05-26T20:32:36Z) - Time and Query Optimal Quantum Algorithms Based on Decision Trees [2.492300648514128]
We show that a quantum algorithm can be implemented in time $tilde O(sqrtGT)$.
Our algorithm is based on non-binary span programs and their efficient implementation.
arXiv Detail & Related papers (2021-05-18T06:51:11Z) - Learning to Accelerate Heuristic Searching for Large-Scale Maximum
Weighted b-Matching Problems in Online Advertising [51.97494906131859]
Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc.
Existing exact and approximate algorithms usually fail in such settings due to either requiring intolerable running time or too much computation resource.
We propose textttNeuSearcher which leverages the knowledge learned from previously instances to solve new problem instances.
arXiv Detail & Related papers (2020-05-09T02:48:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.