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Quantum Computing

Harnessing the strange laws of quantum mechanics -- superposition, entanglement, and interference -- to solve problems beyond the reach of any classical...

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Harnessing the strange laws of quantum mechanics -- superposition, entanglement, and interference -- to solve problems beyond the reach of any classical computer. Key sections include: Quantum Computing; Classical vs. Quantum; Historical Timeline; Qubit Technologies; Quantum Algorithms; The Error Correction Challenge; Applications: Drug Discovery and Chemistry; Applications: Optimization and Finance; Quantum Cryptography; The Quantum Computing Industry.

Key sections

  • 01Quantum Computing
  • 02Classical vs. Quantum
  • 03Historical Timeline
  • 04Qubit Technologies
  • 05Quantum Algorithms
  • 06The Error Correction Challenge
  • 07Applications: Drug Discovery and Chemistry
  • 08Applications: Optimization and Finance
  • 09Quantum Cryptography
  • 10The Quantum Computing Industry
  • 11Quantum Machine Learning
  • 12Quantum Internet and Communication
  • 13Timeline: When Will Quantum Computers Matter?
  • 14Key Takeaways

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Slide outline
  1. 01Quantum Computing
  2. 02Classical vs. Quantum
  3. 03Historical Timeline
  4. 04Qubit Technologies
  5. 05Quantum Algorithms
  6. 06The Error Correction Challenge
  7. 07Applications: Drug Discovery and Chemistry
  8. 08Applications: Optimization and Finance
  9. 09Quantum Cryptography
  10. 10The Quantum Computing Industry
  11. 11Quantum Machine Learning
  12. 12Quantum Internet and Communication
  13. 13Timeline: When Will Quantum Computers Matter?
  14. 14Key Takeaways
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Slide 01

Quantum Computing

  • Technology • Quantum
  • Harnessing the strange laws of quantum mechanics -- superposition, entanglement, and interference -- to solve problems beyond the reach of any classical computer.
  • QubitsSuperpositionEntanglementAlgorithmsCryptography
Slide 02

Classical vs. Quantum

  • Classical Computing
  • Bits: Binary states: 0 or 1. Deterministic. A 64-bit register stores one of 2^64 possible values at any time. Process information sequentially (or with limited parallelism).
  • Quantum Computing
  • Qubits: Exist in superposition of 0 and 1 simultaneously (alpha|0> + beta|1>). N qubits represent 2^N states simultaneously. 300 qubits can represent more states than atoms in the observable universe.
  • Key Quantum Properties
  • Superposition: Qubit exists in multiple states until measured. Enables parallel exploration of solution spaces.
  • Entanglement: Qubits become correlated -- measuring one instantly determines the other regardless of distance. "Spooky action at a distance" (Einstein).
  • Interference: Quantum states can amplify correct answers and cancel wrong ones. The key to quantum speedup.
  • No-cloning theorem: Cannot copy unknown quantum state. Basis for quantum cryptography security.
  • "If you think you understand quantum mechanics, you don't understand quantum mechanics."-- Richard Feynman
Slide 03

Historical Timeline

  • 1981
  • Feynman proposes quantum computers to simulate quantum physics: "Nature isn't classical, dammit"
  • 1985
  • David Deutsch describes universal quantum computer (quantum Turing machine)
  • 1994
  • Peter Shor develops algorithm for factoring large numbers exponentially faster -- threatens RSA encryption
  • 1996
  • Lov Grover develops search algorithm with quadratic speedup
  • 1998
  • First 2-qubit quantum computer built (IBM/Oxford)
  • 2001
  • IBM factors 15 using Shor's algorithm on 7-qubit NMR system
  • 2011
  • D-Wave sells first "quantum computer" (quantum annealer, 128 qubits). Controversy over whether it's truly quantum.
  • 2019
  • Google "quantum supremacy": Sycamore (53 qubits) solves problem in 200 seconds vs. estimated 10,000 years classical
  • 2022
  • IBM unveils 433-qubit Osprey processor. Error rates still limit utility.
  • 2023
  • IBM 1,121-qubit Condor. Harvard/QuEra create 48 logical qubits with error correction.
  • 2024
  • Google Willow: 105 physical qubits, below error-correction threshold. Microsoft announces topological qubit progress.
  • 2025
  • Race toward fault-tolerant quantum computing intensifies. Hybrid quantum-classical approaches dominate near-term.
Slide 04

Qubit Technologies

  • Superconducting
  • Josephson junctions cooled to 15 millikelvin (colder than outer space). Used by IBM, Google, Rigetti. Fast gate times (ns). Short coherence (~100 microseconds). Most mature approach. Requires massive dilution refrigerators.
  • Trapped Ions
  • Individual atoms held in electromagnetic traps, manipulated with lasers. IonQ, Quantinuum (Honeywell). Long coherence times (minutes). High gate fidelity (99.9%+). Slower gates. Full connectivity between qubits.
  • Photonic
  • Qubits encoded in photons. Xanadu, PsiQuantum. Room temperature operation. Natural for networking. Measurement-based computation. Photon loss is main challenge. PsiQuantum targeting 1M+ qubits.
  • Neutral Atoms
  • Arrays of individual atoms held by optical tweezers. QuEra, Pasqal, Atom Computing. Scalable (1000+ qubits demonstrated). Reconfigurable connectivity. Rapidly advancing.
  • Topological
  • Microsoft's approach. Qubits encoded in exotic quasiparticles (Majorana fermions). Inherently error-protected by topology. Theoretically superior but unproven experimentally. "Holy grail" approach.
  • Silicon Spin
  • Electron or nuclear spins in silicon quantum dots. Intel, UNSW. Leverages existing semiconductor fabrication. Small qubits enable density. Compatibility with classical chip manufacturing.
Slide 05

Quantum Algorithms

  • Shor's Algorithm (1994)
  • Purpose: Factor large numbers in polynomial time. Classical best: sub-exponential. A 4,096-bit RSA key requires ~20M physical qubits to break. Threatens all public-key cryptography.
  • Grover's Algorithm (1996)
  • Purpose: Search unsorted database of N items in sqrt(N) time vs. N classically. Quadratic speedup. Implications for symmetric encryption (AES-256 effectively becomes AES-128).
  • VQE/QAOA
  • Variational methods: Hybrid quantum-classical algorithms for near-term devices. Quantum circuit parameterized, classical optimizer adjusts parameters. Used for chemistry and optimization problems.
  • Quantum Speedup Classes
  • Exponential: Factoring (Shor), certain simulations. Transformative -- problems become tractable that were impossible.
  • Polynomial: Grover search (quadratic). Useful but not transformative alone.
  • No speedup: Most everyday computing. Quantum computers will NOT replace classical for general tasks.
  • BQP Complexity Class
  • Bounded-error Quantum Polynomial time: Problems solvable efficiently by quantum computer. Contains P and BPP. Relationship to NP unknown -- quantum computers likely cannot solve all NP problems (but may solve some).
  • "Quantum computers will be like a flashlight -- brilliant for illuminating dark spaces, but you wouldn't use one to read a book."-- Scott Aaronson, UT Austin quantum complexity theorist
Slide 06

The Error Correction Challenge

  • Today's qubits are "noisy" -- they lose quantum information (decohere) and accumulate errors rapidly. Quantum error correction is the critical unsolved engineering challenge.
  • The Problem
  • Error rates: Current physical qubit error: ~0.1-1% per operation. Useful computation requires error rates below ~10^-10. Gap: 8 orders of magnitude. Must be bridged by error correction.
  • Decoherence: Quantum information degrades through interaction with environment. Coherence times: microseconds (superconducting) to minutes (trapped ions). Every operation must complete before decoherence.
  • Error Correction
  • Logical qubits: Encode one logical qubit across many physical qubits. Surface code: ~1,000-10,000 physical qubits per logical qubit. A useful quantum computer may need millions of physical qubits.
  • 2023 breakthrough: Harvard/QuEra demonstrated 48 logical qubits with below-threshold error correction. Google Willow (2024) showed errors decrease as system scales -- first time crossing threshold.
  • NISQ era: Noisy Intermediate-Scale Quantum -- current machines (50-1000 noisy qubits). Limited utility. "Quantum advantage" demonstrated but not yet "quantum utility" for practical problems.
Slide 07

Applications: Drug Discovery and Chemistry

  • Simulating molecular systems is exponentially hard for classical computers but natural for quantum ones. Feynman's original motivation: simulate physics with physics.
  • Why Chemistry?
  • Electron correlation: Exact simulation of molecule with N electrons scales as 2^N classically. Caffeine (24 electrons) already pushes classical limits for exact methods. Quantum: scales polynomially.
  • Applications: Catalyst design, battery materials, drug binding simulations, nitrogen fixation (replacing Haber-Bosch), room-temperature superconductors, carbon capture materials.
  • Current Progress
  • Largest molecule simulated exactly: ~20 qubits (small molecules like H2, LiH, BeH2)
  • Useful pharmaceutical simulations need ~1M physical qubits (2035-2040?)
  • Near-term: hybrid methods (VQE) for approximate solutions
  • Companies: Qu&Co, QSimulate, Rahko (acquired), ProteinQure
  • "Nature isn't classical, dammit, and if you want to make a simulation of nature, you'd better make it quantum mechanical."-- Richard Feynman, 1981 (proposing quantum computing)
Slide 08

Applications: Optimization and Finance

  • Optimization Problems
  • Combinatorial optimization: Routing, scheduling, portfolio optimization, logistics. NP-hard problems where finding optimal solution among exponential possibilities. QAOA and quantum annealing approaches.
  • Examples: Traveling salesman, vehicle routing (UPS saves $50M/year per minute of optimized routes), airline scheduling, supply chain optimization, protein folding (combinatorial structure search).
  • Finance
  • Portfolio optimization: Finding optimal asset allocation considering correlations, constraints, and risk. Markowitz model with 1,000 assets: intractable classically. Goldman Sachs, JPMorgan investing heavily.
  • Monte Carlo simulation: Pricing derivatives, risk assessment. Quantum amplitude estimation offers quadratic speedup. Could accelerate from overnight to real-time.
  • Fraud detection: Pattern recognition in high-dimensional transaction data. Quantum machine learning for anomaly detection. Still theoretical advantage.
Slide 09

Quantum Cryptography

  • The Threat
  • "Harvest now, decrypt later": Nation-states collecting encrypted data today to decrypt when quantum computers mature. NIST estimates: RSA-2048 broken by ~2030-2035. All historical encrypted communications at risk.
  • Post-Quantum Cryptography
  • NIST Standards (2024): CRYSTALS-Kyber (key encapsulation), CRYSTALS-Dilithium (digital signatures), SPHINCS+ (hash-based signatures). Based on lattice problems and hash functions. Migration: "Y2Q" moment approaching.
  • Quantum Key Distribution (QKD)
  • BB84 Protocol (1984): Bennett & Brassard. Uses quantum properties: measuring a qubit disturbs it. Eavesdropper detected by increased error rate. Provably secure by laws of physics (not computational assumptions).
  • China's QUESS satellite (2017): QKD over 1,200 km
  • China's 2,000 km Beijing-Shanghai quantum network operational
  • Limitations: distance (fiber loss), rate, infrastructure cost
  • Commercial: ID Quantique, Toshiba QKD systems deployed
  • "The quantum computer is a threat we must prepare for now, not when it arrives."-- NIST, announcing post-quantum cryptography standards, 2024
Slide 10

The Quantum Computing Industry

  • Major Players
  • IBM: Superconducting qubits. 1,121-qubit Condor (2023). Qiskit open-source framework. Quantum Network (200+ partners). Roadmap: 100K qubits by 2033.
  • Google: Sycamore (quantum supremacy, 2019). Willow (2024): below error-correction threshold. Cirq framework. Targeting "useful quantum computation" by 2029.
  • Microsoft: Topological qubits (long-term bet). Azure Quantum cloud platform. Strong in software stack (Q#). Partnership with Quantinuum.
  • Startups: IonQ (trapped ions, public), Rigetti (superconducting, public), PsiQuantum (photonic, $700M raised), QuEra (neutral atoms), Xanadu (photonic).
  • Investment and Market
  • $35B+
  • total investment in quantum computing companies (2015-2024)
  • Government spending: US ($1.2B NQIA), China ($15B estimated), EU ($1.2B), UK ($3.5B over 10 years)
  • Market forecast: $65B by 2030 (McKinsey), $850B+ by 2040
  • Cloud access: IBM, AWS Braket, Azure Quantum, Google Quantum AI -- democratizing experimentation
  • "We are in the 'vacuum tube era' of quantum computing. The transistor moment is coming."-- Adapted from IBM quantum roadmap presentations
Slide 11

Quantum Machine Learning

  • Quantum machine learning (QML) explores whether quantum computers can speed up or improve ML tasks. Theoretical advantages exist but practical demonstration remains limited.
  • Approaches
  • Quantum kernels: Map data to quantum Hilbert space. Compute kernel functions quantum computers can evaluate efficiently. Potential for better classification of certain structured data.
  • Variational quantum circuits: Parameterized quantum circuits as ML models. Trained via classical optimization. "Quantum neural networks." Near-term compatible (NISQ).
  • Quantum sampling: Generate samples from complex distributions. Potential for generative models, Boltzmann machines. Quantum speedup for specific sampling tasks proven.
  • Skepticism and Hype
  • No proven quantum advantage for practical ML tasks yet
  • "Barren plateau" problem: gradients vanish exponentially with qubits in variational circuits
  • Classical ML already extremely efficient (GPUs, TPUs)
  • Most promising: quantum simulation of quantum systems for ML in chemistry/materials
  • Data loading bottleneck: getting classical data into quantum state is expensive
  • "The question is not whether quantum machine learning is possible, but whether it offers advantage over the best classical methods -- and for which specific problems."-- John Preskill, Caltech, who coined "NISQ" era
Slide 12

Quantum Internet and Communication

  • Vision
  • Quantum internet: Network of quantum computers connected by quantum channels. Enable distributed quantum computing, secure communication, and applications impossible classically (quantum teleportation of states).
  • Building Blocks
  • Quantum repeaters: Extend entanglement over long distances. Classical signal: amplify. Quantum: can't copy (no-cloning). Must use entanglement swapping.
  • Quantum memories: Store quantum states. Current best: minutes (trapped atoms). Need hours-days for practical networks.
  • Photonic links: Photons carry quantum information over fiber or free-space. Loss in fiber limits to ~100km without repeaters.
  • Progress
  • Netherlands: QuTech building first metro-scale quantum network (Delft-The Hague-Amsterdam). Multi-node entanglement demonstrated 2022.
  • China: 4,600 km integrated quantum communication network (satellite + fiber). Beijing-Shanghai backbone operational since 2017. QUESS satellite: intercontinental QKD.
  • US: DOE quantum internet blueprint. Chicago quantum network testbed. NASA/Caltech: teleportation over 44km of fiber (2020, 90% fidelity).
  • "A quantum internet would be to a classical internet what a quantum computer is to a classical computer -- enabling qualitatively new capabilities."-- Stephanie Wehner, QuTech, architect of quantum internet roadmap
Slide 13

Timeline: When Will Quantum Computers Matter?

  • Near-Term (2025-2030)
  • NISQ applications: quantum chemistry approximations, optimization heuristics
  • Quantum advantage for specific scientific problems
  • 1,000-10,000 physical qubit systems
  • First commercial quantum advantage (narrow applications)
  • Post-quantum cryptography migration underway
  • Medium-Term (2030-2040)
  • Error-corrected logical qubits (1,000+)
  • Drug discovery and materials science acceleration
  • Cryptographically relevant quantum computers
  • Financial optimization at scale
  • Quantum internet proof-of-concept
  • Long-Term (2040+)
  • Large-scale fault-tolerant quantum computing
  • Routine quantum simulation of complex systems
  • Quantum AI (if advantages materialize)
  • Global quantum internet
  • Applications we cannot yet imagine
  • Key uncertainty: All timelines depend on engineering breakthroughs (error correction scaling, qubit connectivity, manufacturing yield). Optimistic: 5-10 years to useful. Pessimistic: 20+ years. History suggests transformative technologies take longer than promised but arrive more suddenly than expected.
  • "Prediction is very difficult, especially about the future."-- Niels Bohr (attr.)
Slide 14

Key Takeaways

  • What Quantum Computing Is
  • Not faster classical computing
  • Fundamentally different paradigm
  • Exploits superposition, entanglement, interference
  • Exponential advantage for specific problems
  • Complements (not replaces) classical
  • Where We Are
  • NISQ era: noisy, limited qubits
  • Error correction threshold crossed
  • Cloud access democratizing research
  • Billions invested globally
  • Post-quantum crypto migration starting
  • What to Watch
  • Error correction scaling
  • First practical quantum advantage
  • Cryptographic implications (prepare now)
  • Drug discovery breakthroughs
  • Quantum internet development
Slide 15

Quantum Computing

  • End
  • Harnessing quantum mechanics to solve problems beyond classical reach -- the next computing revolution.
  • 30 slides • Technology • 2024
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