There is a lot of hype around quantum computing. This raises unrealistic expectations while at the same time it undervalues the real progress made by some very smart people who are working on it (because this progress just doesnâ€™t seem so impressive if what youâ€™re expecting is nothing less than imminent revolution).

Iâ€™m not an expert in quantum computing, but I wanted to get a better understanding of where the field is currently. So I used our (= Mergeflowâ€™s) software to do some exploring. First, I wanted to get a very general overview; you can see a snapshot of the data here (click on the image to see the data):

Then I wanted to get some insights into potential(!) quantum computing applications, and into some of the algorithms underpinning these developments.

But first, some basics:

## What are quantum computers?

Quantum computers use quantum mechanical phenomena to perform calculations. They are different in many ways from the computers that are in use today. For example, a quantum computer can be in multiple states simultaneously, whereas a classical computer can only be in one state at a time. This allows quantum computers to perform several calculations at once.

## Why is quantum computing important?

Quantum computing is important because it can be used to solve problems that are difficult or impossible for classical computers to solve. For example, quantum computers can be used to factor large numbers, search large databases, and simulate quantum systems.

## How mature is it?

Quantum computing is still in its early stages, with most quantum computers being built for research purposes. However, there are a few commercial quantum computers available, and quantum computing is expected to become more widely used in the future.

## What companies are working on it?

In addition to companies like AWS, Google, Honeywell, IBM and Microsoft, there are several quantum computing companies that have received venture funding.

Recent (= 2022) examples of venture-funded quantum computing companies include QunaSys, Paragraf, Classiq, Algorithmiq, PQShield, Terra Quantum, and Atom Computing.

I also exported data from Mergeflow on some venture-capital-funded quantum computing companies (click on the table screenshot below to see the data):

## Applications for quantum computing

Just to reiterate, quantum computing is still in its infancy. But several potential applications are already being explored.

It is my understanding that *“being explored”* basically means asking, *“if we had quantum computing now, how should we design algorithms and data structures for our applications so that we could run them on quantum computers?”*

If you’re curious, here is a more general overview of quantum computing use cases, and here is a more detailed description, focusing on use cases in industry.

What follows are brief descriptions of some applications, and links to Mergeflow data snapshots, across business and R&D data, if you want to zoom in to more details.

### Optimization problems

Quantum computers can be used to solve optimization problems more efficiently than classical computers. This is because they can explore a larger space of solutions simultaneously. In addition, quantum computers can take advantage of quantum effects such as entanglement and interference to find the best solution more quickly. Optimization problems play important roles in supply chain and logistics, manufacturing processes, and other applications.

“Quantum computing for optimization” Mergeflow data snapshot

### Automation & robotics

Research suggests that quantum computing could eventually lead to a new era of factory automation. In the future, quantum computers could be used to automate factories by optimizing production schedules and controlling robotic arms.

“Quantum computing for robotics” Mergeflow data snapshot

### Finance

There are a few different ways that quantum computing could be used in finance. For example, quantum computers could be used to create more efficient algorithms for financial analysis and modeling. They could also be used to help solve complex optimization problems, such as portfolio optimization. Additionally, quantum computers could be used to create more secure financial systems, by encrypting data using quantum key distribution.

“Quantum computing for finance” Mergeflow data snapshot

### Materials discovery

In materials discovery, quantum computers can be used to find new materials with specific properties. In chemistry, quantum computers can be used to simulate chemical reactions and to design new drugs.

“Quantum computing for materials” Mergeflow data snapshot

### Cryptography

Quantum cryptography is a type of cryptography that uses quantum bits instead of classical bits. This makes quantum cryptography much more secure than traditional cryptography.

Post-quantum cryptography is a branch of cryptography that is concerned with the development of cryptographic algorithms that are secure against attack by quantum computers.

“Quantum cryptography” Mergeflow data snapshot

## Quantum computing algorithms

Since quantum computing is different from classical computing, quantum computing algorithms need to be different as well.

If you’re interested in a general overview of quantum algorithms, you can look here, for example.

Similar to the applications above, below there are brief descriptions for each algorithm, and a link to a Mergeflow data snapshot with research papers, patents, and other contents.

### Bernstein-Vazirani algorithm

The Bernstein-Vazirani algorithm can be used to solve problems in machine learning, such as finding the weights of a neural network. It can also be used to find the parameters of a probabilistic model, such as the parameters of a Gaussian distribution.

Bernstein-Vazirani-Algorithm data snapshot

### Grover’s algorithm

There are many potential applications of Grover’s algorithm. For example, it could be used to search for a particular item in a database, or to find a needle in a haystack. It could also be used for cryptography, or to solve optimization problems.

Grover’s algorithm data snapshot

### Quantum Approximate Optimization Algorithm (QAOA)

Some potential applications of QAOA include: Finding the shortest path between two points in a network; solving problems in machine learning and artificial intelligence; optimizing financial portfolios; scheduling tasks and resources; designing experiments; planning routes for vehicles.

### Quantum counting

Quantum counting can be used to count the number of qubits in a quantum computer, as well as the number of photons in an optical fiber.

Quantum counting data snapshot

### Quantum Fourier Transform

Quantum Fourier Transform can be used for quantum state estimation, quantum state tomography, quantum process tomography, and quantum error correction.

Quantum Fourier Transform data snapshot

### Quantum phase estimation

Quantum phase estimation can be used to estimate the eigenvalues of a unitary operator. This can be used to find the energy of a quantum system, or to find the time it takes for a quantum system to evolve.

Quantum phase estimation data snapshot

### Quantum walk search algorithm

There are many potential applications for the quantum walk search algorithm. Some examples include: Searching for a specific item in a large database; finding a needle in a haystack; navigating through a maze; optimizing routes in transportation networks.

Quantum walk search algorithm data snapshot

### Shor’s algorithm

Shor’s algorithm is a quantum algorithm for integer factorization created by Peter Shor in 1994. It is the most efficient known classical algorithm for this problem, with a running time of polynomial in the size of the integer to be factored. However, it is not known how to implement Shor’s algorithm on a quantum computer in less than exponential time.

Shor’s algorithm data snapshot

### Simon’s algorithm

Simon’s algorithm is a quantum algorithm for finding the period of a function. It can be used to factor integers and to find the order of an element in a finite group.

Simon’s algorithm data snapshot

### Variational quantum eigensolver algorithm (VQE)

Some potential applications of the VQE algorithm include: Finding the ground state energy of a quantum system; optimizing quantum circuits; solving quantum many-body problems; performing quantum chemistry calculations.