Papers
Live trends in quantum computing research, updated daily from arXiv.
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Hardware platform mentions in abstracts — Photonic leads
Quantum algorithm for matrix functions by Cauchy's integral formula
S. Takahira, A. Ohashi, T. Sogabe +1 more·Feb 1, 2020
For matrix A, vector b and function f, the computation of vector f(A)b arises in many scientific computing applications. We consider the problem of obtaining quantum state |f> corresponding to vector f(A)b. There is a quantum algorithm to compute sta...
Simulating quantum chemistry in the restricted Hartree-Fock space on a qubit-based quantum computing device
V. Elfving, Jos'e A. G'amez, C. Gogolin·Jan 31, 2020
Accurate quantum chemistry simulations remain challenging on classical computers for problems of industrially relevant sizes and there is reason for hope that quantum computing may help push the boundaries of what is technically feasible. While varia...
A Variational Quantum Algorithm for Preparing Quantum Gibbs States
Anirban Narayan Chowdhury, G. Low, N. Wiebe·Jan 31, 2020
Preparation of Gibbs distributions is an important task for quantum computation. It is a necessary first step in some types of quantum simulations and further is essential for quantum algorithms such as quantum Boltzmann training. Despite this, most ...
Absence of logarithmic divergence of the entanglement entropies at the phase transitions of a 2D classical hard rod model
Christophe Chatelain, A. Gendiar·Jan 31, 2020
Entanglement entropy is a powerful tool to detect continuous, discontinuous and even topological phase transitions in quantum as well as classical systems. In this work, von Neumann and Renyi entanglement entropies are studied numerically for classic...
A hybrid quantum enabled RBM advantage: convolutional autoencoders for quantum image compression and generative learning
Jennifer Sleeman, J. Dorband, M. Halem·Jan 31, 2020
Understanding how the D-Wave quantum computer could be used for machine learning problems is of growing interest. Our work explores the feasibility of using the D-Wave as a sampler for a machine learning task. We describe a hybrid method that combine...
Non-Pauli topological stabilizer codes from twisted quantum doubles
J. M. D. L. Fuente, N. Tarantino, J. Eisert·Jan 30, 2020
It has long been known that long-ranged entangled topological phases can be exploited to protect quantum information against unwanted local errors. Indeed, conditions for intrinsic topological order are reminiscent of criteria for faithful quantum er...
Quantum Simulation of Field Theories Without State Preparation
Siddhartha Harmalkar, Henry Lamm, S. Lawrence·Jan 30, 2020
We propose an algorithm for computing real-time observables using a quantum processor while avoiding the need to prepare the full quantum state. This reduction in quantum resources is achieved by classically sampling configurations in imaginary-time ...
The Hidden Subgroup Problem for Universal Algebras
M. Moore, Taylor Walenczyk·Jan 30, 2020
The Hidden Subgroup Problem (HSP) is a computational problem which includes as special cases integer factorization, the discrete logarithm problem, graph isomorphism, and the shortest vector problem. The celebrated polynomial-time quantum algorithms ...
Quantum circuits for the realization of equivalent forms of one-dimensional discrete-time quantum walks on near-term quantum hardware
Shivani Singh, C. H. Alderete, R. Balu +3 more·Jan 30, 2020
Quantum walks are a promising framework for developing quantum algorithms and quantum simulations. They represent an important test case for the application of quantum computers. Here we present different forms of discrete-time quantum walks (DTQWs) ...
Computing real time correlation functions on a hybrid classical/quantum computer
N. Mueller, A. Tarasov, R. Venugopalan·Jan 30, 2020
Abstract Quantum devices may overcome limitations of classical computers in studies of nuclear structure functions and parton Wigner distributions of protons and nuclei. In this talk, we discuss a worldline approach to compute nuclear structure funct...
Single-site Rydberg addressing in 3D atomic arrays for quantum computing with neutral atoms
Xiao-Feng Shi·Jan 30, 2020
Neutral atom arrays are particularly promising for large-scale quantum computing because it is possible to prepare large-scale qubit arrays. An unsolved issue is how to selectively excite one qubit deep in a 3D atomic array to Rydberg states. In this...
Hierarchical decoding to reduce hardware requirements for quantum computing
Nicolas Delfosse·Jan 30, 2020
Extensive quantum error correction is necessary in order to scale quantum hardware to the regime of practical applications. As a result, a significant amount of decoding hardware is necessary to process the colossal amount of data required to constan...
A Classical $\pi$ Machine and Grover's Algorithm
Jiang Liu·Jan 29, 2020
This paper studies a well-known π machine illustrated by Fig. (1). It is shown that the π machine can compute digits of π if the ratio of block weights, m 2 /m 1 , satisfies certain conditions, and that dynamics of the π machine is identical to that o...
On-chip deterministic operation of quantum dots in dual-mode waveguides for a plug-and-play single-photon source
R. Uppu, H. T. Eriksen, H. Thyrrestrup +9 more·Jan 29, 2020
A deterministic source of coherent single photons is an enabling device for quantum information processing. Quantum dots in nanophotonic structures have been employed as excellent sources of single photons with the promise of scaling up towards multi...
Finding broken gates in quantum circuits: exploiting hybrid machine learning
Margarite L. LaBorde, Allee C. Rogers, J. Dowling·Jan 29, 2020
Current implementations of quantum logic gates can be highly faulty and introduce errors. In order to correct these errors, it is necessary to first identify the faulty gates. We demonstrate a procedure to diagnose where gate faults occur in a circui...
On quantum ensembles of quantum classifiers
M. Schuld, Francesco Petruccione·Jan 29, 2020
Quantum machine learning seeks to exploit the underlying nature of a quantum computer to enhance machine learning techniques. A particular framework uses the quantum property of superposition to store sets of parameters, thereby creating an ensemble ...
Fast quantum learning with statistical guarantees
C. Ciliberto, Andrea Rocchetto, Alessandro Rudi +1 more·Jan 28, 2020
Within the framework of statistical learning theory it is possible to bound the minimum number of samples required by a learner to reach a target accuracy. We show that if the bound on the accuracy is taken into account, quantum machine learning algo...
Enhancing a Near-Term Quantum Accelerator's Instruction Set Architecture for Materials Science Applications
X. Zou, A. Matsuura, S. Premaratne +7 more·Jan 28, 2020
Quantum computers with tens to hundreds of noisy qubits are being developed today. To be useful for real-world applications, we believe that these near-term systems cannot simply be scaled-down non-error-corrected versions of future fault-tolerant la...
Intel Quantum Simulator: a cloud-ready high-performance simulator of quantum circuits
G. Guerreschi, J. Hogaboam, F. Baruffa +1 more·Jan 28, 2020
Classical simulation of quantum computers will continue to play an essential role in the progress of quantum information science, both for numerical studies of quantum algorithms and for modeling noise and errors. Here we introduce the latest release...
How symmetric is too symmetric for large quantum speedups?
S. Ben-David, Supartha Podder·Jan 27, 2020
Suppose a Boolean function $f$ is symmetric under a group action $G$ acting on the $n$ bits of the input. For which $G$ does this mean $f$ does not have an exponential quantum speedup? Is there a characterization of how rich $G$ must be before the fu...