講題：後量子密碼標準制定

隨著網路普及，資訊安全的重要性與日俱增。對通訊與儲存保密、進行身分認證，均高度仰賴加解密、數位簽章等密碼演算法。量子電腦的研發持續進展，未來大規模 (large-scale) 通用型 (universal) 量子電腦將可破解當今所有公鑰密碼系統 (public-key cryptosystems)。為抵抗量子計算帶來的威脅，後量子密碼 (PQC, post-quantum cryptography) 應運而生，學術研究已有數十年。後量子密碼以數學演算法為基礎，可使用現有電腦與網路架構，不需額外基礎建設。美國政府正在制定 PQC 後量子密碼國家標準，今年七月已選出四項演算法，未來將成為後量子密碼國際標準，全世界通用。市場上的後量子密碼產品，隨著演算法標準定案，亦如雨後春筍般問世。

講題：量子運算於5G網路最佳化應用實例分享

5G網路具備大頻寬、低延遲、廣連結特性，適合用來布建各種AIOT應用。因應5G AIOT網路多變化的環境以及在網路密度與複雜度大增下，如何有效控制5G網路設置參數讓5G網路效能最佳提供最佳的網路服務是非常有挑戰性的任務。目前有研究使用Machine Learning來嘗試找出可以兼顧基站負載平衡的方法(ML-based Mobility Load Balancing)，但是機器學習的方法在實際運行時對於不在訓練資料集的網路環境變化很難保證效果。量子運算運用在解決最佳化問題可以在很短的時間內算出最佳解，因此仁寶與資策會暨台大合作研究用富士通數位退火雲計算服務來嘗試更有效益地解決5G網路最佳化實務問題以及我們如何將實務問題轉化為QUBO formulation並分享我們在使用富士通數位退火雲計算實驗收斂的過程與成果。

講題：量子電腦的產業生態與應用趨勢

量子電腦（Quantum Computer）是一種應用實現量子力學（Quantum Mechanics）來實現運算效能飛躍的技術。2015、2016年由D-Wave、IBM陸續發布商業應用的產品之後，2019年之後，全球主要雲端服務提供商如AWS、Microsoft Azure、Google等也陸續提出量子電腦的解決方案。若以全球為構面來看，全球專利技術所擁有的前幾大企業之中，多由IBM、Google、Intel、Microsoft、D-Wave（加拿大）、Rigetti（總部於美國柏克萊）所佔據，不過若單純察看不同區域的專利數量，則由亞洲為最多，全球量子電腦近10年新創總募資金額更達15億美元。這顯現出全球量子電腦的產業生態系急遽變化，同時包括交通運輸、生技製藥、金融投資組合、車用電池設計等，都可以看見量子電腦應用的試驗與落地。可以發現到全球量子電腦科技的競逐，除了基礎科學之外，如何推進商業應用的速度，也已成為國際競爭的關鍵。分析全球量子電腦的產業生態系，以及應用發展，有助於台灣以前瞻的視角，來思考產業布局與策略方向。

講題：

Quantum Computation for Drug Discovery Applications: Preferred Tautomeric State Prediction

Prediction of tautomers plays an essential role in computer-aided drug discovery. However, it remains a challenging task nowadays to accurately predict the canonical tautomeric form of a given drug-like molecule. Lack of extensive tautomer databases, most likely due to the difficulty in experimental studies, hampers the development of effective empirical methods for tautomer predictions. A more accurate estimation of the stable tautomeric form can be achieved by quantum chemistry calculations. Yet, the computational cost required prevents quantum chemistry calculation as a standard tool for tautomer prediction in computer-aided drug discovery. In this paper we propose a hybrid quantum chemistry-quantum computation workflow to efficiently predict the dominant tautomeric form. Specifically, we select active-space molecular orbitals based on quantum chemistry methods. Then we utilize efficient encoding methods to map the Hamiltonian onto quantum devices to reduce the qubit resources and circuit depth. Finally, variational quantum eigensolver (VQE) algorithms are employed for ground state estimation where hardware-efficient ansatz circuits are used. To demonstrate the applicability of our methodology, we perform experiments on two tautomeric systems: acetone and Edaravone, each having 52 and 150 spin-orbitals in the STO-3G basis set, respectively. Our numerical results show that their tautomeric state prediction agrees with the CCSD benchmarks. Moreover, the required quantum resources are efficient: in the example of Edaravone, we could achieve chemical accuracy with only eight qubits and 80 two-qubit gates.

講題：

Predicting dissociation energy of phenol derivatives based on the group contribution method on QUBO solvers

In the chemical process, undesired polymerization reactions of monomers during separation, storage, and transportation sometimes cause severe problems. The control of these undesired polymerization reactions becomes essential. Besides tuning the process condition parameters, we can achieve the effect by adding different additives like polymeric inhibitors. Among these factors, different additives play an essential role in the petrochemical industry, especially in chemical manufacturing. Because of a large number of possible combinations, selecting suitable additives has become an intractable problem. In this paper, we apply a strategy named enhanced group contribution method (EGCM) based on the group contribution method (GCM) to model the problem into quadratic unconstrained binary optimization (QUBO) forms. We consider the case of screening additives structure as proof-of-concept work. Our results show that the QUBO formulation from EGCM achieves strong ranking correlations with the results obtained directly from DFT calculations. Based on this formulation, one could use the calculation results from QUBO solvers to screen the appropriate additives in chemical manufacturing.

講題：量子電腦之低溫關鍵模組技術分享

近年量子科技的運用中，量子電腦因較古典電腦具強大的運算能力及重量級應用，受到全世界各國與高科技廠商極大的關注與投資，學術界與產業界更掀起研發熱潮。對量子電腦發展，量子位元控制讀取電路是關鍵因素，並大量仰賴半導體製程與電路設計相關的尖端科技。藉由經濟部技術處運用科技專案計畫推動關鍵產業技術，結合臺灣半導體產業優勢，發展控制與讀取量子位元的低溫微波電路關鍵模組，提供「量子電腦」面臨擴充性困難時的解決方案。在量子電腦產業初期投入資源進行突破性的研究與開發，相信助於臺灣在第二次量子革命的浪潮中，站穩關鍵位置，持續樹立半導體產業的領先地位。

講題：中原大學智慧運算與量子教育現況與展望

隨著大數據資料流的應用盛行，智慧運算技術的需求孔急，除了鴻海集團設立鴻海研究院以作為「鴻海大腦」、發展AI與量子運算技術外；行政院2018年起以每年約100億的預算，推動「台灣AI行動計畫」，2022年起也預計以五年80億的目標，成立「量子國家隊」，厚植本土科研在量子電腦硬體、光量子技術、量子軟體技術與應用開發的實力。中原大學於2022成立「智慧運算與量子資訊學院」，第一年招收大學部80名與碩士班16名新生，除專注人工智慧、大數據分析技術與量子計算專業技術的訓練外，更培育學生兼顧系統整合之全方位能力，讓學生可以學習先進技能，並應用所學於解決實際問題。另外，也成立校級的「量子資訊中心」，攜手產業界跨域研究，以增進人類福祉為目標。

講題：漫談臺灣量子科技立法

從量子力學到量子科技，經過許多大師開墾，許多時間摸索嘗試，在本世紀逐漸受到政策面的注目，美國、歐盟、英國陸續有政策引導與立法例，亞洲的中國、日、韓、新加坡也都納為科技政策要項，因為他們深知量子科技不但影響經濟發展，也攸關國家安全問題。我國有先進良好的電子產業基礎，有優秀的研究與技術人才，面對量子科技的新興發展，實不應落後於國際，若能從政策上加以扶持，對於技術研發以及未來的產業應用，都會有正面積極意義。從立法而言，有四大面向的規劃是至關緊要，分別是：設立專責單位負責統籌業務，國家編列預算投入、提供企業租稅獎勵以及培育專業人才。因此在立法旨趣上，先以基本法的型態規範主要架構，賦予主管機關預算規劃、跨部會協調、計畫草擬、監督考核等功能，而相關組織、預算規模、獎勵辦法、教育培訓方法則可依照國情與國際趨向，逐一立法或確立施行要點。

講題：

Quantum and Brain Work Together for Quantum AI

Humans, or more generally, living systems differ from physical objects in two main ways: the former have both heredity and intelligence, while the latter do not. Understanding the functions and mechanisms of brain intelligence is one of the most challenging problems in the natural sciences. Physicist Freeman Dyson pointed out long time ago: Mind, manifested as the ability to make choices, is to some extent inherent to every electron. In this talk, I will discuss the quantum description of the nervous system. We study quantum mechanically the neurotransmission in brain, to understand the functions of memory, cognition and learning during neurotransmissions. From the physiological point of view, currently it is known that nerve signals are transmitted through ion channels crossing cell membranes along the axon connecting neurons, and are operated under action potentials generated from neuronal stimulations. The length of ion channels is the order of several nm, the action potential pulses generated in neuron stimulations are the order of tens of mV, and the induced electric current is about the order of several pA. The microscopic physical mechanism of neurotransmissions seems to be very similar to the physical scale and physical process of quantum transport through quantum dots in nanoelectronics. We have thereby established a quantum transport theory for neurotransmission and will eventually develop a quantum neural network for quantum AI. We are comparing our theoretical results with experimental data.