A template-based technique for efficient Clifford+T-based quantum circuit implementation

The near-future possibility of Quantum supremacy, which aspires to establish a set of algorithms running efficiently on a Quantum computer – have significantly fuelled the interest in design and automation of Quantum circuits. Multiple technologies such as Ion-Trap, Nuclear Magnetic Resonance (NMR),...

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Bibliographic Details
Main Authors: Biswal, Laxmidhar, Das, Rakesh, Bandyopadhyay, Chandan, Chattopadhyay, Anupam, Rahaman, Hafizur
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2020
Subjects:
BDD
Online Access:https://hdl.handle.net/10356/139091
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Institution: Nanyang Technological University
Language: English
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Summary:The near-future possibility of Quantum supremacy, which aspires to establish a set of algorithms running efficiently on a Quantum computer – have significantly fuelled the interest in design and automation of Quantum circuits. Multiple technologies such as Ion-Trap, Nuclear Magnetic Resonance (NMR), have made great progress in recent years towards a practical Quantum circuit implementation. For all these technologies, in order to suppress the inherent computation noise, fault-tolerance is a desirable feature. Fault tolerance is achieved by Quantum error correction codes, such as surface code. Due to the efficient realization of surface codes using Clifford + T gate library of Quantum logic gates, it is now becoming de facto gate library for Quantum circuit implementation. In this paper, we improve two key performance metrics, T − depth and T − count, for Quantum circuit realization using Clifford + T gates. In contrast with the previous approaches, we have incorporated two techniques - 1) restructuring of the gate positions in the designs to make it amenable towards a lower T − depth 2) using Binary Decision Diagrams (BDD) as an intermediate representation for achieving scalability. To validate our proposed optimizations, we have tested a wide spectrum of benchmarks, registering an average improvement of 74% and 21% on T − depth and T − count in compared works.