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Quantum Error Correction Fault Tolerance
Resource Optimized Quantum Squaring Circuit
arXiv
Authors: Afrin Sultana, Edgard Muñoz-Coreas
Year
2024
Paper ID
66961
Status
Preprint
Abstract Read
~2 min
Abstract Words
221
Citations
N/A
Abstract
Quantum squaring operation is a useful building block in implementing quantum algorithms such as linear regression, regularized least squares algorithm, order-finding algorithm, quantum search algorithm, Newton Raphson division, Euclidean distance calculation, cryptography, and in finding roots and reciprocals. Quantum circuits could be made fault-tolerant by using error correcting codes and fault-tolerant quantum gates (such as the Clifford + T-gates). However, the T-gate is very costly to implement. Two qubit gates (such as the CNOT-gate) are more prone to noise errors than single qubit gates. Consequently, in order to realize reliable quantum algorithms, the quantum circuits should have a low T-count and CNOT-count. In this paper, we present a novel quantum integer squaring architecture optimized for T-count, CNOT-count, T-depth, CNOT-depth, and KQT that produces no garbage outputs. To reduce costs, we use a novel approach for arranging the generated partial products that allows us to reduce the number of adders by 50%. We also use the resource efficient logical-AND gate and uncomputation gate shown in [1] to further save resources. The proposed quantum squaring circuit sees an asymptotic reduction of 66.67% in T-count, 50% in T-depth, 29.41% in CNOT-count, 42.86% in CNOT-depth, and 25% in KQ T with respect to Thapliyal et al. [2]. With respect to Nagamani et al. [3] the design sees an asymptotic reduction of 77.27% in T-count, 68.75% in T-depth, 50% in CNOT-count, 61.90% in CNOT-depth, and 6.25% in the KQT.
Why This Paper Matters
- This paper contributes to the Quantum Error Correction & Fault Tolerance research area in the Quantum Articles archive.
- It adds a 2024 reference point for readers tracking recent quantum research.
- Quantum squaring operation is a useful building block in implementing quantum algorithms such as linear regression, regularized least squares algorithm, order-finding...
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