Free Maps on Garmin eTrex 10 (and all Garmin GPS)
Despite it is sold as a non-cartographic handled GPS device, with limited storage capacity of 10 MB and the inability to expand it, the eTrex 10 GPS, like al...
Factorization using Qlasskit and DWave Quantum Annealer
In the last release of Qlasskit, I introduced a new feature able to export a qlassf function to a binary quadratic model (as bqm, qubo or ising model).
This feature introduces qlasskit to the realm of quantum annealer like the ones manufactered by DWave; in this blog post, we’ll explore this
new feature, using Qlasskit and the DWave quantum annealer for prime factorization.
Instead of employing the traditional Shor algorithm utilized in circuit-base quantum computers, we opt to frame our problem as a minimization problem and we exploit the adiabatic quantum computing for searching a solution.
We begin defining a Qlasskit function called test_factor_generic which takes the number num to be factorized along with its two factors a and b as inputs.
It returns 0 if a multiplied by b equals num.
from qlasskit import qlassf, Qint4, Qint3, Parameter
@qlassf
def test_factor_generic(num: Parameter[Qint4], a: Qint3, b: Qint3) -> Qint4:
return num - (a * b)
Next, we bind the num parameter to the number 15 and convert the Qlasskit function to a binary quadratic model (BQM) using the to_bqm function.
test_factor = test_factor_generic.bind(num=15)
bqm = test_factor.to_bqm()
This BQM represents the optimization problem of finding the factors of 15.
Running the sampler
With our problem encoded into a BQM, we’re now poised to execute it on a real quantum annealer. Here’s how:
from dwave.system import DWaveSampler, EmbeddingComposite
from qlasskit.bqm import decode_samples
sampler = EmbeddingComposite(DWaveSampler())
sampleset = sampler.sample(bqm, num_reads=10)
decoded_samples = decode_samples(test_factor, sampleset)
best_sample = min(decoded_samples, key=lambda x: x.energy)
print(best_sample.sample)
This code snippet runs the BQM on a DWave quantum annealer and prints the best solution found, which, as expected, yields {'a': 3, 'b': 5}, since 5 times 3 equals 15.
If you don’t have access to a DWave account, you can still run smaller problems like this one using a simulated annealing sampler as follows:
import neal
sa = neal.SimulatedAnnealingSampler()
sampleset = sa.sample(bqm, num_reads=10)
decoded_samples = decode_samples(test_factor, sampleset)
best_sample = min(decoded_samples, key=lambda x: x.energy)
print(best_sample.sample)
Conclusion
In this post, we’ve demonstrated how to leverage Qlasskit and the DWave quantum annealer to factorize a number. Although this example is relatively straightforward, it underscores the potential of the qlasskit library in describing optimization problems using high-level abstractions.
Useful Links:
2024
Optimizing quantum circuit using boolean algebra
Since the latest v0.1.18 version, the Qlasskit library offers two useful tool for circuit analysis and optimization.
Factorization using Qlasskit and DWave Quantum Annealer
In the last release of Qlasskit, I introduced a new feature able to export a qlassf function to a binary quadratic model (as bqm, qubo or ising model). This...
The Time I Built a Probabilistic Computer
In early 2023, I embarked on a journey to explore the field of probabilistic computing. This endeavor culminated in the construction of a hardware prototype,...
Solving Sudoku Puzzles on a Quantum Computer
Today, I’m going to show you how to use Qlasskit to create a quantum circuit able to search for Sudoku puzzle solutions.
2023
Expanding the Commodore 64 Quantum Emulator
In a recent article I wrote, “Quantum Computing on a Commodore 64 in 200 Lines of BASIC”, published both on Medium and Hackaday.com, shows a two-qubit quantu...
Qlasskit - a bridge between Python and Quantum algorithms
Traditionally, creating quantum circuits requires specialized knowledge in quantum programming. This requirement holds true when encoding a classical algorit...
Quantum Computing on a Commodore 64 in 200 Lines of BASIC
In an age where companies are selling two-qubit quantum computers for a sum of money that would make your wallet recoil in horror, here we are, stepping off ...
My Journey at the UnitaryHack Hackathon: A Quantum Computing Adventure
This June I emerged as one of the top participants with 9 bounties collected (alongside another exceptional contributor) in the #UnitaryHack Hackathon, hoste...
Ship detection from Sentinel-2 satellite images
A few days ago I came across a yt video discussing the ESA Copernicus program, a European initiative for monitoring earth via a satellite constellation. This...
2020
OCaml and Quantum Computing
Qiskit is a python SDK developed by IBM and allows everyone to create quantum circuits, simulate them locally and also run the quantum circuit on a real quan...
Yallo, a new Tezos language
As someone noticed from the previous post, last weeks I started to write a new programming language for Tezos smart contracts. This project was initially int...
King of Tezos: a smart-ponzi on Tezos
While writing a new programming language, it is often useful to write some real use-cases to test the syntax, the language expressiveness and the code cleann...
Favorite dev quote
Documentation is like sex: when it is good, it is very, very good; and when it is bad, it is better than nothing
New blog
This is my new blog, based on jekyll. I’ll soon import old posts from my old blog.
2016
Contractvm: decentralized applications on Bitcoin
Contractvm is a general-purpose decentralized framework based on blockchain. The framework allows to implement arbitrary decentralized applications in an eas...
2015
Dices provably fair - Nonce overflow vulnerability
Most of bitcoin dice software use a system to prove the fair play of the server for each bet. Most of them implement this mechanism using two seed (server se...
2014
Apache2: redirect different domains to subfolder
In the aim to merge two of my server on digitalocean, today I tried to write a mod_rewrite rule to redirect a secondary domain to a subfolder. After one hour...
2013
MineML: F# miner
MineML is a multithread CPU based bitcoin miner written in F#. At the moment it’s a slow implementation, but the class structure offers the possibility to im...