Quantum computing
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Quantum computing has several potential applications such as solving very large calculations, improving security and optimization problems, and advancing machine learning and simulation. It harnesses quantum phenomena like superposition and entanglement to store and process information using quantum bits that can be in multiple states at once. This allows quantum computers to massively parallelize computations and solve certain problems like integer factorization much faster than classical computers. However, quantum computers face challenges like decoherence that cause qubits to lose their quantum properties, limiting their size and capabilities. Researchers are working to develop different approaches to building larger, more useful quantum computers.
Quantum computing
- 1. Quantum Computing There's plenty of room at the bottom. Richard Feynman
- 2. Why Do We Need Quantum Computing ● Moore's Law. ● Calculations (Very Very …. Large ones) ● Security ● Optimizations ● Machine Learning ● Simulation
- 4. RSA-2048 (617 Digits) Prime Factorisation ● Using classical General Number Field Sieve ● Using Quantum Peter Shor's Factoring Alogorith ● t ~ 10seconds● T ~ 4 x 1017 seconds ~13 x 109 Years i.e Age of Universe Time to solve
- 5. Security
- 6. Optimization ● To find the best value to your capabilities,money,capital,etc ● Classical Light Switch Problem ● Water Network Optimization ● Radiotherapy Optimaztion ● Protein Folding
- 7. Machine Learning ● Artificial Intelligence ● Object Detection ● Labelling New Stories ● Video Compression
- 8. Harnessing Quantum Phenomenon for Computations ● Quantum Tunnelling ● Quantization of Energy ● Entanglement● Superposition Delhi Hyderabad
- 9. Bit vs Qubit ● 0 or 1 ● Freely readable ● Programming is done in 0's or 1's ● 0 or 1 or ● It is destroyed when reading Q-bit ● Programming is done in terms of energy to be given to a Q-bit ● Intrinsic Randomness ● Entanglement ● Uncertainty Principle
- 11. Classical Vs Quantum Register Quantum Classical If the n = 300, The Quantum Register could hold more numbers simultaneously than there are atoms in the 'Observable Universe'
- 12. How does it solve
- 13. Implementing ● Different Models:Different Models: Gate, One-way Topological, Adiabatic Annealing ● Different Basis:Different Basis: Superconducting metals, ion traps, photons, solid state SQuID Nuclear Spin Based Q-bit Trapped Ion QC
- 14. Decoherence ● Schrodinger's cat can be 'Dead' and 'Alive' only if its completely isolated from all kinds of interactions with the world. ● Likewise the Quantum computer should not be sneaked while computing. ● This is the major hinderance in Q-Computing
- 15. Till Now...
- 16. Till Now ● First execution of Shor's algorithm at IBM's Almaden Research Center and Stanford University. The number 15 was factored using 1018 identical molecules, each containing seven active nuclear spins ● Scientists transfer data by quantum teleportation over a distance of 10 feet with zero percent error rate, a vital step towards a quantum internet ● D-wave systems bult a 512 Q-bit Processor with NASA and Google's collaboration
- 17. Thank you
Editor's Notes
- #4: "Moore's law" is the observation that, over the history of computing hardware, the number of transistors in a dense integrated circuit doubles approximately every two years. The observation is named after Gordon E. Moore, co-founder of the Intel Corporation, who first described the trend in a 1965 paper[1][2][2][3] and formulated its current statement in 1975. His prediction has proven to be accurate, in part because the law now is used in the semiconductor industry to guide long-term planning and to set targets for research and development.[4] The capabilities of many digital electronic devices are strongly linked to Moore's law: quality-adjusted microprocessor prices,[5] memory capacity, sensors and even the number and size of pixels in digital cameras.[6] All of these are improving at roughly exponential rates as well. This exponential improvement has dramatically enhanced the effect of digital electronics in nearly every segment of the world economy.[7] Moore's law describes a driving force of technological and social change, productivity, and economic growth in the late twentieth and early twenty-first centuries.[8][9][10][11] The period is often quoted as 18 months because of Intel executive David House, who predicted that chip performance would double every 18 months (being a combination of the effect of more transistors and their being faster).[12] Although this trend has continued for more than half a century, "Moore's law" should be considered an observation or conjecture and not a physical or natural law. Sources in 2005 expected it to continue until at least 2015 or 2020.[note 1][14] The 2010 update to the International Technology Roadmap for Semiconductors predicted that growth will slow at the end of 2013,[15] however, when transistor counts and densities are to double only every three years.