There were a number of interesting research papers published this month that we think may be significant for future quantum computing applications. Although we are not able to provide in-depth reviews of each of these, we are providing a short description along with a link so our readers can study them in more details. We apologize in advance for any that we have missed, but with the ever expanding research currently going on in quantum computing it is becoming exponentially hard to be aware of all of the excellent research taking place.
Title: Building a Fault-Tolerant Quantum Computer Using Concatenated Cat Codes
Organization: Amazon AWS Center for Quantum Computing and Others
A research paper on a new hybrid electro-acoustic qubit gives a theoretical analysis of an architecture for building a fault-tolerant quantum computer with hybrid electro-acoustic qubits. By designing more efficient fault-tolerant schemes tailored to the properties of the hybrid electro-acoustic qubits, researchers conclude that one could realize a greater than 10x improvement in the overhead required for building a fault-tolerant quantum computer. You can view the paper published on arXiv here.
Title: Demonstration of Quantum Machine Learning (QML) Algorithms on Near-Term Quantum Hardware
Organizations: QC Ware and IonQ
QC Ware has written a QML algorithm to implement at nearest centroid classification on MNIST handwritten digits using IonQ’s 11 qubit trapped-ion quantum computer. The algorithm uses the Forge Data Loader capability that QC Ware announced last July to load in the classical data into quantum states. You can view a Medium article posted by QC Ware about this here and a paper posted on arXiv here.
Title: Generation of High Resolution Hand Written Digits with an Ion-Trap Quantum Computer
Organizations: Zapata and IonQ
The paper describes the first practical and experimental implementation of a quantum-classical generative algorithm capable of generating high-resolution images of handwritten digits with state-of-the-art gate-based quantum computers. You can view the paper on arXiv here.
Title: Foundations for Near-Term Quantum Natural Language Processing
Organization: Cambridge Quantum Computing
Scientists from Cambridge Quantum Computing, in collaboration with Oxford University, say they have proven that quantum computers will be able to interpret and understand human language, grammar and sentiment far beyond any previous technology. You can view the arXiv paper here and an associated news release about it here.
Title: A Threshold for Quantum Advantage in Derivative Pricing
Organizations: Goldman Sachs and IBM
The paper gives an upper bound on the resources required for valuable quantum advantage in pricing derivatives. The benchmark find that the use cases examined require 7.5k logical qubits, a T-depth of 46 million and estimated logical clock speed required of 10 Mhz. While these resource requirements are out of reach of current systems, the authors hope they will provide a roadmap for further improvements in algorithms, implementations, and planned hardware architectures. You can view the paper on arXiv here.
Title: Low Depth Circuits for Quantum Amplitude Estimation
Organizations: Goldman Sachs and QC Ware
The paper describes two new low depth algorithms for amplitude estimation (AE) to achieve an optimal tradeoff between the quantum speedup and circuit depth. These algorithms bring quantum speedups for Monte Carlo methods closer to realization, as they can provide speedups with shallower circuits. You can view the paper on arXiv here.
December 19, 2020