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QC/QIS Links for “Classical” Developers (My best of...)

1st read Terry Rudolph’s Q is for Quantum (http://qisforquantum.org)
Michael Nielsen’s (@michael_nielsen) quantum intuition
Yanofsky’s AN INTRODUCTION TO QUANTUM COMPUTING (his book, fyi, QC for CS)
Scott Aaronson and Charlie Bennett videos (and Charlie’s Strengths and Weaknesses of Quantum Computing)
Quantum Intuition’s (@learn_quantum) videos, e.g., https://www.youtube.com/watch?v=dOAfO9tU1N0
Jonathan Hui’s QC — Quantum Computing Series
Andrea Rocchetto’s Quantum Computing Fact Sheet
Best way to learn is Hand-on with IBM Q (see also Qiskit's learning resources)
For a unique perspective and new “language” to reason about quantum computing, see

Picturing Quantum Processes: A First Course in Quantum Theory and Diagrammatic Reasoning

(and related, see the TensorNetwork project on github)

Quantum Error Correction (the latest buzz is LDPC codes in High-threshold and low-overhead fault-tolerant quantum memory)
Quantum Chemistry (an excellent survey paper and algorithms paper)
Quantum ML (a highly-cited paper, a survey paper, Schuld’s intro paper, plus SA’s QML: Read the fine print)
Quantum Optimization (TBD)
And, if you’re really curious, the best QM textbook, A Modern Approach to Quantum Mechanics
Oldies but An Introduction to Quantum Computing for Non-Physicists & Basic concepts in quantum computation
FAQ: Peter Shor on breaking crypto.
FAQ: What about Bitcoin (also see this)? :)
FAQ: What’s meant by “interference?”
FAQ: What’s a dilution refrigerator?
FAQ: Latest experts panel?
Curated list of papers

From the quantum journey of: �Joe Latone (jlatone@us.ibm.com)�IBM Research

Last updated: 22 Nov 2023

Licensed under the CC BY 4.0, https://creativecommons.org/licenses/by/4.0/

A comic reminder:

AVOID terrible analogies�AVOID ridiculous overhyped nonsense�AVOID oversimplified factoids, e.g., � “0 and 1 at the same time”� “computes all answers at once”�AVOID confusion

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A guide to self-learning for QC/QIS beginners

Areas to master & intuit	Read & practice & iterate¹ (you will need to grind it out, no shortcuts)
Quantum Strangeness	Superposition, Entanglement, Interference See Q is for Quantum
Quantum Math	ℂomplex Numbers, Tensors (Vectors, Matrices, Linear Algebra) See AN INTRODUCTION TO QUANTUM COMPUTING and 8.370
Quantum Computation	Computation Models (Turing vs Circuit), Complexity (P, NP)
Quantum Gates	Hadamard (superposition), CNOT (entanglement)
Quantum Constructs	QFT, Amplitude Amplification, Phase Estimation, Hamiltonian Simulation, VQE/QAOA See Quantum Computation: a Tutorial, Benoıt Valiron
Quantum Algorithm Design	Developing an intuition for quantum programming: QCVC, Search
Quantum Algorithms	Big 3: Shor’s (factoring), Grover’s (search), HHL (linear equations) See Quantum Algorithm Implementations for Beginners
Quantum Applications	Chemistry, AI/ML, Optimization (more links TBD)

¹Helpful interactive, visual simulators while studying/practicing above:

IBM Q Experience’s Composer, Craig Gidney’s quirk, Stewart Smith’s Q, and Quantastica’s Quantum Programming Studio

Quantum computing exploits the behavior and properties of quantum mechanics, and QM is Fourier analysis. Matrices, vectors, and complex numbers happen to map very well to the mathematics of Fourier analysis, so they naturally form the basic language in which QM is expressed...and, thus, the basic language in which QC is expressed.

Building quantum algorithms is most challenging for 2 reasons:

Quantum mechanics is a counterintuitive theory, and intuition plays a key role in algorithm design; and
A competitive quantum algorithm must not only produce the expected solutions, it also has to be more efficient, at least for some input values, than any classical algorithm (at least more efficient than existing classical algorithms).

In the history of computing, there are 2 use-cases for specialized co-processors:

the need to optimize at scale a particular data structure, e.g., GPUs were created to process the billions & trillions of polygons moved/scaled/rotated/textured to render photo-realistic images; and
the need to optimize a critical inner loop, e.g., FPUs were created to process the complex microcode of a floating point operation.

Albeit quite specialized, and the comparison downplays the amazing science & engineering that goes into creating/building one, a QC (QPU) is a co-processor, not entirely unlike a FPU, GPU, FPGA, ASIC. I.e., you’ll always have a general purpose CPU-based application invoking it to do something highly specialized as part of the bigger application.