domcsis-epic-tinker-box 0.5.1

Domcsi's Epic Tinker Box - a collection of Python utilities for scientific and quantum computing research.

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Domcsi's Epic Tinker Box

Hello stranger!

Domcsi's Epic Tinker Box is my little repository of Python and Julia libraries filled (gradually) with code and routines that I use throughout my coding, scientific and research journey.

This is also a playground for myself to learn and grow as a programmer.

Table of contents:

• Description
• Implemented Features
  • statistics
  • pauli_algebra
  • quantum_state
  • magic
• Metrics
  • Number of Tests
  • LOC metrics

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Implemented Features

statistics

Function	Description	Python	Julia
mean	Arithmetic mean of a numeric vector	✅	✅
mean_complex	Population mean of complex samples; returns (real_part, complex_mean)	✅	❌
variance	Population variance (N denominator)	✅	❌
skewness	Pearson's moment coefficient of skewness	✅	❌
generate_gaussian_rnd_numbers	Samples from 𝒩(μ, σ²)	✅	❌
generate_uniform_rnd_numbers	Samples from Uniform(a, b)	✅	❌
generate_exponential_rnd_numbers	Samples from Exp(λ)	✅	❌
generate_wigner_surmise_rnd_numbers	Samples from the generalised Wigner surmise (GOE/GUE/GSE and beyond)	✅	❌
generate_random_numbers	Unified dispatch wrapper for all distributions	✅	❌

All Python samplers support optional rng injection or seed for reproducible, non-global-state sampling.

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pauli_algebra

Function / Class	Description	Python	Julia
pauli_matrix(which)	Single-qubit Pauli matrix in sparse CSR format	✅	❌
generate_all_pauli_strings(n)	All 4ⁿ Pauli strings for n qubits in lexicographic order	✅	❌
generate_pauli_operators(n)	All 4ⁿ sparse Pauli matrices	✅	❌
PauliOperators	Immutable validated collection with factories all(n) and from_strings(set)	✅	❌

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quantum_state

Constructor	Description	Python	Julia
QuantumState.from_vector(ψ)	Pure state from state vector (2ᴺ,)	✅	❌
QuantumState.from_density_matrix(ρ)	State from density matrix (2ᴺ, 2ᴺ)	✅	❌
QuantumState.from_vectorized_density_matrix(ρ_vec)	State from vec(ρ) (4ᴺ,)	✅	❌

Derived quantities computed on demand:

Property	Returns	Python	Julia
.vector	State vector; raises for mixed states	✅	❌
.density_matrix	ρ = |ψ⟩⟨ψ| or stored ρ	✅	❌
.purity	Tr(ρ²)	✅	❌
.dim	Hilbert space dimension 2ᴺ	✅	❌

Function	Description	Python	Julia
bell_state	Generate general Bell-states	✅	❌
w_state	Generate general w-states	✅	❌
bell_state	Generate general Bell-states	✅	❌
generate_all_stabilizer_states	-	❌	❌
generate_random_stabilizer_state	-	❌	❌

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magic

Function	Description	Python	Julia
stabilizer_renyi_entropy(state, paulis, α)	α-Stabilizer Rényi Entropy M_α	✅	❌
_stabilizer_renyi_entropy_unchecked(...)	Fast path for hot loops	✅	❌
characteristic_function(ψ, paulis)	Ξ(P) = |⟨ψ|P|ψ⟩|² / 2ⁿ for all P	✅	❌
validate_alpha(α)	Standalone α validator	✅	❌
validate_compatible(state, paulis)	Checks n_qubits agreement	✅	❌

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Metrics

Number of Tests

• Python tests: 344 tests
• Julia tests: 6 tests

LOC metrics

Now this is not important, and I do not think that it is a good idea to attribute quality, effort or productiveness to the following metric, but it is like the first, easy-peasy GitHub Action that one can set up, so here it is:

LOC is lines of code
LOCo is lines of comments
code share is LOC/(LOC + LOCo)

• Python: 2613 LOC, code share = 65.7%
• Julia: 36 LOC, code share = 97.3%

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