Further Quantum Algorithms and Hardware

Jincheng Zhang

The burgeoning field of quantum computing hinges on the efficient execution of quantum algorithms. However, realizing this potential necessitates overcoming significant challenges related to hardware heterogeneity. Current quantum hardware comprises a diverse range of platforms, each possessing unique characteristics – varying qubit connectivity, gate fidelities, and coherence times. A naive approach to scheduling quantum algorithms across these platforms will likely result in suboptimal performance due to the inherent limitations of each device. This work proposes a novel resource-aware scheduling algorithm designed to mitigate these issues. The algorithm dynamically analyzes both the available resources of a quantum hardware platform and the specific algorithmic requirements of a given quantum algorithm. The core of the algorithm involves a cost function that balances resource utilization with algorithmic performance, guiding the selection of the most suitable hardware platform and execution strategy. This approach represents a step towards a more practical and efficient deployment of quantum algorithms, paving the way for leveraging the strengths of diverse quantum computing architectures. The presented method offers a framework for optimizing quantum computation, acknowledging the complex interplay between hardware and algorithm.

Tzvetan S. Metodi, Arvin I. Faruque, Frederic T. Chong

Song Yuan Yan

Marcello Benedetti, Mattia Fiorentini, Michael Lubasch

Parameterized quantum circuits are a promising technology for achieving a quantum advantage. An important application is the variational simulation of time evolution of quantum systems. To make the most of quantum hardware, variational algorithms need to be as hardware-efficient as possible. Here we present alternatives to the time-dependent variational principle that are hardware-efficient and do not require matrix inversion. In relation to imaginary time evolution, our approach significantly reduces the hardware requirements. With regards to real time evolution, where high precision can be important, we present algorithms of systematically increasing accuracy and hardware requirements. We numerically analyze the performance of our algorithms using quantum Hamiltonians with local interactions.

Stefanie Muroya, Krishnendu Chatterjee, Thomas A. Henzinger

Quantum hardware is inherently fragile and noisy. We find that the accuracy of traditional quantum error correction algorithms can be improved depending on the hardware. Given different hardware specifications, we automatically synthesize hardware-optimal algorithms for parity correction, qubit resetting, and GHZ (Greenberger-Horne-Zeilinger) state preparation. Using stochastic techniques from computer science, our method presents a computational tool to compute exact accuracy guarantees and synthesize optimal algorithms that are often different from traditional ones. We also show that improvements can be gained with respect to the Qiskit transpiler as we compute the hardware-optimal qubit mapping for the GHZ state-preparation problem.

Author 1 - Mr. K. Suresh Babu Author 2 : Dr. T. P. Anish Author 3 : Dr. Cornelius K