Abstract
There is no unique way to encode a quantum algorithm into a quantum circuit. With limited qubit counts, connectivities, and coherence times, circuit optimization is essential to make the best use of quantum devices produced over a next decade. We introduce two separate ideas for circuit optimization and combine them in a multi-tiered quantum circuit optimization protocol called AQCEL. The first ingredient is a technique to recognize repeated patterns of quantum gates, opening up the possibility of future hardware optimization. The second ingredient is an approach to reduce circuit complexity by identifying zero- or low-amplitude computational basis states and redundant gates. As a demonstration, AQCEL is deployed on an iterative and effcient quantum algorithm designed to model final state radiation in high energy physics. For this algorithm, our optimization scheme brings a significant reduction in the gate count without losing any accuracy compared to the original circuit. Additionally, we have investigated whether this can be demonstrated on a quantum computer using polynomial resources. Our technique is generic and can be useful for a wide variety of quantum algorithms.
Highlights
Recent technology advances have resulted in a variety of universal quantum computers that are being used to implement quantum algorithms
Circuit optimization performance of Aqcel is evaluated for a quantum circuit of the quantum parton shower (QPS) simulation with Nevol = 2 branching steps assuming an ideal topology
We have proposed a new protocol, called Aqcel, for analyzing quantum circuits to identify recurring sets of gates and remove redundant controlled operations
Summary
Recent technology advances have resulted in a variety of universal quantum computers that are being used to implement quantum algorithms These noisy-intermediate-scale quantum (NISQ) devices [1] may not have sufficient qubit counts or qubit connectivity and may not have the capability to stay coherent for entirety of the operations in a particular algorithm implementation. Despite these challenges, a variety of applications have emerged across science and industry. There are many promising studies in experimental and theoretical high energy physics (HEP) for exploiting quantum computers. A common feature of all of these algorithms is that only simplified versions can be run on existing hardware due to the limitations mentioned above
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