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András Gilyén

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

11 papers
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11

FOCS Conference 2025 Conference Paper

A Distillation-Teleportation Protocol for Fault-Tolerant QRAM

  • Alexander M. Dalzell
  • András Gilyén
  • Connor T. Hann
  • Sam McArdle
  • Grant Salton
  • Quynh T. Nguyen
  • Aleksander Kubica
  • Fernando G. S. L. Brandão

We present a protocol for fault-tolerantly implementing the logical quantum random access memory (QRAM) operation, given access to a specialized, noisy QRAM device. For coherently accessing classical memories of size $2^{n}$, our protocol consumes only poly $(n)$ fault-tolerant quantum resources (logical gates, logical qubits, quantum error correction cycles, etc.), avoiding the need to perform active error correction on all $\Omega\left(2^{n}\right)$ components of the QRAM device. This is the first rigorous conceptual demonstration that a specialized, noisy QRAM device could be useful for implementing a fault-tolerant quantum algorithm. In fact, the fidelity of the device can be as low as $1 / \operatorname{poly}(n)$. The protocol queries the noisy QRAM device $\operatorname{poly}(n)$ times to prepare a sequence of n-qubit QRAM resource states, which are moved to a general-purpose poly $(n)$ size processor to be encoded into a QEC code, distilled, and faulttolerantly teleported into the computation. To aid this protocol, we develop a new gate-efficient streaming version of quantum purity amplification that matches the optimal sample complexity in a wide range of parameters and is therefore of independent interest. The exponential reduction in fault-tolerant quantum resources comes at the expense of an exponential quantity of purely classical complexity-each of the n iterations of the protocol requires adaptively updating the $2^{n}$-size classical dataset and providing the noisy QRAM device with access to the updated dataset at the next iteration. We show that this classical operation can be parallelized to poly $(n)$ classical circuit depth, but only in a model where classical sparse matrix-vector multiplication for $2^{n}$-dimensional vectors can be as well. While our protocol demonstrates that QRAM is more compatible with fault-tolerant quantum computation than previously thought, the need for significant classical computational complexity exposes potentially fundamental limitations to realizing a truly poly $(n)$-cost faulttolerant QRAM.

SODA Conference 2023 Conference Paper

Quantum tomography using state-preparation unitaries

  • Joran van Apeldoorn
  • Arjan Cornelissen
  • András Gilyén
  • Giacomo Nannicini

We describe algorithms to obtain an approximate classical description of a d -dimensional quantum state when given access to a unitary (and its inverse) that prepares it. For pure states we characterize the query complexity for ℓ q -norm error up to logarithmic factors. As a special case, we show that it takes applications of the unitaries to obtain an ε-ℓ 2 -approximation of the state. For mixed states we consider a similar model, where the unitary prepares a purification of the state. We characterize the query complexity for obtaining Schatten q -norm estimates of a rank- r mixed state, up to polylogarithmic factors. In particular, we show that a trace-norm ( q = 1) estimate can be obtained with queries. This improves (assuming our stronger input model) the ε-dependence over the works of O'Donnell and Wright ( STOC 2016) and Haah et al. ( IEEE Trans. Inf. Theory, 63. 9, 2017 ), that use a joint measurement on copies of the state. To our knowledge, the most sample-efficient results for pure-state tomography come from setting the rank to 1 in generic mixed-state tomography algorithms, which can require a large amount of computing resources. We describe sample-optimal algorithms for pure states that are simple and fast to implement. Along the way we show that an ℓ ∞ -norm estimate of a normalized vector induces a (slightly worse) ℓ q -norm estimate for that vector, without losing a dimension-dependent factor in the precision. We also develop an unbiased and symmetric version of phase estimation, where the probability distribution of the estimate is centered around the true value. Finally, we give an efficient method for estimating multiple expectation values, improving over the recent result by Huggins et al. ( arXiv: 2111. 09283 ) when the measurement operators do not fully overlap. More specifically, we show that for E 1, …, E m normalized measurement operators, all expectation values Tr( E j ρ ) can be efficiently learned up to error ε with applications of a state-preparation unitary for a purification of ρ. * The full version of the paper can be accessed at https: //arxiv. org/abs/2207. 08800

STOC Conference 2021 Conference Paper

(Sub)Exponential advantage of adiabatic Quantum computation with no sign problem

  • András Gilyén
  • Matthew B. Hastings
  • Umesh V. Vazirani

We demonstrate the possibility of (sub)exponential quantum speedup via a quantum algorithm that follows an adiabatic path of a gapped Hamiltonian with no sign problem. The Hamiltonian that exhibits this speed-up comes from the adjacency matrix of an undirected graph whose vertices are labeled by n -bit strings, and we can view the adiabatic evolution as an efficient O ( poly ( n ))-time quantum algorithm for finding a specific “EXIT” vertex in the graph given the “ENTRANCE” vertex. On the other hand we show that if the graph is given via an adjacency-list oracle, there is no classical algorithm that finds the “EXIT” with probability greater than exp(− n δ ) using at most exp( n δ ) queries for δ= 1/5 − o (1). Our construction of the graph is somewhat similar to the “welded-trees” construction of Childs et al., but uses additional ideas of Hastings for achieving a spectral gap and a short adiabatic path.

STOC Conference 2020 Conference Paper

Quadratic speedup for finding marked vertices by quantum walks

  • Andris Ambainis
  • András Gilyén
  • Stacey Jeffery
  • Martins Kokainis

A quantum walk algorithm can detect the presence of a marked vertex on a graph quadratically faster than the corresponding random walk algorithm (Szegedy, FOCS 2004). However, quantum algorithms that actually find a marked element quadratically faster than a classical random walk were only known for the special case when the marked set consists of just a single vertex, or in the case of some specific graphs. We present a new quantum algorithm for finding a marked vertex in any graph, with any set of marked vertices, that is (up to a log factor) quadratically faster than the corresponding classical random walk, resolving a question that had been open for 15 years.

STOC Conference 2020 Conference Paper

Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning

  • Nai-Hui Chia
  • András Gilyén
  • Tongyang Li
  • Han-Hsuan Lin
  • Ewin Tang
  • Chunhao Wang

We present an algorithmic framework for quantum-inspired classical algorithms on close-to-low-rank matrices, generalizing the series of results started by Tang’s breakthrough quantum-inspired algorithm for recommendation systems [STOC’19]. Motivated by quantum linear algebra algorithms and the quantum singular value transformation (SVT) framework of Gilyén et al. [STOC’19], we develop classical algorithms for SVT that run in time independent of input dimension, under suitable quantum-inspired sampling assumptions. Our results give compelling evidence that in the corresponding QRAM data structure input model, quantum SVT does not yield exponential quantum speedups. Since the quantum SVT framework generalizes essentially all known techniques for quantum linear algebra, our results, combined with sampling lemmas from previous work, suffices to generalize all recent results about dequantizing quantum machine learning algorithms. In particular, our classical SVT framework recovers and often improves the dequantization results on recommendation systems, principal component analysis, supervised clustering, support vector machines, low-rank regression, and semidefinite program solving. We also give additional dequantization results on low-rank Hamiltonian simulation and discriminant analysis. Our improvements come from identifying the key feature of the quantum-inspired input model that is at the core of all prior quantum-inspired results: ℓ 2 -norm sampling can approximate matrix products in time independent of their dimension. We reduce all our main results to this fact, making our exposition concise, self-contained, and intuitive.

FOCS Conference 2020 Conference Paper

Symmetries, Graph Properties, and Quantum Speedups

  • Shalev Ben-David
  • Andrew M. Childs
  • András Gilyén
  • William Kretschmer
  • Supartha Podder
  • Daochen Wang

Aaronson and Ambainis (2009) and Chailloux (2018) showed that fully symmetric (partial) functions do not admit exponential quantum query speedups. This raises a natural question: how symmetric must a function be before it cannot exhibit a large quantum speedup? In this work, we prove that hypergraph symmetries in the adjacency matrix model allow at most a polynomial separation between randomized and quantum query complexities. We also show that, remarkably, permutation groups constructed out of these symmetries are essentially the only permutation groups that prevent super-polynomial quantum speedups. We prove this by fully characterizing the primitive permutation groups that allow super-polynomial quantum speedups. In contrast, in the adjacency list model for bounded-degree graphs-where graph symmetry is manifested differently-we exhibit a property testing problem that shows an exponential quantum speedup. These results resolve open questions posed by Ambainis, Childs, and Liu (2010) and Montanaro and de Wolf (2013).

SODA Conference 2019 Conference Paper

Optimizing quantum optimization algorithms via faster quantum gradient computation

  • András Gilyén
  • Srinivasan Arunachalam
  • Nathan Wiebe

We consider a generic framework of optimization algorithms based on gradient descent. We develop a quantum algorithm that computes the gradient of a multi-variate realvalued function f: ℝ d → ℝ by evaluating it at only a logarithmic number of times in superposition. Our algorithm is an improved version of Jordan's gradient computation algorithm [28], providing an approximation of the gradient ▽ f with quadratically better dependence on the evaluation accuracy of f, for an important class of smooth functions. Furthermore, we show that objective functions arising from variational quantum circuits usually satisfy the necessary smoothness conditions, hence our algorithm provides a quadratic improvement in the complexity of computing their gradient. We also show that in a continuous phase-query model, our gradient computation algorithm has optimal query complexity up to poly-logarithmic factors, for a particular class of smooth functions. Moreover, we show that for low-degree multivariate polynomials our algorithm can provide exponential speedups compared to Jordan's algorithm in terms of the dimension d. One of the technical challenges in applying our gradient computation procedure for quantum optimization problems is the need to convert between a probability oracle (which is common in quantum optimization procedures) and a phase oracle (which is common in quantum algorithms) of the objective function f. We provide efficient subroutines to perform this delicate interconversion between the two types of oracles incurring only a logarithmic overhead, which might be of independent interest. Finally, using these tools we improve the runtime of prior approaches for training quantum auto-encoders, variational quantum eigensolvers (VQE), and quantum approximate optimization algorithms (QAOA).

STOC Conference 2019 Conference Paper

Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics

  • András Gilyén
  • Yuan Su
  • Guang Hao Low
  • Nathan Wiebe

An n -qubit quantum circuit performs a unitary operation on an exponentially large, 2 n -dimensional, Hilbert space, which is a major source of quantum speed-ups. We develop a new “Quantum singular value transformation” algorithm that can directly harness the advantages of exponential dimensionality by applying polynomial transformations to the singular values of a block of a unitary operator. The transformations are realized by quantum circuits with a very simple structure - typically using only a constant number of ancilla qubits - leading to optimal algorithms with appealing constant factors. We show that our framework allows describing many quantum algorithms on a high level, and enables remarkably concise proofs for many prominent quantum algorithms, ranging from optimal Hamiltonian simulation to various quantum machine learning applications. We also devise a new singular vector transformation algorithm, describe how to exponentially improve the complexity of implementing fractional queries to unitaries with a gapped spectrum, and show how to efficiently implement principal component regression. Finally, we also prove a quantum lower bound on spectral transformations.

FOCS Conference 2017 Conference Paper

On Preparing Ground States of Gapped Hamiltonians: An Efficient Quantum Lovász Local Lemma

  • András Gilyén
  • Or Sattath

A frustration-free local Hamiltonian has the property that its ground state minimises the energy of all local terms simultaneously. In general, even deciding whether a Hamiltonian is frustration-free is a hard task, as it is closely related to the QMA 1 -complete quantum satisfiability problem (QSAT) - the quantum analogue of SAT, which is the archetypal NP-complete problem in classical computer science. This connection shows that the frustration-free property is not only relevant to physics but also to computer science. The Quantum Lovasz Local Lemma (QLLL) provides a sufficient condition for frustration-freeness. Is there an efficient way to prepare a frustration-free state under the conditions of the QLLL? Previous results showed that the answer is positive if all local terms commute. These works were based on Moser's “compression argument” which was the original analysis technique of the celebrated resampling algorithm. We generalise and simplify the “compression argument”, so that it provides a simplified version of the previous quantum results, and improves on some classical results as well. More importantly, we improve on the previous constructive results by designing an algorithm that works efficiently for non-commuting terms as well, assuming that the system is “uniformly” gapped, by which we mean that the system and all its subsystems have an inverse polynomial energy gap. Similarly to the previous results, our algorithm has the charming feature that it uses only local measurement operations corresponding to the local Hamiltonian terms.

FOCS Conference 2017 Conference Paper

Quantum SDP-Solvers: Better Upper and Lower Bounds

  • Joran van Apeldoorn
  • András Gilyén
  • Sander Gribling
  • Ronald de Wolf

Brandao and Svore recently gave quantum algorithms for approximately solving semidefinite programs, which in some regimes are faster than the best-possible classical algorithms in terms of the dimension n of the problem and the number m of constraints, but worse in terms of various other parameters. In this paper we improve their algorithms in several ways, getting better dependence on those other parameters. To this end we develop new techniques for quantum algorithms, for instance a general way to efficiently implement smooth functions of sparse Hamiltonians, and a generalized minimum-finding procedure. We also show limits on this approach to quantum SDP-solvers, for instance for combinatorial optimizations problems that have a lot of symmetry. Finally, we prove some general lower bounds showing that in the worst case, the complexity of every quantum LP-solver (and hence also SDP-solver) has to scale linearly with mn when m is approximately n, which is the same as classical.

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