D-Wave Quantum Annealer Practical Use in 2023

What is Quantum Annealer?

By making use of properties specific to quantum physics such as quantum tunneling, entanglement, and superposition, quantum annealing (QA) (related to adiabatic quantum computation) is a method for solving problems involving a large number of possible solutions and variables.

The goal of quantum annealing is to solve optimization problems using adiabatic quantum computing. Optimization problems are solved by gradually changing a simple quantum state — called the initial state — into a more complicated quantum state that encodes the solution. Hamiltonians are mathematical operators that describe the energy of a quantum system and control the transformation. Throughout the transformation process, the system tends to settle down to the state of lowest energy, which corresponds to the solution of the optimization problem.

In contrast, adiabatic quantum computing uses a quantum computer to solve various problems. As a result, simple quantum states are gradually transformed into more complicated quantum states that encode solutions. As in quantum annealing, the transformation is controlled by the Hamiltonian. It doesn’t have to be specifically designed for optimization problems, because the transformation can be more general.

As one of the pioneers of quantum computing, D-Wave using QA, an optimization process that uses quantum fluctuations to determine the global minimum of a given objective function.

Last summer, D-Wave make an announcement next generation Advantage2 annealed quantum computer prototype in Leap’s quantum cloud service. Using an innovative new qubit design and the new Zephyr topology, the D-Wave prototype has over 500 qubits, featuring 20-way connectivity.

However, in less than a year, the company has stepped up from last year’s figure, as D-Wave joint progress towards the next generation Advantage2 annealed quantum computing system, which will feature 7000+ qubits and 20-way connectivity, and is expected to be implemented in fabrication stacks with lower noise, enabling customers to solve more complex problems with greater precision.

Modern technology is enabling quantum annealing to become a commercial reality. Compared with traditional computers, it is claimed to be more effective in solving optimization problems with large local minima.

As well as taking place in a laboratory, it is a promising quantum technology for companies that have serious optimization problems that are too difficult for traditional computers to solve.

What are the Applications of Quantum Annealing?

As already mentioned, quantum annealing is best suited for optimization problems. A good use case featuring this is described in the paper Application of Quantum Annealing to Nurse Scheduling Problemspublished in Scientific Reports by Kazuki Ikeda, Yuma Nakamura and Travis S. Humble in 2019.

The three detail that there is considerable interest in measuring the performance of quantum annealing on real-world problems to gain insight into how this approach can be most effectively applied in practice. Using the D-Wave 2000Q quantum annealing device, the researchers investigated the empirical performance of quantum annealing in solving the Nurse Scheduling Problem (NSP) with hard constraints. Under a series of schedules and personnel constraints, NSP strives to optimally assign nurses to shifts. They found that by reducing the NSP to the new Ising-type Hamiltonian, they could evaluate the quality of the solution obtained from the D-Wave 2000Q against the constraint requirements.

Final paper abstract:

“For the test issues explored here, our results demonstrated that quantum annealing recovers satisfactory solutions for NSPs and suggested heuristic methods could potentially be achievable for practical use. Moreover, we observe that solution quality can be greatly improved through the use of back annealing, where it is possible to fine-tune the returned result by using the annealing process a second time. We compare the performance of NSPs using forward and reverse annealing methods and explain how this approach can be used in practice.”

Difference Between Quantum Annealing and Gate Model

The fundamental difference between the two approaches is that the gated model quantum computer requires the problem to be expressed in terms of a quantum gate, whereas the annealed quantum computer requires the problem to be expressed in terms of an operations research problem. That’s one way to understand the difference between the two types of quantum computers.

What are the Benefits of Quantum Annealer?

Again and without treading old ground, quantum annealing makes it possible to solve certain problems of enormous complexity by designing the problem so that when the computer reaches its minimum energy state, it will be able to solve it.

The Importance of QUBO Modeling

An optimization technique known as BATHE (Quadratic Unconstrained Binary Optimization) is used in quantum computing, machine learning (ML), and optimization to solve complex optimization problems. The importance of the QUBO model lies in its ability to represent complex optimization problems using binary variables, making translation into the language of a quantum computer much easier.

The importance of QUBO modeling can be attributed to several factors:

QUBO modeling is very useful for solving complex optimization problems. Various types of problems can be modeled using it, including scheduling, logistics, and finance.

Another area where QUBO could benefit is quantum computing, as it allows it to tackle problems that are impossible or extremely difficult for classical computers.

In addition, QUBO modeling is useful for feature selection in ML. ML models can be trained by identifying the most important features in a data set.

Finally, when binary variables are used in QUBO modeling, the complexity of the optimization problem is reduced, allowing classical or quantum computing techniques to be used to solve the optimization problem.

How was Quantum Annealing Used in the Past?

To answer this question in part, we need to return to the work of D-Wave, a company that has the honor of being the first in the world to sell a computer that incorporates quantum effects into its operations.

D-Wave announced the first commercial quantum annealer on the market in 2011 under the name D-Wave One, which is described in Natural paper. In terms of the processor chipset, the company claims at the time that this system has 128 qubits. Later that year, Lockheed Martin Corporation entered into an agreement with D-Wave to purchase the D-Wave One system.

According to a 2013 announcement, Google, NASA Ames, and the nonprofit University Space Research Association purchased an adiabatic quantum computer from D-Wave Systems.

A year later, and as part of its commitment to solving real-world problems with quantum hardware, D-Wave announced a new quantum application ecosystem with computing finance firm 1QB Information Technologies (1QBit) and cancer research group DNA-SEQ.

What are the Future Prospects of Quantum Annealer?

Unlike traditional quantum computers, D-Wave has had a different architecture in the past to most quantum computing companies, but made the announcement at Qubits 2021 that the company is working on its first universal quantum computer that can run Shor’s algorithm and other gateway model algorithms such as QAOA. and VQE. The business already has a strong Intellectual Property base relevant to gateway model quantum computing and this is seen as a natural next step within the organization.

April 2023 saw D-Wave publish the results of one of the largest programmable quantum simulations ever reported. For the first time, computations using more than 5,000 qubits in the D-Wave Advantage quantum computer demonstrated coherent quantum dynamics faster than classical dynamics in programmable 3D spin glass, an optimization problem that was difficult to solve.

Scientists from D-Wave and Boston University collaborated to publish a paper entitled “Quantum critical dynamics in a programmable 5,000 qubit rotating glass,” in the Natural.

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Featured image: D-Wave