Merge pull request #435 from rawkintrevo/2177

MAHOUT-2177 Add `papers` feed

website/_config.yml

@@ -53,3 +53,8 @@ exclude:
 #   - vendor/cache/
 #   - vendor/gems/
 #   - vendor/ruby/
+
+collections:
+  papers:
+    output: true
+

website/_includes/navbar.html

@@ -39,6 +39,8 @@
                        aria-haspopup="true"
                        aria-expanded="false">Documentation</a>
                     <div class="dropdown-menu" aria-labelledby="navbarDropdownMenuLink">
+                        <a class="dropdown-item"
+                           href="/papers">Papers</a>
                         <a class="dropdown-item"
                            href="/documentation/users">User Guide</a>
                         <a class="dropdown-item"

website/_papers/An-Efficient-Quantum-Factoring-Algorithm.md

@@ -0,0 +1,37 @@
+---
+layout: post
+title: "Summary of 'An Efficient Quantum Factoring Algorithm'"
+date: 2024-03-04
+---
+
+Author: Oded Regev
+
+[Original Paper](https://arxiv.org/abs/2308.06572)
+
+The paper presents an efficient quantum factoring algorithm that can be used to 
+factorize n-bit integers. The algorithm involves running a quantum circuit with 
+˜O(n3/2) gates for √n + 4 times, and then using a polynomial-time classical 
+post-processing step. The correctness of the algorithm is based on a 
+number-theoretic assumption similar to those used in subexponential classical 
+factorization algorithms. The author demonstrates that quantum circuits of size 
+˜O(n3/2) are sufficient for factoring integers, which is an improvement over 
+previous algorithms that required larger circuit sizes. The number of qubits in 
+the quantum circuit is O(n3/2), which is higher than the qubit requirement in 
+optimized implementations of Shor's algorithm. However, the depth of the quantum
+circuit is smaller than Shor's algorithm, making it more feasible for 
+implementation. The paper also discusses the potential implications of the 
+algorithm in practice. It is highlighted that the analysis is asymptotic and the
+algorithm may not be efficient for small values of n. The algorithm may benefit 
+from optimizations in fast integer multiplication and the use of smaller qubit 
+counts, similar to optimizations used in Shor's algorithm. However, it is 
+currently unclear if these optimizations can be applied to the proposed 
+algorithm. The author concludes by stating that the algorithm provides an 
+improvement over Shor's algorithm in terms of circuit size. However, it remains 
+to be seen if the algorithm can be practically implemented and if it can provide
+an improvement over Shor's algorithm for small values of n. The analysis in the 
+paper is based on asymptotics, and it is unclear if hidden constants in the 
+algorithm would make it inefficient for small values of n. In summary, the paper
+presents an efficient quantum factoring algorithm that uses a quantum circuit 
+with ˜O(n3/2) gates and a classical post-processing step. The algorithm provides
+an improvement over previous algorithms in terms of circuit size, but its 
+practicality and potential improvements for small values of n remain to be seen.

website/_papers/Unleashing-the-Potential-of-LLMs-for-Quantum-Computing.md

@@ -0,0 +1,49 @@
+---
+layout: post
+title: "Summary of 'Unleashing the Potential of LLMs for Quantum Computing: A Study in Quantum Architecture Design'"
+date: 2024-03-04
+---
+
+Author: Zhiding Liang, Jinglei Cheng, Rui Yang, Hang Ren, Zhixin Song, Di Wu, 
+Xuehai Qian, Tongyang Li, Yiyu Shi
+
+[Original Paper](https://arxiv.org/abs/2307.08191)
+
+This paper discusses the potential of large language models (LLMs), specifically
+generative pretrained transformers (GPTs), in the field of quantum computing. 
+The authors propose a Quantum GPT-Guided Architecture Search (QGAS) model that 
+utilizes GPT-4 to recommend high-quality ansatz architectures for variational 
+quantum algorithms (VQAs). The ansatz architecture is a crucial component of 
+quantum computing and determines the efficiency and accuracy of quantum 
+algorithms. The authors conduct experiments using a series of application 
+benchmarks, including portfolio optimization, the MaxCut problem, the Traveling 
+Salesman Problem (TSP), and the estimation of molecule ground state energy for 
+Lithium Hydride (LiH) and Water (H2O). They compare the performance of the 
+ansatz architectures generated by QGAS with existing ansatzes and 
+state-of-the-art ansatz architecture search methods. The results show that QGAS 
+outperforms other ansatz architectures in some benchmark applications, 
+demonstrating the potential of LLMs in quantum architecture design. The authors 
+highlight the importance of human feedback in guiding the performance of GPT-4. 
+Human experts provide specific guidance and feedback to improve the search 
+strategies and evaluate the generated ansatz architectures. The iterative 
+feedback loop between human experts and GPT-4 leads to better performance and 
+optimization of the quantum circuits. The paper also discusses the limitations 
+of GPT in the field of quantum computing. GPT is not a general artificial 
+intelligence and cannot think dynamically about quantum physics or make accurate
+predictions about scientific phenomena in quantum experiments. It also relies on
+large-scale data models, which may contain biased or misleading information 
+about quantum computing. The authors suggest future directions for the 
+integration of LLMs, such as GPT, in quantum computing. They propose that GPT 
+can be used to design and optimize fault-tolerant quantum algorithms and assist 
+in the calibration of quantum hardware. They also envision GPT playing a role in
+the simulation of quantum computers and providing agile validation of 
+algorithmic innovations. In conclusion, this paper highlights the potential of 
+LLMs, specifically GPT, in the field of quantum computing. The QGAS model 
+demonstrates the effectiveness of using GPT-4 to generate high-performance 
+ansatz architectures for quantum algorithms. The integration of human feedback 
+and the power of GPT-4 provides a promising avenue for advancing quantum 
+architecture design and optimization. However, the limitations of GPT and the 
+challenges of applying LLMs to quantum computing should be considered. The 
+authors suggest further research and development to leverage the capabilities of
+GPT and address the limitations to fully harness the potential of LLMs in 
+quantum computing.

website/papers.md

@@ -0,0 +1,10 @@
+---
+layout: page
+title: Papers
+---
+
+# Papers
+
+{% for paper in site.papers %}
+- [{{ paper.title }}]({{ paper.url }})
+{% endfor %}