This is the open-source repository for the book Quantum Computing for Programmers by Robert Hundt, Cambridge University Press (QCC4CP for short). The book describes this implementation in great detail, including all the underlying math and derivations. Note, however, that this code base is evolving - not all algorithms found here are discussed in the book.
To get started quickly on the Python sources, you may find the Quickstart Guide helpful.
This project builds vendor-independent infrastructure from the ground up and implements standard algorithms, such as Quantum Teleportation, Superdense coding, Deutsch-Jozsa, Bernstein-Vazirani, Quantum Phase estimation (QPE), Grover's Search (with application to Quantum counting, amplitude estimation, Mean and Median estimation, 3SAT, Graph Coloring, and Minimum finding), Quantum random walks, VQE, Max-Cut, Subset-Sum, Quantum Fourier Transform (QFT), Shor's integer factorization, Solovay-Kitaev, Principal Component Analysis, and a few more. It also implements high performance quantum simulation and a transpilation technique to compile circuits to other infrastructures, such as Qiskit or Cirq.
The code is organized as follows:
srcis the main source directory. All algorithms are in this directory.src/libcontains the library functions for tensors, states, operators, circuits, and so on, as well as their corresponding tests. All algorithms depend on these library functions.src/libqcontains the sparse implementation.src/benchmarkscontains a few benchmarks, as they are mentioned in the book.resourcescontains additional text, sections and chapters.erratacontains the errata for the book - corrections and clarifications.externalcontains the *.BUILD files to pointbazeltopythonandnumpy.
There are several ways to get started on this code base:
- Instructions for a Python-only, minimal setup can be found here.
- If you have access to Docker, the corresponding simple instructions are here
- Manual installation on Linux (Debian / Ubuntu) are here
- For MacOS, see README.MacOS.md.
- For Windows (partially supported), see README.Windows.md.
- For interactive SageMath, see README.SageMath.md.
- CentOS is also supported (see README.CentOS.md).
The main algorithms are all in src.
To run individual algorithms via bazel, run any of these command lines. Note the missing .py extensions when using bazel). Alternatively, run each Python file with python <file> (PYTHONPATH must point to the root directory):
# Algorithms discussed in the book:
bazel run arith_classic
bazel run arith_quantum
bazel run bernstein
bazel run counting
bazel run deutsch
bazel run deutsch_jozsa
bazel run entanglement_swap
bazel run grover
bazel run max_cut
bazel run order_finding
bazel run phase_estimation
bazel run phase_kick
bazel run quantum_walk
bazel run shor_classic
bazel run simon
bazel run simon_general
bazel run solovay_kitaev
bazel run subset_sum
bazel run superdense
bazel run supremacy
bazel run swap_test
bazel run teleportation
bazel run vqe_simple
# Additional algorithms and techniques, to clarify, or
# in preparation of a new edition of the book:
bazel run amplitude_estimation
bazel run bell_basis
bazel run chsh
bazel run estimate_pi
bazel run euclidean_distance
bazel run graph_coloring
bazel run hadamard_test
bazel run hamiltonian_encoding
bazel run hhl
bazel run hhl_2x2
bazel run inversion_test
bazel run minimum_finding
bazel run oracle_synth
bazel run pauli_rep
bazel run purification
bazel run qram
bazel run quantum_mean
bazel run quantum_median
bazel run quantum_pca
bazel run sat3
bazel run schmidt_decomp
bazel run spectral_decomp
bazel run state_prep
bazel run state_prep_mottonen
bazel run zy_decomp
To test aspects of the sparse implementation:
cd src/libq
bazel test ...
To run the benchmarks:
cd src/benchmarks
bazel run larose_benchmark
bazel run tensor_math
To experiment with transpilation, a few things must work together:
-
Specify a target output. For example, to generate a
libqC++ file, use--libq=./test.cc -
The code should only contain a single
circuit.qc()-generated circuit. This circuit will not be eagerly executed. Instead, all gates and qubits will be collected in an internal IR. -
There must be a single call to
qc.dump_to_file(). The circuit as that point will be transpiled to the target platform (an example of this can be found inorder_finding.py).
For the given example, the generated file test.cc can be compiled and linked with libq
with a command-line similar to this one:
$ cd qcc/src
$ cc -O2 -Ilibq test.cc libq/qureg.cc libq/apply.cc libq/gates.cc -o a.out -lc++
$ a.out
This code and book were written by Robert Hundt. At the time of this writing, Robert is a Distinguished Enginer at Google. However, this is a private project, developed on personal infrastructure and in private time. It is completely independent of Robert's work at Google.
Reach Robert at
- https://www.linkedin.com/in/robert-hundt-2000/
- [email protected] (site-specific email account)
- Colin Zhu, for pointing out coding problems.
- Kevin Crook, Univ. of CA, Berkeley, for feedback and discussion of the Chinese Remainder Theorem.
- Moez A. AbdelGawad, Alexandria University, Egypt, for suggesting Windows and SageMath ports.
- Stefanie Scherzinger, Universitaet Passau, for corrections and suggesting Docker.
- Abdolhamid Pourghazi and Stefan Klessinger, for providing and maintaining the Dockerfile.
- Michael Broughton, for help with purification.
- Mikhail Remnev, for pointing out a .dylib problem in MacOS
- Andrea Novellini, for fixing a WORKSPACE issue with bazel 7.0.x
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent