Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Optimal algorithms for learning quantum phase states
Srinivasan Arunachalam, S. Bravyi, Arko Dutt +1 more·Aug 16, 2022
We analyze the complexity of learning $n$-qubit quantum phase states. A degree-$d$ phase state is defined as a superposition of all $2^n$ basis vectors $x$ with amplitudes proportional to $(-1)^{f(x)}$, where $f$ is a degree-$d$ Boolean polynomial ov...
Tunable Planar Josephson Junctions Driven by Time-Dependent Spin-Orbit Coupling
D. Monroe, M. Alidoust, I. Žutić·Aug 16, 2022
Integrating conventional superconductors with common III-V semiconductors provides a versatile platform to implement tunable Josephson junctions (JJs) and their applications. We propose that with gate-controlled time-dependent spin-orbit coupling, it...
Efficient Parameterised Compilation for Hybrid Quantum Programming
Anna M. Krol, Koen J. Mesman, A. Sarkar +1 more·Aug 16, 2022
Near term quantum devices have the potential to outperform classical computing through the use of hybrid classical-quantum algorithms such as Variational Quantum Eigensolvers. These iterative algorithms use a classical optimiser to update a parameter...
Mean estimation when you have the source code; or, quantum Monte Carlo methods
Robin Kothari, R. O'Donnell·Aug 16, 2022
Suppose $\boldsymbol{y}$ is a real random variable, and one is given access to ``the code'' that generates it (for example, a randomized or quantum circuit whose output is $\boldsymbol{y}$). We give a quantum procedure that runs the code $O(n)$ times...
Evaluate Quantum Combinatorial Optimization for Distribution Network Reconfiguration
P. Ngo, Christan Thomas, Hieu Nguyen +2 more·Aug 16, 2022
This paper aims to implement and evaluate the performance of quantum computing on solving combinatorial optimization problems arising from the operations of the power grid. To this end, we construct a novel mixed integer conic programming formulation...
Modeling and simulation of a quantum thermal noise on the qubit
F. Chapeau-Blondeau·Aug 15, 2022
Quantum noise or decoherence is a major factor impacting the performance of quantum technologies. On the qubit, an important quantum noise, often relevant in practice, is the thermal noise or generalized amplitude damping noise, describing the intera...
On quantum factoring using noisy intermediate scale quantum computers
Vivian Phan, A. Ponni, M. Raasakka +1 more·Aug 15, 2022
We study the performance and resource usage of the variational quantum factoring (VQF) algorithm for different instance sizes and optimization algorithms. Our simulations show better chance of finding the ground state when using VQE rather than QAOA ...
Federated Quantum Natural Gradient Descent for Quantum Federated Learning
Jun Qi·Aug 15, 2022
The heart of Quantum Federated Learning (QFL) is associated with a distributed learning architecture across several local quantum devices and a more efficient training algorithm for the QFL is expected to minimize the communication overhead among diff...
Quantum Support Vector Machines for Aerodynamic Classification
Xinjian Yuan, Ziyang Chen, Yudan Liu +5 more·Aug 15, 2022
Aerodynamics plays an important role in aviation industry and aircraft design. Detecting and minimizing the phenomenon of flow separation from scattered pressure data on airfoil is critical for ensuring stable and efficient aviation. However, since i...
Fabrication of airbridges with gradient exposure
Yuting Sun, Jiayu Ding, Xiaoyu Xia +6 more·Aug 15, 2022
In superconducting quantum circuits, airbridges are critical for eliminating parasitic slotline modes of coplanar waveguide circuits and reducing crosstalks between direct current magnetic flux biases. Here, we present a technique for fabricating sup...
Quantum Bandit With Amplitude Amplification Exploration in an Adversarial Environment
Byungjin Cho, Yu Xiao, Pan Hui +1 more·Aug 15, 2022
The rapid proliferation of learning systems in an arbitrarily changing environment mandates the need to manage tensions between exploration and exploitation. This work proposes a quantum-inspired bandit learning approach for the learning-and-adapting...
Exploring the scaling limitations of the variational quantum eigensolver with the bond dissociation of hydride diatomic molecules
Jacob M. Clary, E. Jones, Derek Vigil-Fowler +2 more·Aug 15, 2022
Materials simulations involving strongly correlated electrons pose fundamental challenges to state-of-the-art electronic structure methods but are hypothesized to be the ideal use case for quantum computing. To date, no quantum computer has simulated...
Solving boolean satisfiability problems with the quantum approximate optimization algorithm
Sami Boulebnane, A. Montanaro·Aug 14, 2022
The quantum approximate optimization algorithm (QAOA) is one of the most prominent proposed applications for near-term quantum computing. Here we study the ability of QAOA to solve hard constraint satisfaction problems, as opposed to optimization pro...
Efficient quantum algorithms for solving quantum linear system problems
Hefeng Wang, Hua Xiang·Aug 14, 2022
We transform the problem of solving linear system of equations A x = b to a problem of finding the right singular vector with singular value zero of an augmented matrix C , and present two quantum algorithms for solving this problem. The first algorith...
Time-marching based quantum solvers for time-dependent linear differential equations
Di Fang, Lin Lin, Yu Tong·Aug 14, 2022
The time-marching strategy, which propagates the solution from one time step to the next, is a natural strategy for solving time-dependent differential equations on classical computers, as well as for solving the Hamiltonian simulation problem on qua...
Imperfect Quantum Photonic Neural Networks
Jacob Ewaniuk, J. Carolan, B. Shastri +1 more·Aug 13, 2022
Quantum photonic neural networks are variational photonic circuits that can be trained to implement high‐fidelity quantum operations. However, work‐to‐date has assumed idealized components, including a perfect π Kerr nonlinearity. This work investiga...
Improving Quantum Measurements by Introducing "Ghost" Pauli Products.
Seonghoon Choi, Tzu-Ching Yen, A. Izmaylov·Aug 13, 2022
Reducing the number of measurements required to estimate the expectation value of an observable is crucial for the variational quantum eigensolver to become competitive with state-of-the-art classical algorithms. To measure complicated observables su...
CollComm: Enabling Efficient Collective Quantum Communication Based on EPR buffering
Anbang Wu, Yufei Ding, A. Li·Aug 13, 2022
The noisy and lengthy nature of quantum communication hinders the development of distributed quantum computing. The inefficient design of existing compilers for distributed quantum computing worsens the situation. Previous compilation frameworks coup...
Multi-stage Stern-Gerlach experiment modeled (with additional appendices)
Lihong V. Wang·Aug 12, 2022
In the classic multi-stage Stern$-$Gerlach experiment conducted by Frisch and Segrè, the Majorana (Landau$-$Zener) and Rabi formulae diverge afar from the experimental observation while the physical mechanism for electron-spin collapse remains uniden...
On establishing learning separations between classical and quantum machine learning with classical data
Casper Gyurik, V. Dunjko·Aug 12, 2022
Despite years of effort, the quantum machine learning community has only been able to show quantum learning advantages for certain contrived cryptography-inspired datasets in the case of classical data. In this note, we discuss the challenges of find...