875 research outputs found

    Quantum communication complexity of linear regression

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    Dequantized algorithms show that quantum computers do not have exponential speedups for many linear algebra problems in terms of time and query complexity. In this work, we show that quantum computers can have exponential speedups in terms of communication complexity for some fundamental linear algebra problems. We mainly focus on solving linear regression and Hamiltonian simulation. In the quantum case, the task is to prepare the quantum state of the result. To allow for a fair comparison, in the classical case the task is to sample from the result. We investigate these two problems in two-party and multiparty models, propose near-optimal quantum protocols and prove quantum/classical lower bounds. In this process, we propose an efficient quantum protocol for quantum singular value transformation, which is a powerful technique for designing quantum algorithms. As a result, for many linear algebra problems where quantum computers lose exponential speedups in terms of time and query complexity, it is possible to have exponential speedups in terms of communication complexity.Comment: 28 page

    The Structure of Promises in Quantum Speedups

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    It has long been known that in the usual black-box model, one cannot get super-polynomial quantum speedups without some promise on the inputs. In this paper, we examine certain types of symmetric promises, and show that they also cannot give rise to super-polynomial quantum speedups. We conclude that exponential quantum speedups only occur given "structured" promises on the input. Specifically, we show that there is a polynomial relationship of degree 1212 between D(f)D(f) and Q(f)Q(f) for any function ff defined on permutations (elements of {0,1,…,M−1}n\{0,1,\dots, M-1\}^n in which each alphabet element occurs exactly once). We generalize this result to all functions ff defined on orbits of the symmetric group action SnS_n (which acts on an element of {0,1,…,M−1}n\{0,1,\dots, M-1\}^n by permuting its entries). We also show that when MM is constant, any function ff defined on a "symmetric set" - one invariant under SnS_n - satisfies R(f)=O(Q(f)12(M−1))R(f)=O(Q(f)^{12(M-1)}).Comment: 15 page

    The need for structure in quantum speedups

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    Is there a general theorem that tells us when we can hope for exponential speedups from quantum algorithms, and when we cannot? In this paper, we make two advances toward such a theorem, in the black-box model where most quantum algorithms operate. First, we show that for any problem that is invariant under permuting inputs and outputs (like the collision or the element distinctness problems), the quantum query complexity is at least the 9th root of the classical randomized query complexity. This resolves a conjecture of Watrous from 2002. Second, inspired by recent work of O'Donnell et al. and Dinur et al., we conjecture that every bounded low-degree polynomial has a "highly influential" variable. Assuming this conjecture, we show that every T-query quantum algorithm can be simulated on most inputs by a poly(T)-query classical algorithm, and that one essentially cannot hope to prove P!=BQP relative to a random oracle

    Quantum machine learning: a classical perspective

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    Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets are motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed-up classical machine learning algorithms. Here we review the literature in quantum machine learning and discuss perspectives for a mixed readership of classical machine learning and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in machine learning are identified as promising directions for the field. Practical questions, like how to upload classical data into quantum form, will also be addressed.Comment: v3 33 pages; typos corrected and references adde
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