TPCEE 2022
Highlights in Science, Engineering and Technology © 2022 TPCEE
Comparisons of Conventional Computing and Quantum Computing Approaches.pdfThis paper compares classical (conventional) and quantum computing approaches, emphasizing quantum computers' potential in solving problems that classical machines struggle with—especially in cryptography and molecular simulations.
The author discusses quantum algorithms like Shor’s Algorithm and simulation techniques based on quantum dynamics, while also acknowledging current limitations in hardware and commercialization.
This review summarizes the core arguments of the paper and highlights the implications for scientific computing and cybersecurity in the quantum era.
Classical computers excel at many tasks but fail in scenarios involving exponential complexity—e.g., simulating quantum systems or factoring large numbers.
🔐 Cryptography:
Modern encryption (like RSA) relies on classical computers' inability to factor large numbers efficiently.
🧪 Molecular Simulation:
Solving the Schrödinger equation for multi-particle systems is computationally intense for classical machines.
📌 Core Question:
How do quantum computers offer practical advantages over classical computers in these domains?
✅ Introduces quantum computing concepts based on superposition and entanglement
✅ Compares classical and quantum strategies in cryptography and molecular simulations
✅ Describes Shor’s Algorithm as a landmark quantum solution to RSA encryption
✅ Presents quantum algorithms for simulating molecules efficiently
3️⃣ Quantum Computing Fundamentals
📌 Superposition:
A quantum bit (qubit) can exist in both 0 and 1 simultaneously. This allows massive parallelism.
📌 Entanglement:
Measurement of one qubit affects another, regardless of distance—a property used to coordinate quantum states.
4️⃣ Shor’s Algorithm & Cryptography
🔑 RSA Security:
Built on the difficulty of factoring large numbers. Classical computers take exponential time.
🧠 Shor’s Algorithm (1994):
Reduces factoring to period finding, solvable efficiently by quantum Fourier transform (QFT).
Only Step 3 in the procedure requires quantum resources; the rest can be done classically.
💥 Result:
Quantum computers can break RSA encryption, demonstrating quantum supremacy in cryptography.
5️⃣ Molecular Simulation & Quantum Algorithms
🔬 Challenge:
Classical computers struggle to solve many-body Schrödinger equations.
⚛️ Quantum Advantage:
Quantum computers require exponentially fewer resources to simulate molecules.
📌 Examples:
qEOM (quantum Equation of Motion) for excited states
Product Formula (Lie–Trotter–Suzuki) to simulate Hamiltonians
Variational Quantum Algorithms (VQA) combining classical and quantum methods
✅ These allow more accurate modeling of quantum dynamics and electron structures.
6️⃣ Limitations & Challenges
📉 Current Limitations:
Quantum computers aren’t yet commercial-scale
Qubits are sensitive to noise; require near-absolute-zero environments
Difficult to isolate and extract clean outputs from noisy quantum states
Even top-tier machines (e.g., IBM’s 127-qubit Eagle) can't yet solve large-scale real-world problems like RSA-2048
7️⃣ Future Prospects
📈 Emerging Opportunities:
Quantum-assisted optimization in AI, finance, healthcare
Near-term commercial applications in quantum sampling and simulation
Continued development of hybrid classical-quantum algorithms to extend reach
🛡️ Cybersecurity Shift:
Security professionals must prepare for quantum threats ahead of hardware maturity to avoid major risks.
8️⃣ Conclusion
📌 Key Takeaways: 1️⃣ Quantum computers outperform classical ones in specific, complex tasks
2️⃣ Shor’s algorithm breaks traditional encryption schemes
3️⃣ Quantum methods enhance molecular simulations
4️⃣ Commercialization is on the horizon but not yet here
📌 Final Thoughts:
✅ Quantum computing is not a universal replacement but a complementary powerhouse for solving specialized problems.
✅ Investment and research today will define the computational landscape of tomorrow.
학부연구생_4월_11일 발표.pdf