Can quantum computing re-route our supply chain issues
Quantum software development firm Classiq thinks so
What if there was a solution that could optimize shipping routes in the face of changing conditions, discover new materials, and determine option pricing quickly and accurately? As quantum computing continues to advance and become more available to global markets, it has the potential to offer significant ROI and solutions for supply chain professionals especially.
Quantum software development company, Classiq says that it can even solve problems in the case of the Suez Canal blockade earlier this year.
Quantum computers can solve this class of problem
“Global shipping companies had to scramble when the Suez Canal was recently blocked. Quantum computers can solve this class of problem (called ‘the traveling salesperson problem’) very efficiently, and much quicker than a classical computer can,” according to Yuval Boger, chief marketing officer at Classiq.
Through quantum computing, Boger says shippers can quickly determine optimal shipping sequences including which route is fastest, which is most cost-effective, which has the least environmental impact, and even how these might change during the day with changing traffic or weather conditions.
EP&T conducted the following Q&A interview with Yuval Boger at Classiq on a number of subjects relating to quantum computing, and its role in quantum software development.
Q. Detail the benefits of quantum computing in supply chain management, as it relates to electronics component distribution channels in North America.
Quantum computers are particularly adept at running many simultaneous calculations. This is in contrast with classical computers that can run one calculation at a time.
Furthermore, the number of simultaneous calculations grows exponentially. For instance, if a quantum computer can perform a thousand simultaneous calculations, then a quantum computer that is twice as large will be able to perform a million simultaneous calculations.
This capability lends itself particularly well to optimization problems, because optimization is about picking the best option from many alternatives. Generally, the more alternatives you can explore, the better the solution will be. Additionally, because quantum computers explore all these alternatives concurrently, quantum computing could potentially discover the best alternative faster than when a classical computer considers them sequentially. The speed of arriving at the best answer may hold considerable value in reacting quickly to changing market conditions.
An example of such an optimization problem is what is often referred to as the ‘traveling salesperson problem’. This is a class of problems dealing with finding the optimal sequence of stops on a multi-route journey. It’s called ‘traveling salesperson’ because one incarnation of this problem is a salesperson that needs to make multiple customer stops and tries to figure out which customer to visit first, second, etc. Of course, a multi-stop route for a delivery truck is also such a problem. The definition of ‘optimal’ could also vary from time to time. For some, it might be the route that minimizes the time to complete the route. For others, it might be minimal cost taking into account fuel and tolls. Quantum computers can be very helpful in solving such problems.
Q. How can quantum computing optimize challenges faced by electronics distribution in relation to changing and unpredictable supply chain conditions.
Facing unpredictable supply chain conditions, distributors might want to optimize their inventory levels. A distributor wants to have enough inventory to satisfy demand, but at the same time, avoid carrying excess inventory that could become obsolete or otherwise spoiled. Sometimes, these problems are solved by Monte Carlo simulations. For instance, a distributor might expect a weekly shipment of parts from their suppliers, but the exact quantity of parts might differ from week-to-week because of supply chain issues. Monte Carlo allows making certain assumptions about the distribution function – how many parts will be received every week – and then compare this with expected demand from customers. Such optimizations can help avoid stock-out conditions while optimizing cash flow.
Q. What are some of the existing limitations exhibited by laborious gate-level component design today. And, how can quantum algorithms eliminate these traditional roadblocks.
Quantum computers need quantum software algorithms to work. Hardware is useless without software, and the problem is that today’s quantum software development is very limiting.
Today’s software development is done at the gate 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 forth. This may work OK when you have 5 or 10 qubits, but if you have 100 or 1000 qubits, you’ll find it impossible to design and debug circuits this way.
This is where Classiq comes in. We looked at how CPUs and other chips with millions or billions of transistors and logical gates are designed, and applied the same principles to quantum. The user defines 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 they care about, just like one would do when programming an FPGA. Our software platform then synthesizes, within seconds, a quantum circuit. This circuit performs what the programmer asked for and meets the constraints.
This is revolutionary for many reasons: First, it allows building new algorithms with ease. One can focus on the function of the quantum program as opposed to how the quantum circuit is built. Second, it’s a huge time saver. Redoing a quantum circuit could take weeks. Now it takes minutes. Third, it scales. One can easily generate circuits that take advantage of that larger qubit count that’s coming. Fourth, you no longer have to hire a PhD in quantum information science to build a quantum circuit.