Short on time? Read the key takeaways:
- Since the 1940s, classical computers have gone from magical to groundbreaking to commonplace. This trajectory could foreshadow the evolution of quantum computing.
- Quantum annealing is an optimization technique that leverages the principles of quantum mechanics to find the global minimum of a function to solve problems.
- Quantum gates are the building blocks of quantum circuits and allow quantum information to be processed by manipulating the quantum state of qubits through unitary operations — like classical logic gates.
- Using quantum annealing optimization, quantum computing experts at Unisys solved a critical component of last-mile logistics.
Eighty years ago, the renowned Alan Turing and other computer scientists struggled to manually crack the Enigma machine to help the allies during World War II.
Eager to break the Nazi code cipher, they turned to the new Colossus computer. Manually cracking the Enigma was impossible. With the Colossus — a computer with lower processing power and capabilities than a standard-issued modern laptop — they were successful.
Since the 1940s, classical computers have gone from magical to groundbreaking to commonplace. This trajectory could foreshadow the evolution of quantum computing. Quantum computers use quantum mechanics to enable significantly faster processing and problem-solving than is possible with a classical computer. Now, several organizations are applying quantum computing to solve complex business problems much faster.
This article explores the current state of quantum and some essential fundamentals and predicts when you can expect to gain a quantum advantage.
The current state of quantum and essential fundamentals
How is quantum annealing different than quantum gates?
At its most fundamental level, quantum computing is grouped into one of two sub-branches: quantum annealing and quantum gate models.
Quantum annealing is an optimization technique that leverages the principles of quantum mechanics to find the global minimum of a function. It is used to solve problems, including optimization. The process involves mapping a problem to a physical system, such as a network of interacting qubits, and letting the system naturally evolve to find the solution.
Quantum gates are the building blocks of quantum circuits and allow quantum information to be processed. They work by manipulating the quantum state of qubits through unitary operations — like classical logic gates. In this sub-branch, quantum algorithms are constructed as sequences of quantum gates, which are executed to perform specific tasks — also like classical circuits, where algorithms are implemented using logic gates. Quantum gates are basic quantum circuits that operate on a small number of qubits to perform complex quantum algorithms for tasks such as simulating quantum systems, solving unstructured search problems and cracking cryptography.
Each sub-branch offers a different approach to solving problems and has distinct use cases, helping you make informed decisions about when and how to incorporate quantum computing into your organization's technology strategy.
The progress of innovation
Computer scientists assumed the first technology to emerge would be a quantum gate model — the big supermachines that solve giant general problems, such as unstructured search problems. But that hasn’t been the case. Technology hasn’t evolved as quickly as people thought, creating some degree of public skepticism around quantum. In its place, the concept of quantum annealing has emerged.
At its core, quantum annealing focuses on optimizing data sets and can be used to solve practical business problems. Quantum annealing has the potential to provide faster and more efficient solutions for optimization problems. It could be useful for applications such as portfolio optimization, scheduling and logistics.
Quantum annealing challenges users in one significant way. Quantum annealing takes in data like a vacuum, and you need someone on the other end to remove outliers, which takes time and effort. Data filtration creates challenges in application and business cases because it’s being solved manually, adversely affecting the technical and business outcomes.
Artificial intelligence and advanced analytics filter noise by helping structure the data and creating models to link that data. But those are topics for another day.
How to optimize for efficiency with quantum annealing
Quantum annealing is the sub-branch we can quickly deploy today with credible use cases in many industries, including logistics management. Let’s walk through one of these use cases. Recently, quantum computing experts at Unisys solved the capacitated vehicle routing problem (CVRP) with quantum annealing optimization.
CVRP is a vehicle-routing problem where vehicles with limited carrying capacity must load or unload cargo at multiple locations. With this problem, the objective is to minimize the cost of deliveries across all customers and all routes, given that:
- Only one vehicle visits each customer's location.
- Each vehicle can leave the depot only once.
- Each vehicle starts and ends its route at the depot.
- Each customer's demand is indivisible, and each vehicle is not allowed to exceed its maximum load capacity.
- No route is disconnected from the depot (sub-routing elimination).
- Customer demand, distribution distances between customers and delivery costs are known.
We made the following simplifying assumptions:
- All vehicles have the same capacity.
- All vehicles have the exact cost-per-unit distance traveled.
Using quantum annealing optimization, we solved a critical component of last-mile logistics — the classic example of calculating the most efficient route for delivering goods to customers to get a solution comparable to the best-known solution in a fraction of the time (milliseconds). The benefit is having solutions quicker or addressing problems that, because of their complexity and size, cannot be solved on traditional systems, e.g., due to time constraints.
The power of quantum annealing allows for more data to be analyzed in near-real-time to arrive at the best possible outcome instead of the most convenient information available. This processing power can impact all aspects of the front, middle and back office for organizations of any size. For example, financial institutions can offer customers more personalized recommendations by dynamically evaluating offering mixes against spending categories, credit risk and exposure with the ability to alter profiles as patterns change dynamically. The speed at which you can make these decisions and offers can lead to greater customer acquisition and retention while managing risk in the back office. The good news is quantum annealing computers can provide an answer in minutes — a speed we’ve never seen before.
When to expect the quantum advantage
One month in the quantum world is equivalent to years of research in the classic computing ecosystem. Many believe we are about five years from gaining the quantum advantage for quantum gate computers, but that could be overly ambitious.
While there may not be a general quantum gate computational machine within that timeframe, we could see specific use cases that exhibit the advantage. Even without a computer dedicated to that purpose, computer scientists can use a combination of hardware parts, algorithms and quantum correction errors to solve specific problems in a way not possible today.
As we approach attaining the quantum advantage, many organizations need help deploying this technology. Solving problems with quantum computing is heavily influenced by formulations, the complexity of the problem, the data involved and other factors. A partner like Unisys can help you determine the most efficient way to solve the problem with a quantum annealer and then compare your results to ensure they make sense.