Can Quantum Computing Reorient Our Supply Chain Problems

What if there was a solution that could optimize shipping routes for changing conditions, discover new materials, and price options quickly and accurately? As quantum computing continues to advance and becomes more and more available in global markets, it has the potential to deliver significant return on investment and solutions for supply chain professionals in particular.

Quantum software development company Classiq says it can even solve problems in the case of the Suez Canal blockade earlier this year.

Quantum computers can solve this class of problems

“Global shipping lines had to scramble when the Suez Canal was recently blocked. Quantum computers can solve this type of problem (called the “roaming vendor problem”) very efficiently and much faster than a conventional computer, ”according to Yuval Boger, Marketing Director at Classiq.

Using quantum computing, Boger says shippers can quickly determine optimal shipping sequences, including the fastest, most cost-effective route, least environmental impact, and even how these might change. during the day with changing traffic or weather conditions.

Source: Classiq

EP&T conducted the following question-and-answer interview with Yuval Boger at Classiq on a number of topics related to quantum computing and its role in the development of quantum software.

Q. Detail the benefits of quantum computing in supply chain management, with respect to electronic component distribution channels in North America.

Quantum computers are particularly adept at performing many calculations simultaneously. This contrasts with conventional computers which can perform one calculation at a time.

In addition, the number of simultaneous calculations is growing exponentially. For example, if a quantum computer can perform a thousand calculations simultaneously, then a quantum computer twice the size will be able to perform a million calculations simultaneously.

This ability lends itself particularly well to optimization problems, because optimization is about choosing the best option among many alternatives. Generally, the more alternatives you can explore, the better the solution will be. Moreover, as quantum computers explore all of these alternatives simultaneously, quantum computing could potentially discover the best alternative faster than when a classical computer considers them sequentially. How quickly you get the best response can be very helpful in reacting quickly to changing market conditions.

An example of such an optimization problem is what is often referred to as the “roaming vendor problem”. This is a category of problems dealing with finding the optimal sequence of stops on a multi-route trip. This is called a “traveling salesperson” because an embodiment of this problem is a salesperson who has to make multiple customer stops and tries to determine which customer to visit first, second, and so on. Of course, a hop-on hop-off route for a delivery truck is such a problem as well. The definition of “optimal” could also vary from time to time. For some, this may be the route that minimizes the time needed to complete the route. For others, it may be a minimal cost considering fuel and tolls. Quantum computers can be of great help in solving such problems.

Q. How can quantum computing optimize the challenges faced by electronic distribution in the face of changing and unpredictable supply chain conditions.

Faced with unpredictable supply chain conditions, distributors may want to optimize their inventory levels. A distributor wants to have enough inventory to meet demand, but at the same time to avoid carrying excess inventory that could become obsolete or otherwise wasted. Sometimes these problems are solved by Monte Carlo simulations. For example, a distributor may expect weekly parts delivery from their suppliers, but the exact quantity of parts may differ from week to week due to supply chain issues. Monte Carlo allows you to make some assumptions about the distribution function – how many coins will be received each week – and then compare that with the expected demand from customers. Such optimizations can help avoid out-of-stock conditions while optimizing cash flow.

Q. What are some of the existing limitations presented by the laborious design of door-level components today. And how can quantum algorithms remove these traditional barriers.

Quantum computers need quantum software algorithms to function. Hardware is useless without software, and the problem is that quantum software development today is very limited.

Today’s software development is done at the door level. Quantum computers have bits (called qubits – quantum bits) and gates, similar to logic gates. The programmer specifies which qubit connects to which quantum gate and so on. It may work fine when you have 5 or 10 qubits, but if you have 100 or 1000 qubits, you will not be able to design and debug circuits that way.

This is where Classiq comes in. We looked at how processors and other chips with millions or billions of transistors and logic gates are designed and applied the same principles to quantum. The user sets a high level model of what they want the circuit to do, similar to a VHDL or Verilog model in electronics. The user also specifies the constraints that interest him, as one would do when programming an FPGA. Our software platform then synthesizes, in a few seconds, a quantum circuit. This circuit performs what the programmer requested and respects the constraints.

This is revolutionary for many reasons: First, it makes it easy to create new algorithms. One can focus on the function of the quantum program as opposed to how the quantum circuit is constructed. Second, it’s a huge time saver. Redoing a quantum circuit could take weeks. Now it takes a few minutes. Third, it evolves. We can easily generate circuits that take advantage of this larger number of qubits to come. Fourth, you no longer need to hire a PhD in Quantum Information Science to build a quantum circuit.

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