Optimizing Quantum Circuits for Modular Exponentiation
Title | Optimizing Quantum Circuits for Modular Exponentiation |
Publication Type | Conference Paper |
Year of Publication | 2019 |
Authors | Das, Rakesh, Chattopadhyay, Anupam, Rahaman, Hafizur |
Conference Name | 2019 32nd International Conference on VLSI Design and 2019 18th International Conference on Embedded Systems (VLSID) |
Keywords | Computer architecture, error correction, exponentiation, exponentiation functions, hardware description languages, Hardware design languages, Linear Nearest Neighbor (LNN), linear nearest neighbor property, logic circuits, logic design, logic designs, Logic gates, modular exponentiation, modular exponentiation functions, pubcrawl, Quantum Algorithm(QA), quantum architectures, quantum circuits, quantum computers, quantum computing, Quantum Error Correcting Codes (QECC), Quantum error correction, Qubit, Resiliency, reversible modular exponentiation function, Scalability, scalable synthesis methods, Tools, verilog implementation |
Abstract | Today's rapid progress in the physical implementation of quantum computers demands scalable synthesis methods to map practical logic designs to quantum architectures. There exist many quantum algorithms which use classical functions with superposition of states. Motivated by recent trends, in this paper, we show the design of quantum circuit to perform modular exponentiation functions using two different approaches. In the design phase, first we generate quantum circuit from a verilog implementation of exponentiation functions using synthesis tools and then apply two different Quantum Error Correction techniques. Finally the circuit is further optimized using the Linear Nearest Neighbor (LNN) Property. We demonstrate the effectiveness of our approach by generating a set of networks for the reversible modular exponentiation function for a set of input values. At the end of the work, we have summarized the obtained results, where a cost analysis over our developed approaches has been made. Experimental results show that depending on the choice of different QECC methods the performance figures can vary by up to 11%, 10%, 8% in T-count, number of qubits, number of gates respectively. |
DOI | 10.1109/VLSID.2019.00088 |
Citation Key | das_optimizing_2019 |
- pubcrawl
- verilog implementation
- tools
- scalable synthesis methods
- Scalability
- reversible modular exponentiation function
- Resiliency
- Qubit
- Quantum error correction
- Quantum Error Correcting Codes (QECC)
- quantum computing
- quantum computers
- quantum circuits
- quantum architectures
- Quantum Algorithm(QA)
- computer architecture
- modular exponentiation functions
- modular exponentiation
- Logic gates
- logic designs
- logic design
- logic circuits
- linear nearest neighbor property
- Linear Nearest Neighbor (LNN)
- Hardware design languages
- hardware description languages
- exponentiation functions
- exponentiation
- error correction