Hello. Quantum computers are moving fast, aren’t they? In this article, I summarize the quantum computer industry.

4 hardware

Here are four types of hardware you need to know,

The first is superconducting, for which IBM and Google were competing. It is still widely used, and the price is much lower and easier to use than before.

The second is the ion trap, which is the method that is currently attracting the most attention. It is easy to use because of its low error, high coupling number, and long coherence time. …

Google published a paper on quantum computing on April.


Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor

Google AI Quantum and Collaborators∗ (Dated: April 10, 2020)

This paper shows the result of experiment on actual quantum computer with combinatorial optimization problem.


Quantum Approximate optimization algorithm is an algorithm for NISQ quantum computer. NISQ is a shor name of Noisy Intermediate Scale Quantum, which has errors and small number of qubits. QAOA is optimized the quantum circuit to work on these kind of near term devices.

Quantum Annealing

Quantum annealing is an algorithm to simulate the annealing process…

Baidu announced that they publish paddle quantum in paddle paddle deep learning framework. Let’s take a brief look at this framework.

NISQ variational algorithm + deep learning optimization

Basically existing quantum computer is working on hybrid system with classical computer.

First you do quantum circuit to get expectation value of state vector behind the calculation and get the expectation value from the measurement and optimize it to get lower value of expectation.

On this process the quantum circuit contains a lot of variational parameters as angles and deep learning tensor calculation is very similar to that.

So the deep learning framework is used to simulate the quantum…

The new release of blueqat now support QAOA mixer, RXX,RYY,RZZ ising coupling gate and the reverse playback of quantum circuit. Now I do some trial on Quantum Adiabatic process with variational QAOA algorithm on gate model quantum computing.

Install Blueqat

Install blueqat from pip,

!pip install -U blueqat

The hamiltonian is like below and the initial state is |+> corresponding to the initial mixer X.

from blueqat import Circuit
from blueqat import vqe
from blueqat.pauli import X,Y,Z
h = 5*Z[0]-2*Z[0]*Z[1]
step = 2
result = vqe.Vqe(vqe.QaoaAnsatz(h,step)).run()

The result is,

(((1, 1), 0.8144445064787001),)Circuit(2).h[:].cx[0, 1].rz(49.460289478707516)[1].cx[0, 1].rz(-123.6507236967688)[0].rx(4.758485377427176)[:].cx[0, 1].rz(64.56773083408335)[1].cx[0, 1].rz(-161.41932708520838)[0].rx(0.7862442700373741)[:]

This circuit include…

Like Tensorflow follows quantum computing and NISQ variational circuit like tensorflow quantum,

I just tried to implement two model on blueqat with simple implementation, let’s look at how to create.


The model is from tensor network field. Here the tree structure of quantum state can be written in quantum circuit.

|0> --[input]--U3--*--
|0> --[input]--U3--X--U3--*--
|0> --[input]--U3--*-- |
| |
|0> --[input]--U3--X--U3--X--[m]-[expt]-[loss]-[output]

Here I used U3 gate which has 3 angle parameters and CX gate to operate on single quantum state and entanglement among multiple qubits.

#MERA circuit
def mera(a):

u = Circuit()

For solving quantum chemistry or combinatorial optimization problem we usually use vqe or qaoa. This is quantum-classical hybrid system and to update or optimize the parameter in the circuit we usually use classical method. Here I just introduce way to optimize angle parameter using auto grad function of pytorch/tensorflow to update it using optimizer in these deep learning tools.

Numerical differentiation

With function f(x) and small change of h, we have,

An example of f(x) = x^2 , x=1 and h=0.1 the derivative is,

x = 1
h = 0.1

On the network or circuit, there are lot…

[Apology] About VQE article

Two of our medium article did violation that not mention about the original article on blogpost and github, and used directly their words. We already removed these article.

We just apologized to the original authors, that they are doing a very good contribution to the quantum computing community and we just respect their work.

The original post are,

Variational Quantum Eigensolver explained


Variational quantum eigensolver

These are very good articles. Please do refer to these articles.

We do effort to not happen again.Sorry again and we do apology letter here.

Yuichiro Minato


When I looking at a paper, I just found a very interesting way of clustering.

“Graph Clustering Approaches using Quantum Annealing”


This document is on clustering on D-Wave machine and looks interesting.

2 way of clustering

On the document there are 2 way of clustering,

  1. standard clustering with constraint
  2. clustering with modularity

Modularity is a kind of function to evaluate and analyze the network and graph. Here modularity sounds little bit complicated to understand here, we just try the first one to make a simple QUBO on clustering.

Simple Clustering on QUBO

Clustering on QUBO consists of 2 part. One is weight inside the cluster and the…

D-Wave released a new solver for quantum classical hybrid system.


D-Wave machine

This is a kind of quantum computing located near Vancouver in Canada. This is called quantum annealing machine and mainly solve combinatorial optimization problem. We install tools in our PC and post problems to the machine through internet. We solve these problems using a specific QUBO formulation and get the result.


There are many changes.

  1. Hybrid system is coming to the main function. (Also we can manage quantum machine itself.)
  2. New online IDE environment.

Let’s look inside.

Online IDE

Here we have a new IDE programming environment online. It contains…

By using structure of tensor network we can make many kind of quantum computing simulator. Here we just introduce one of it making a simple entangled quantum state with quantum circuit.

Previous article

Basic operation on tensor network using Google’s new tool is available from here.

Quantum Computation as 2-Dimensional Graph network on tensor

Usually the quantum computation is done from initial state to final state to operate quantum logic gate.

Here we see the quantum state as vector, 1qubit quantum logic gate as matrix. The point is how to see the 2qubits gate.

Yuichiro Minato

Blueqat, Quantum Computing@Tokyo, https://blueqat.com/

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