Optimal Parallel Quantum Query Algorithms

We study the complexity of quantum query algorithms that make p queries in parallel in each timestep. This model is in part motivated by the fact that decoherence times of qubits are typically small, so it makes sense to parallelize quantum algorithms as much as possible. We show tight bounds for a...

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Vydané v:Algorithmica Ročník 79; číslo 2; s. 509 - 529
Hlavní autori: Jeffery, Stacey, Magniez, Frederic, de Wolf, Ronald
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.10.2017
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Abstract We study the complexity of quantum query algorithms that make p queries in parallel in each timestep. This model is in part motivated by the fact that decoherence times of qubits are typically small, so it makes sense to parallelize quantum algorithms as much as possible. We show tight bounds for a number of problems, specifically Θ ( ( n / p ) 2 / 3 ) p -parallel queries for element distinctness and Θ ( ( n / p ) k / ( k + 1 ) ) for k -sum. Our upper bounds are obtained by parallelized quantum walk algorithms, and our lower bounds are based on a relatively small modification of the adversary lower bound method, combined with recent results of Belovs et al. on learning graphs. We also prove some general bounds, in particular that quantum and classical p -parallel query complexity are polynomially related for all total functions  f when p is small compared to f ’s block sensitivity.
AbstractList We study the complexity of quantum query algorithms that make p queries in parallel in each timestep. This model is in part motivated by the fact that decoherence times of qubits are typically small, so it makes sense to parallelize quantum algorithms as much as possible. We show tight bounds for a number of problems, specifically Θ ( ( n / p ) 2 / 3 ) p -parallel queries for element distinctness and Θ ( ( n / p ) k / ( k + 1 ) ) for k -sum. Our upper bounds are obtained by parallelized quantum walk algorithms, and our lower bounds are based on a relatively small modification of the adversary lower bound method, combined with recent results of Belovs et al. on learning graphs. We also prove some general bounds, in particular that quantum and classical p -parallel query complexity are polynomially related for all total functions  f when p is small compared to f ’s block sensitivity.
We define the Streaming Communication model that combines the main aspects of communication complexity and streaming. Input arrives as a stream, spread between several agents across a network. Each agent has a bounded memory, which can be updated upon receiving a new bit, or a message from another agent. We provide tight tradeoffs between the necessary resources, i.e., communication between agents and memory, for some of the canonical problems from communication complexity by proving a strong general lower bound technique. Second, we analyze the Approximate Matching problem and show that the complexity of this problem (i.e., the achievable approximation ratio) in the one-way variant of our model is strictly different both from the streaming complexity and the one-way communication complexity thereof.
We study the complexity of quantum query algorithms that make p queries in parallel in each timestep. This model is in part motivated by the fact that decoherence times of qubits are typically small, so it makes sense to parallelize quantum algorithms as much as possible. We show tight bounds for a number of problems, specifically Θ ( ( n / p ) 2 / 3 ) p-parallel queries for element distinctness and Θ ( ( n / p ) k / ( k + 1 ) ) for k -sum. Our upper bounds are obtained by parallelized quantum walk algorithms, and our lower bounds are based on a relatively small modification of the adversary lower bound method, combined with recent results of Belovs et al. on learning graphs. We also prove some general bounds, in particular that quantum and classical p-parallel query complexity are polynomially related for all total functions f when p is small compared to f’s block sensitivity.
Author Jeffery, Stacey
de Wolf, Ronald
Magniez, Frederic
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  givenname: Ronald
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  fullname: de Wolf, Ronald
  organization: QuSoft, CWI and University of Amsterdam
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crossref_primary_10_1007_s11128_018_1930_x
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Issue 2
Keywords Quantum algorithms
Lower bounds
Query complexity
Parallel algorithms
Language English
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References_xml – reference: AmbainisAQuantum lower bounds by quantum argumentsJ. Comput. Syst. Sci.2002644750767191230110.1006/jcss.2002.18261015.68075
– reference: van Dam, W.: Quantum oracle interrogation: getting all information for almost half the price. In: Proceedings of 39th IEEE FOCS, pp. 362–367 (1998)
– reference: GreenFHomerSMooreCPollettCCounting, fanout and the complexity of quantum ACCQuantum Inf. Comput.200221356519075201187.81065
– reference: GroverLRudolphTHow significant are the known collision and element distinctness quantum algorithms?Quantum Inf. Comput.20044320120620904551175.81056
– reference: Belovs, A., Lee, T.: Quantum algorithm for k-distinctness with prior knowledge on the input. Technical Report (2011). arXiv:1108.3022
– reference: BuhrmanHde WolfRComplexity measures and decision tree complexity: a surveyTheor. Comput. Sci.200228812143193488810.1016/S0304-3975(01)00144-X1061.68058
– reference: DeutschDJozsaRRapid solution of problems by quantum computationProc. R. Soc. Lond.1992A439553558119643310.1098/rspa.1992.01670792.68058
– reference: Aaronson, S., Ambainis, A.: Forrelation: a problem that optimally separates quantum from classical computing. In: Proceedings of 47th ACM STOC, pp. 307–316 (2015). arXiv:1411.5729
– reference: Aaronson, S., Ben-David, S., Kothari, R.: Separations in query complexity using cheat sheets. In: Proceedings of 48th STOC, pp. 863–876 (2016)
– reference: HøyerPŠpalekRQuantum fan-out is powerfulTheory Comput.20051181103232251510.4086/toc.2005.v001a0051213.68298
– reference: NisanNCREW PRAMs and decision treesSIAM J. Comput.19912069991007113574410.1137/02200620737.68028
– reference: Høyer, P., Lee, T., Špalek, R.: Negative weights make adversaries stronger. In: Proceedings of 39th ACM STOC, pp. 526–535 (2007)
– reference: Cleve, R., Watrous, J.: Fast parallel circuits for the quantum Fourier transform. In: Proceedings of 41st IEEE FOCS, pp. 526–536 (2000)
– reference: MontanaroANonadaptive quantum query complexityInf. Process. Lett.20101102411101113277824410.1016/j.ipl.2010.09.009
– reference: Belovs, A.: Adversary lower bound for element distinctness (2012). arXiv:1204.5074
– reference: MooreCNilssonMParallel quantum computation and quantum codesSIAM J. Comput.2002313799815189645910.1137/S00975397993550531161.81326
– reference: Belovs, A.: Learning-graph-based quantum algorithm for k-distinctness. In: Proceedings of 53rd IEEE FOCS, pp. 207–216 (2012)
– reference: JozsaRAngelakisDGChristandlMEkertAAn introduction to measurement based quantum computationQuantum Information Processing2006AmsterdamIOS Press137158
– reference: NisanNSzegedyMOn the degree of Boolean functions as real polynomialsComput. Complex.199444301313131353110.1007/BF012634190829.68047
– reference: ShorPWPolynomial-time algorithms for prime factorization and discrete logarithms on a quantum computerSIAM J. Comput.199726514841509147199010.1137/S00975397952931721005.11065
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Snippet We study the complexity of quantum query algorithms that make p queries in parallel in each timestep. This model is in part motivated by the fact that...
We study the complexity of quantum query algorithms that make p queries in parallel in each timestep. This model is in part motivated by the fact that...
We define the Streaming Communication model that combines the main aspects of communication complexity and streaming. Input arrives as a stream, spread between...
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SubjectTerms Algorithm Analysis and Problem Complexity
Algorithms
Complexity
Computer Science
Computer Systems Organization and Communication Networks
Data Structures and Information Theory
Functions (mathematics)
Lower bounds
Mathematical analysis
Mathematics of Computing
Parallel processing
Qubits (quantum computing)
Queries
Theory of Computation
Upper bounds
Title Optimal Parallel Quantum Query Algorithms
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