Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Multi-mode architectures for noise-resilient superconducting qubits
A. Calzona, M. Carrega·Aug 4, 2022
Great interest revolves around the development of new strategies to efficiently store and manipulate quantum information in a robust and decoherence-free fashion. Several proposals have been put forward to encode information into qubits that are simu...
Quantum chemistry simulation of ground- and excited-state properties of the sulfonium cation on a superconducting quantum processor
M. Motta, G. O. Jones, J. Rice +5 more·Aug 4, 2022
The computational description of correlated electronic structure, and particularly of excited states of many-electron systems, is an anticipated application for quantum devices. An important ramification is to determine the dominant molecular fragmen...
wpScalable Quantum Neural Networks for Classification
Jindi Wu, Zeyi Tao, Qun Li·Aug 4, 2022
Many recent machine learning tasks resort to quantum computing to improve classification accuracy and training efficiency by taking advantage of quantum mechanics, known as quantum machine learning (QML). The variational quantum circuit (VQC) is freq...
Neural network accelerator for quantum control
David Xu, A. B. Özgüler, G. D. Guglielmo +4 more·Aug 4, 2022
Efficient quantum control is necessary for practical quantum computing implementations with current technologies. Conventional algorithms for determining optimal control parameters are computationally expensive, largely excluding them from use outsid...
Quantum approximate optimization algorithm in non-Markovian quantum systems
Bo Yue, Shibei Xue, Yu Pan +1 more·Aug 3, 2022
Although quantum approximate optimization algorithm (QAOA) has demonstrated its quantum supremacy, its performance on Noisy Intermediate-Scale Quantum (NISQ) devices would be influenced by complicated noises, e.g. quantum colored noises. To evaluate ...
Evaluating the evidence for exponential quantum advantage in ground-state quantum chemistry
Seunghoon Lee, Joonho Lee, Huanchen Zhai +15 more·Aug 3, 2022
Due to intense interest in the potential applications of quantum computing, it is critical to understand the basis for potential exponential quantum advantage in quantum chemistry. Here we gather the evidence for this case in the most common task in ...
Implementing Fault-tolerant Entangling Gates on the Five-qubit Code and the Color Code
C. Ryan-Anderson, N. C. Brown, M. S. Allman +34 more·Aug 3, 2022
We compare two different implementations of fault-tolerant entangling gates on logical qubits. In one instance, a twelve-qubit trapped-ion quantum computer is used to implement a non-transversal logical CNOT gate between two five qubit codes. The ope...
Quantum mean centering for block-encoding-based quantum algorithm
Hailing Liu, Chao-Hua Yu, Lin-Chun Wan +3 more·Aug 3, 2022
Mean Centering (MC) is an important data preprocessing technique, which has a wide range of applications in data mining, machine learning, and multivariate statistical analysis. When the data set is large, this process will be time-consuming. In this...
Re-QGAN: an optimized adversarial quantum circuit learning framework
Sandra Nguemto, Vicente Leyton-Ortega·Aug 3, 2022
Adversarial learning represents a powerful technique for generating data statistics. Its successful implementation in quantum computational platforms is not straightforward due to limitations in connectivity, quantum operation fidelity, and limited a...
Information-theoretic hardness of out-of-time-order correlators
Jordan S. Cotler, T. Schuster, M. Mohseni·Aug 3, 2022
We establish that there are properties of quantum many-body dynamics which are efficiently learnable if we are given access to out-of-time-order correlators (OTOCs), but which require exponentially many operations in the system size if we can only me...
Quantum Encoding and Analysis on Continuous Time Stochastic Process with Financial Applications
Xi-Ning Zhuang, Zhao-Yun Chen, Cheng Xue +2 more·Aug 3, 2022
Modeling stochastic phenomena in continuous time is an essential yet challenging problem. Analytic solutions are often unavailable, and numerical methods can be prohibitively time-consuming and computationally expensive. To address this issue, we pro...
Active learning on a programmable photonic quantum processor
Chen Ding, Xiao-Yue Xu, Yun-Fei Niu +3 more·Aug 3, 2022
Training a quantum machine learning model generally requires a large labeled dataset, which incurs high labeling and computational costs. To reduce such costs, a selective training strategy, called active learning (AL), chooses only a subset of the o...
Optimizing counterdiabaticity by variational quantum circuits
Dan Sun, P. Chandarana, Z. Xin +1 more·Aug 3, 2022
Using counterdiabatic (CD) driving—aiming at suppression of diabatic transition—in digitized adiabatic evolution has garnered immense interest in quantum protocols and algorithms. However, improving the approximate CD terms with a nested commutator a...
Energy transport and optimal design of noisy Platonic quantum networks
C. Javaherian, C. Ferrie·Aug 2, 2022
Optimal energy transport is one of the primary goals for designing efficient quantum networks. In this work, the maximum energy transport is investigated for three-dimensional quantum networks with Platonic geometries affected by dephasing and dissip...
Simulating Quantum Circuits Using Efficient Tensor Network Contraction Algorithms with Subexponential Upper Bound.
T. Wahl, Sergii Strelchuk·Aug 2, 2022
We derive a rigorous upper bound on the classical computation time of finite-ranged tensor network contractions in d≥2 dimensions. Consequently, we show that quantum circuits of single-qubit and finite-ranged two-qubit gates can be classically simula...
Unsupervised quantum machine learning for fraud detection
Oleksandr Kyriienko, E. Magnusson·Aug 2, 2022
We develop quantum protocols for anomaly detection and apply them to the task of credit card fraud detection (FD). First, we establish classical benchmarks based on supervised and unsupervised machine learning methods, where average precision is chos...
Enhancing the coherence of superconducting quantum bits with electric fields
J. Lisenfeld, A. Bilmes, A. Ustinov·Aug 2, 2022
In the endeavor to make quantum computers a reality, integrated superconducting circuits have become a promising architecture. A major challenge of this approach is decoherence originating from spurious atomic tunneling defects at the interfaces of q...
An Algorithm for Ennola's Second Theorem and Counting Smooth Numbers in Practice
Chloe M. Makdad, Jonathan P. Sorenson·Aug 2, 2022
Let Ψ( x, y ) count the number of positive integers n ≤ x such that every prime divisor of n is at most y . There are a number of ap-plications where values of Ψ( x, y ) are needed, such as in optimizing integer factoring and discrete logarithm algor...
NAPA: Intermediate-Level Variational Native-Pulse Ansatz for Variational Quantum Algorithms
Zhiding Liang, Jinglei Cheng, Hang Ren +8 more·Aug 2, 2022
Variational quantum algorithms (VQAs) have demonstrated great potentials in the noisy intermediate scale quantum (NISQ) era. In the workflow of VQA, the parameters of ansatz are iteratively updated to approximate the desired quantum states. We have s...
Techniques for combining fast local decoders with global decoders under circuit-level noise
C. Chamberland, L. Gonçalves, P. Sivarajah +2 more·Aug 2, 2022
Implementing algorithms on a fault-tolerant quantum computer will require fast decoding throughput and latency times to prevent an exponential increase in buffer times between the applications of gates. In this work we begin by quantifying these requ...