Quantum Cost Vector Analysis

A. Kamaraj, P. Marichamy, S. Karthika Devi +1 more

Background/Objectives: The objective is to reduce the Quantum Cost (QC), Garbage Output (GO) and Gate Counts in the design of the adder by incorporating the Novel Reversible Gates. Methods/Statistical Analysis: In recent day’s energy dissipation is the major complex problem in circuit designing in order to recede this problem the reversible logic is employed, where the input vectors can be derived from the output vectors and from the output vectors the input vectors can be procured. Using this technique the Quantum Cost, Garbage Outputs and Gate Counts of the design is reduced. Findings: Based on the technique of reversible logic a novel reversible Gates is proposed from that the novel reversible adders have been designed. The novel reversible gates satisfy the property of reversibility and universality which shows the uniqueness of the proposed gates. The comparative analysis have been made with the existing work, the proposed work shows a significant improvement in the design optimization parameters. Application/Improvements: The proposed structures can be employed in the ALU design and in Floating Point Multiplier design. The comparative analysis shows that Number of Gates, Garbage Outputs and Quantum Cost is minimum for the proposed gate based structure rather than conventional one Keywords: Garbage Outputs (GO), QCA, Quantum Cost (QC), Reversibility, Reversible Logic, Universality.

Jesse Van Griensven Thé, Roydon Andrew Fraser, José R. Rosas-Bustos

Hudson Leone, Nathan Miller, Deepesh Singh +2 more

Entanglement distribution will form the backbone of many future distributed quantum technologies, especially the quantum internet. The act of purifying multiple noisy entangled states into a single one of higher quality has no analogue in classical networking and as such, this transforms the way in which we will consider future algorithms for routing entanglement. We outline the differences that arise because of this, demonstrate some elementary formalisms for `multi-path entanglement routing', and discuss the philosophical differences that arise when comparing this regime to conventional digital network theory. We also present a software package, QuNet, that uses novel `quantum cost-vector analysis' to simulate and benchmark routing in multi-user entanglement networks in a way that is is highly scalable in network size and the number of competing users. Our software accommodates both ground- and space-based networks, and implements efficient multi-user time-optimisation for mitigating congestion when quantum memories are available.

Tad Hogg, Dmitriy Portnov

Tomoyuki Yamakami

Seong-Min Cho, Changyeol Lee, Seung-Hyun Seo

The security of lattice-based cryptography relies on the computational complexity of solving the Shortest Vector Problem (SVP) on a high-dimensional lattice. Due to its efficacy in addressing SVP, lattice-based cryptographic systems have so far used the sieve algorithm to analyze their security. Previous works have analyzed the theoretical complexity improvement of the sieve algorithm in quantum computing environments, noting that Grover’s algorithm provides a quadratic speed-up for search problems. However, these works have solely focused on the theoretical analysis of query complexity, neglecting to present quantum circuit designs for sieves. Quantum circuit design and quantum resource estimation are necessary for practical analysis of the complexity of quantum sieves. Additionally, the cost of quantum error correction must also be considered, as quantum computation has a large number of errors. In this paper, we present quantum circuit designs for the sieve algorithm and provide estimates of the quantum resources required, including the number of gates and their depth. Furthermore, we evaluate the quantum sieve’s impact on the security level of ML-KEM and ML-DSA, comparing it to the classical sieve algorithm. We do this by evaluating the classical processing cost for quantum error correction using these estimates. Our results show that the quantum sieve algorithm does not break ML-KEM and ML-DSA, but it reduces their security level by 15 to 27 bits compared to the classical sieve.