Papers

L. Sanchez-Palencia·Sep 1, 2018

Envisioned by Richard Feynman in the early 1980s, quantum simulation has received dramatic impetus thanks to the development of a variety of plateforms able to emulate a wide class of quantum Hamiltonians. During the past decade, most of the quantum ...

M. Mališ, P. Barkoutsos, M. Ganzhorn +4 more·Aug 31, 2018

In this work, we develop a method to design control pulses for fixed-frequency superconducting qubits coupled via tunable couplers based on local control theory, an approach commonly employed to steer chemical reactions. Local control theory provides...

Sam McArdle, Suguru Endo, Alán Aspuru-Guzik +2 more·Aug 31, 2018

With small quantum computers becoming a reality, first applications are eagerly sought. Quantum chemistry presents a spectrum of computational problems, from relatively easy to classically intractable. Algorithms for the easiest of these have been ru...

Changpeng Shao·Aug 31, 2018

Multilayer perceptron is the most common used class of feed-forward artificial neural network. It contains many applications in diverse fields such as speech recognition, image recognition, and machine translation software. To cater for the fast deve...

P. Klimov, J. Kelly, Z. Chen +30 more·Aug 31, 2018

Superconducting qubits are an attractive platform for quantum computing since they have demonstrated high-fidelity quantum gates and extensibility to modest system sizes. Nonetheless, an outstanding challenge is stabilizing their energy-relaxation ti...

A. Milne, C. Edmunds, Cornelius Hempel +3 more·Aug 30, 2018

Entangling operations are among the most important primitive gates employed in quantum computing and it is crucial to ensure high-fidelity implementations as systems are scaled up. We experimentally realize and characterize a simple scheme to minimiz...

Daniel W. F. Alves, Michael O. Flynn·Aug 30, 2018

By modeling quantum chaotic dynamics with ensembles of random operators, we explore how a deep learning architecture known as a convolutional neural network (CNN) can be used to detect pseudorandom behavior in qubit systems. We analyze samples consis...

N. Klco, M. Savage·Aug 30, 2018

Qubit, operator and gate resources required for the digitization of lattice $\lambda\phi^4$ scalar field theories onto quantum computers are considered, building upon the foundational work by Jordan, Lee and Preskill, with a focus towards noisy inter...

W. Woods, G. Calusine, A. Melville +6 more·Aug 30, 2018

Superconducting quantum computing architectures comprise resonators and qubits that experience energy loss due to two-level systems (TLS) in bulk and interfacial dielectrics. Understanding these losses is critical to improving performance in supercon...

Teiko Heinosaari, Yui Kuramochi·Aug 30, 2018

A finite set of quantum observables (positive operator valued measures) is called compatible if these observables are marginals of a some observable, called a joint observable of them. For a given set of compatible observables, their joint observable...

Gregory R. Steinbrecher, J. Olson, D. Englund +1 more·Aug 29, 2018

Physically motivated quantum algorithms for specific near-term quantum hardware will likely be the next frontier in quantum information science. Here, we show how many of the features of neural networks for machine learning can naturally be mapped in...

G. Alagic, S. Jeffery, M. Ozols +1 more·Aug 29, 2018

Large-scale quantum computing poses a major threat to classical public-key cryptography. Recently, strong “quantum access” security models have shown that numerous symmetric-key cryptosystems are also vulnerable. In this paper, we consider classical ...

Chenxu Liu, M. Dutt, D. Pekker·Aug 29, 2018

The implementation of a high-fidelity two-qubit quantum logic gate remains an outstanding challenge for isolated solid-state qubits such as Nitrogen-Vacancy (NV) centers in diamond. In this work, we show that by driving pairs of NV centers to undergo...

Dan-Bo Zhang, Shi-Liang Zhu, Z. D. Wang·Aug 29, 2018

Incorporating nonlinearity into quantum machine learning is essential for learning a complicated input-output mapping. We here propose quantum algorithms for nonlinear regression, where nonlinearity is introduced with feature maps when loading classi...

Iordanis Kerenidis, A. Prakash·Aug 28, 2018

We present a quantum interior point method (IPM) for semi-definite programs that has a worst-case running time of Õ(n2.5 / ξ2 μ κ 3 log(1/ε)). The algorithm outputs a pair of matrices (S,Y) that have objective value within ε of the optimal and satisf...

M. Bukov·Aug 27, 2018

I demonstrate the potential of reinforcement learning (RL) to prepare quantum states of strongly periodically driven non-linear single-particle models. The ability of Q-Learning to control systems far away from equilibrium is exhibited by steering th...

Priscila G. M. dos Santos, Rodrigo S. Sousa, Ismael C. S. Araújo +1 more·Aug 27, 2018

This paper proposes a quantum-classical algorithm to evaluate and select classical artificial neural networks architectures. The proposed algorithm is based on a probabilistic quantum memory (PQM) and the possibility to train artificial neural networ...

Eric Anschütz, J. Olson, Alán Aspuru-Guzik +1 more·Aug 27, 2018

Integer factorization has been one of the cornerstone applications of the field of quantum computing since the discovery of an efficient algorithm for factoring by Peter Shor. Unfortunately, factoring via Shor’s algorithm is well beyond the capabilit...

Chai-Yu Lin, W. Su, Shin-Tza Wu·Aug 27, 2018

We provide a theoretical framework for encoding arbitrary logical states of a quantum bit (qubit) into a continuous-variable quantum mode through quantum walks. Starting with a squeezed vacuum state of the quantum mode, we show that quantum walks of ...

D. Ottaviani, A. Amendola·Aug 27, 2018

In this article we want to demonstrate the effectiveness of the new D-Wave quantum annealer, D-Wave 2000Q, in dealing with real world problems. In particular, it is shown how the quantum annealing process is able to find global optima even in the cas...