Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Controlled-Controlled-Phase Gates for Superconducting Qubits Mediated by a Shared Tunable Coupler
N. J. Glaser, F. Roy, S. Filipp·Jun 24, 2022
Applications for noisy intermediate-scale quantum computing devices rely on the efficient entanglement of many qubits to reach a potential quantum advantage. Although entanglement is typically generated using two-qubit gates, direct control of strong...
Fast classical simulation of Hamiltonian dynamics by simultaneous diagonalization using Clifford transformation with parallel computation
Yoshiaki Kawase, K. Fujii·Jun 23, 2022
Simulating quantum many-body dynamics is important both for fundamental understanding of physics and practical applications for quantum information processing. Therefore, classical simulation methods have been developed so far. Specifically, the Trot...
Quantum network medicine: rethinking medicine with network science and quantum algorithms
S. Maniscalco, Elsi-Mari Borrelli, D. Cavalcanti +14 more·Jun 22, 2022
Scientific and technological advances in medicine and systems biology have unequivocally shown that health and disease must be viewed in the context of the interplay among multiple molecular and environmental factors. Understanding the effects of cel...
Quantum self-consistent equation-of-motion method for computing molecular excitation energies, ionization potentials, and electron affinities on a quantum computer
Ayush Asthana, Ashutosh Kumar, Vibin Abraham +8 more·Jun 21, 2022
Near-term quantum computers are expected to facilitate material and chemical research through accurate molecular simulations. Several developments have already shown that accurate ground-state energies for small molecules can be evaluated on present-...
Variational quantum and quantum-inspired clustering
Pablo Bermejo, R. Orús·Jun 20, 2022
Here we present a quantum algorithm for clustering data based on a variational quantum circuit. The algorithm allows to classify data into many clusters, and can easily be implemented in few-qubit Noisy Intermediate-Scale Quantum devices. The idea of...
Inference-Based Quantum Sensing
C. H. Alderete, Max Hunter Gordon, F. Sauvage +4 more·Jun 20, 2022
In a standard quantum sensing (QS) task one aims at estimating an unknown parameter θ, encoded into an n-qubit probe state, via measurements of the system. The success of this task hinges on the ability to correlate changes in the parameter to change...
Classical splitting of parametrized quantum circuits
Cenk Tüysüz, Giuseppe Clemente, Arianna Crippa +3 more·Jun 20, 2022
Barren plateaus appear to be a major obstacle for using variational quantum algorithms to simulate large-scale quantum systems or to replace traditional machine learning algorithms. They can be caused by multiple factors such as the expressivity of t...
Quantum AI simulator using a hybrid CPU–FPGA approach
Teppei Suzuki, Tsubasa Miyazaki, Toshiki Inaritai +1 more·Jun 20, 2022
The quantum kernel method has attracted considerable attention in the field of quantum machine learning. However, exploring the applicability of quantum kernels in more realistic settings has been hindered by the number of physical qubits current noi...
Localization Detection Based on Quantum Dynamics
K. Kudo·Jun 18, 2022
Detecting many-body localization (MBL) typically requires the calculation of high-energy eigenstates using numerical approaches. This study investigates methods that assume the use of a quantum device to detect disorder-induced localization. Numerica...
Mapping renormalized coupled cluster methods to quantum computers through a compact unitary representation of nonunitary operators
Bo Peng, K. Kowalski·Jun 17, 2022
Non-unitary theories are commonly seen in the classical simulations of quantum systems. Among these theories, the method of moments of coupled-cluster equations (MMCCs) and the ensuing classes of the renormalized coupled-cluster (CC) approaches have ...
Quantum anomaly detection for collider physics
Sulaiman Alvi, C. Bauer, B. Nachman·Jun 16, 2022
We explore the use of Quantum Machine Learning (QML) for anomaly detection at the Large Hadron Collider (LHC). In particular, we explore a semi-supervised approach in the four-lepton final state where simulations are reliable enough for a direct back...
Linear cross-entropy benchmarking with Clifford circuits
Jianxin Chen, D. Ding, Cupjin Huang +1 more·Jun 16, 2022
With the advent of quantum processors exceeding $100$ qubits and the high engineering complexities involved, there is a need for holistically benchmarking the processor to have quality assurance. Linear cross-entropy benchmarking (XEB) has been used ...
Comparative analysis of error mitigation techniques for variational quantum eigensolver implementations on IBM quantum system
Shaobo Zhang, C. Hill, M. Usman·Jun 16, 2022
Quantum computers are anticipated to transcend classical supercomputers for computationally intensive tasks by exploiting the principles of quantum mechanics. However, the capabilities of the current generation of quantum devices are limited due to n...
Bayesian learning of parameterised quantum circuits
Samuel Duffield, Marcello Benedetti, Matthias Rosenkranz·Jun 15, 2022
Currently available quantum computers suffer from constraints including hardware noise and a limited number of qubits. As such, variational quantum algorithms that utilise a classical optimiser in order to train a parameterised quantum circuit have d...
High-fidelity qutrit entangling gates for superconducting circuits
Noah Goss, A. Morvan, Brett Marinelli +9 more·Jun 15, 2022
Ternary quantum information processing in superconducting devices poses a promising alternative to its more popular binary counterpart through larger, more connected computational spaces and proposed advantages in quantum simulation and error correct...
Steered quantum annealing: improving time efficiency with partial information
Ana de Luis, A. García-Sáez, M. Estarellas·Jun 15, 2022
In the computational model of quantum annealing, the size of the minimum gap between the ground state and the first excited state of the system is of particular importance, since it is inversely proportional to the running time of the algorithm. Thus,...
Comparing the hardness of MAX 2-SAT problem instances for quantum and classical algorithms
Puya Mirkarimi, A. Callison, Lewis Light +2 more·Jun 14, 2022
An algorithm for a particular problem may find some instances of the problem easier and others harder to solve, even for a fixed input size. We numerically analyse the relative hardness of MAX 2-SAT problem instances for various continuous-time quant...
Entanglement perspective on the quantum approximate optimization algorithm
M. Dupont, N. Didier, Mark Hodson +2 more·Jun 14, 2022
Many quantum algorithms seek to output a specific bitstring solving the problem of interest—or a few if the solution is degenerate. It is the case for the quantum approximate optimization algorithm (QAOA) in the limit of large circuit depth, which aim...
Characterization of variational quantum algorithms using free fermions
G. Matos, C. Self, Zlatko Papi'c +2 more·Jun 13, 2022
We study variational quantum algorithms from the perspective of free fermions. By deriving the explicit structure of the associated Lie algebras, we show that the Quantum Approximate Optimization Algorithm (QAOA) on a one-dimensional lattice – with a...
Synthesis of and compilation with time-optimal multi-qubit gates
Pascal Bassler, Matthias Zipper, C. Cedzich +4 more·Jun 13, 2022
We develop a method to synthesize a class of entangling multi-qubit gates for a quantum computing platform with fixed Ising-type interaction with all-to-all connectivity. The only requirement on the flexibility of the interaction is that it can be sw...