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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Quantum Fourier Transform

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

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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.

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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.

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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.

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