Emerging Science & Technology

Quantum Computing

Some background on quantum computing, followed by data snapshots of potential applications and algorithms.

What are quantum computers?

Quantum computers use quantum mechanical phenomena to perform calculations. These computers 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.

Some of the emerging trends in quantum computing include the use of superconducting qubits, the use of trapped ions, and the use of optical lattices.

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.

See more venture-funded companies →

A 360° view on quantum computing

Click here or on the image below to see a snapshot of business and R&D data on quantum computing.

'Quantum computing' Mergeflow 360° Report

Applications for quantum computing

Although quantum computing is still in its early stages, several potential applications are already being explored.

Here is a more general overview of quantum computing use cases. And here is a more detailed description, focusing on use cases in industry.

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.

Learn More

Automation & robotics

Quantum computing is still in its early developmental stages, but research suggests that it 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.

Learn More


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.

Learn More

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.

Learn More


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.

Learn More

Quantum computing algorithms

Some of the emerging trends in quantum computing algorithms include the use of machine learning for quantum control, the use of quantum annealing for optimization problems, and the use of quantum walks for search algorithms.

Click here for a general overview of quantum algorithms.

Shows up in Market Analyses

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.

Learn More

Rising activity in Scientific Publications

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.

Learn More

Rising activity in Scientific Publications

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.

Learn More

Rising activity in Scientific Publications

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.

Learn More


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.

Learn More

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.

Learn More

Quantum Fourier Transform

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

Learn More

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.

Learn More

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.

Learn More

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.

Learn More

Would you like to explore more?

With Mergeflow, you can explore the technologies above—and others as well—in more depth, and stay informed on new developments.

Try Mergeflow For Free

14-day free trial. No credit card, no software to install. Trial ends automatically after 14 days.

Curious to see how it works?

Video course, how to discover early-stage deep tech companies

Watch our free video course:

'Tech Discovery for Innovators'

Teams and individuals from across industries and tech sectors use Mergeflow

Australian Department of Foreign Affairs and Trade
Leica Microsystems
Siemens Healthcare

Company details

Court Register

München, HRB 168854

VAT Reg. No.

DE 255 860 072

Managing Board

Dr. Florian Wolf

Legal Status


Registered Office


Supervisory Board Chairman

Dr. Richard Sizmann

© 2021 Mergeflow