scholarly journals Optimal calibration of gates in trapped-ion quantum computers

2021 ◽  
Author(s):  
Andrii Maksymov ◽  
Pradeep Niroula ◽  
Yunseong Nam
Keyword(s):  
Quantum Computers ◽  
Trapped Ion ◽  
Optimal Calibration

scholarly journals Quantum gate teleportation between separated qubits in a trapped-ion processor

Science ◽  
2019 ◽  
Vol 364 (6443) ◽  
pp. 875-878 ◽  
Author(s):  
Yong Wan ◽  
Daniel Kienzler ◽  
Stephen D. Erickson ◽  
Karl H. Mayer ◽  
Ting Rei Tan ◽  
...  
Keyword(s):  
Confidence Level ◽  
Large Scale ◽  
Ion Trap ◽  
Quantum Gate ◽  
Quantum Computers ◽  
Mixed Species ◽  
Cnot Gate ◽  
Classical Communication ◽  
Trapped Ion ◽  
Single Qubit

Large-scale quantum computers will require quantum gate operations between widely separated qubits. A method for implementing such operations, known as quantum gate teleportation (QGT), requires only local operations, classical communication, and shared entanglement. We demonstrate QGT in a scalable architecture by deterministically teleporting a controlled-NOT (CNOT) gate between two qubits in spatially separated locations in an ion trap. The entanglement fidelity of our teleported CNOT is in the interval (0.845, 0.872) at the 95% confidence level. The implementation combines ion shuttling with individually addressed single-qubit rotations and detections, same- and mixed-species two-qubit gates, and real-time conditional operations, thereby demonstrating essential tools for scaling trapped-ion quantum computers combined in a single device.

scholarly journals Scaling Trapped Ion Quantum Computers Using Fast Gates and Microtraps

2018 ◽  
Vol 120 (22) ◽  
Author(s):  
Alexander K. Ratcliffe ◽  
Richard L. Taylor ◽  
Joseph J. Hope ◽  
André R. R. Carvalho
Keyword(s):  
Quantum Computers ◽  
Trapped Ion

Networking Trapped-ion Quantum Computers

2019 ◽  
Author(s):  
C. J. Ballance ◽  
L. J. Stephenson ◽  
D. P. Nadlinger ◽  
B. C. Nichol ◽  
S. An ◽  
...  
Keyword(s):  
Quantum Computers ◽  
Trapped Ion

scholarly journals Decoherence bounds on the capabilities of cold trapped ion quantum computers

1997 ◽  
Author(s):  
Daniel F. James ◽  
Richard J. Hughes ◽  
Emanuel H. Knill ◽  
Raymond Laflamme ◽  
Albert G. Petschek
Keyword(s):  
Quantum Computers ◽  
Trapped Ion

scholarly journals Nearest centroid classification on a trapped ion quantum computer

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Sonika Johri ◽  
Shantanu Debnath ◽  
Avinash Mocherla ◽  
Alexandros SINGK ◽  
Anupam Prakash ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantum Computer ◽  
State Of The Art ◽  
Synthetic Data ◽  
Quantum Computers ◽  
Trapped Ion ◽  
Quantum Machine Learning ◽  
Real World Applications ◽  
Promising Area ◽  
Centroid Classifier

AbstractQuantum machine learning has seen considerable theoretical and practical developments in recent years and has become a promising area for finding real world applications of quantum computers. In pursuit of this goal, here we combine state-of-the-art algorithms and quantum hardware to provide an experimental demonstration of a quantum machine learning application with provable guarantees for its performance and efficiency. In particular, we design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations, and experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.

scholarly journals Shuttle-Exploiting Attacks and Their Defenses in Trapped-Ion Quantum Computers

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Abdullah Ash Saki ◽  
Rasit Onur Topaloglu ◽  
Swaroop Ghosh
Keyword(s):  
Quantum Computers ◽  
Trapped Ion

scholarly journals Filtering variational quantum algorithms for combinatorial optimization

2021 ◽  
Author(s):  
David Amaro ◽  
Carlo Modica ◽  
Matthias Rosenkranz ◽  
Mattia Fiorentini ◽  
Marcello Benedetti ◽  
...  
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Algorithm ◽  
Quantum Computer ◽  
Quantum Algorithms ◽  
Optimal Solution ◽  
Quantum Computers ◽  
Trapped Ion ◽  
Quantum Processor ◽  
Computational Advantage ◽  
Better Than

Abstract Current gate-based quantum computers have the potential to provide a computational advantage if algorithms use quantum hardware efficiently. To make combinatorial optimization more efficient, we introduce the Filtering Variational Quantum Eigensolver (F-VQE) which utilizes filtering operators to achieve faster and more reliable convergence to the optimal solution. Additionally we explore the use of causal cones to reduce the number of qubits required on a quantum computer. Using random weighted MaxCut problems, we numerically analyze our methods and show that they perform better than the original VQE algorithm and the Quantum Approximate Optimization Algorithm (QAOA). We also demonstrate the experimental feasibility of our algorithms on a Honeywell trapped-ion quantum processor.

scholarly journals Materials challenges for trapped-ion quantum computers

2021 ◽  
Author(s):  
Kenneth R. Brown ◽  
John Chiaverini ◽  
Jeremy M. Sage ◽  
Hartmut Häffner
Keyword(s):  
Quantum Computers ◽  
Trapped Ion
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