Quantum Computing, Seen Through the Eyes of an Old IT Guy (Part 6)

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A Hybrid Workflow for Inventory & Logistics (Palantir + Quantum Computer)

Why Logistics? — How Big is the Logistics Market?

Logistics costs represent 10–15% of global GDP on average, though this percentage varies significantly by country efficiency.

Wow! post number six already. I started this casually, but every new thing I learned stacked up more things to learn and chasing each new question led me here.

Everything started from one question: “What can we actually DO with a quantum computer?”

YouTube, blogs, they mostly tell the same story. RSA will be broken, crypto collapses, finance issues….

Really? Will it pop up one day and solve everything we couldn’t? (Someday, maybe) Rather than vague faith, I started with reasonable doubt.

• Is a quantum computer almighty? No.
• Does it replace classical computers? No.
• Is it efficient? Sometimes yes, sometimes no.
• Is it completed? No.

So, I shifted my thinking. Instead of envisioning a single quantum computer doing everything, I first identified areas where traditional computers and AI were already proving effective, and considered areas where quantum computing could support to achieve even greater results.

That is when Palantir came to mind. I often watched Palantir’s AIPCon on YouTube. At AIPCon, Palantir clients raise their issue and even present improvement cases with actual figures. (https://www.palantir.com/aipcon/)

So the topic of this post became:

Hybrid Workflow for Inventory & Logistics (Palantir + Quantum Computer)

I kept a note next to my desk and repeatedly recited:

• Avoid quantum hype like “quantum computers are about to revolutionize logistics.”
• No vague claims like “it will replace classical computers.”
• Distinguish between what is actually applied in reality and what remains in the research stage.

1. The problem: why are inventory and logistics issues still unsolved?

Let’s consider a simple scenario. Suddenly, the flu breaks out in a certain region. Fever reducers and vaccines run out in an instant at local pharmacies and hospitals. However, in a warehouse in the city right next door, those same items are piled high. It is a situation where supplies are unavailable where they are needed, yet overflowing where they are not. This phenomenon occurs anywhere in the world.

1.1 The field is similar everywhere

Whether it is a hospital or a large supermarket, faced similar issues.

• Response systems cannot keep up with sudden demand spikes (heat waves, typhoons, concerts, infectious diseases, etc.).
• Restocking runs on fixed cycles, so a sudden local surge slips through.
• Some places sell out while others sit on excess stock, and it happens in neighboring areas at the same time.

In reality, global supply chains are riddled with unpredictable variables such as weather, geopolitical conflicts, regional events, and accidents. Consequently, a vicious cycle repeats where one side cannot sell due to a lack of goods (revenue loss), while another side incurs only warehousing costs due to leftover inventory (inventory burden).

1.2 Why can’t it be solved with classical computers?

AI is good at prediction. “This store will probably sell N fever reducers tomorrow”

However, “What is the cheapest and fastest way to ship which item from 200 warehouses and 500 stores nationwide, loaded onto which truck, and sent via which route?” is a completely different problem.

That’s a combinatorial problem. And combinatorial problems explode as the options grow.

A courier can find the shortest route through 5 stops by hand. Make it 20 stops and the possibilities balloon so astronomically that even the fastest supercomputer can’t enumerate them all. Think of seating 300 employees so that everyone is happy. Same flavor.

This is called combinatorial optimization. Once variables (stores, items, trucks, weather, events) start to ‘entangle’ with each other, it crosses into a realm that traditional computers cannot handle. It is a problem where the amount of computation explodes uncontrollably as the number of choices increases, and this is called NP-Hard.

(Entangled, I said? Then surely the QC will be good at entanglement, right? lol)

2. What Palantir actually does

What I want to cover here is grounded in things that are already commercialized and running in the field.

Palantir Foundry is Control Tower software. It pulls scattered data (warehouse inventory, store sales, truck locations, weather, traffic, ERP systems) into one place and replicates the company’s supply chain inside the computer, building a Digital Twin.

Core of Palantir: the Ontology. ontology is a platform that organizes the relationships between stores, warehouses, trucks, and personnel in a way that is easy for computers to understand. Thanks to it, managers can monitor in real-time what is happening where and what the problems are, enabling them to respond immediately. In the past, situations would arise where a problem arose and you had to contact A, who would pass the message to B, who would pass it to C, who would pass it to D, and finally A would receive the call. Considering this, you can imagine just how much more efficient operations can become thanks to this ontology.

For real case studies, the AIPCon (https://www.palantir.com/aipcon/) is worth to read.

Palantir platform is very good at knowing ‘what’s happening now and how much we’ll need tomorrow.’ But that combinatorial question is hard to answer with the Palantir alone.

What if we made exactly that spot more efficient with a quantum computer? That’s the core idea of this post. I would call it as Quantum Assist, maybe?

3. The Hybrid Workflow: Let’s divide Who is good at What

Palantir’s strengths are clear. So I sketched out stages in my notebook, dividing up the rest.

• Phase 0: Should this problem be sent to the quantum computer? (Triage)
• Phase 1: Digital Twin (Palantir)
• Phase 2: Translate business rules into a math problem
• Phase 3: Compute the optimal combination on a quantum computer
• Phase 4: Turn the answer back into field orders, execute and feedback

Let’s keep running with the flu scenario.

Phase 0. Send it to the quantum computer? (The triage gate)

Throwing every problem to quantum computer is wildly inefficient. Let classical computers keep doing what they are good at and send only the problems quantum computer is genuinely better at.

• Complexity triage: score how many variables and constraints are involved, and split classical problems from quantum problems.
• Urgency triage: sort by time pressure. (Emergency hospital meds and disaster relief on one side, things that can wait on the other.)

Phase 1. Digital twin: replicate reality in the computer (Palantir Foundry)

Pull all the scattered data together, build a digital twin of the real supply chain, and forecast the risks ahead.

• Data integration: ERP (SAP, Oracle, etc.), IoT sensors, warehouse management systems (WMS), external data (weather, traffic, events), and retail data (POS), all in one place.
• Demand forecasting: AI predicts ‘fever-reducer demand in this region is about to spike’ ahead of time, and sets the safety stock to keep in reserve.
• Constraint mapping: truck load limits, drivers’ legal working hours, cold-storage capacity, warehouse space. Real-world rules turned into numbers.

Phase 2. Quantum translation: turn business rules into a math problem (QUBO)

This is the hard part to grasp, so let me try an analogy. Think of the quantum computer as a foreign genius who doesn’t understand a word of your language. However brilliant, it can only solve the problem if you translate it into the one language it knows.

So the constraints and objectives Palantir mapped out in Phase 1 (minimize logistics cost, opportunity cost, transfer cost) get converted into a math problem the quantum computer understands. The format of that math problem is called QUBO. (Quadratic Unconstrained Binary Optimization)

This translation is the classical computer’s job. (Generating the QUBO.)

Phase 3. Quantum optimization: hand the combinatorial work to the quantum computer

The combinatorial problem that overwhelms classical computers gets passed to the quantum side.

• The quantum computer holds its qubits in superposition and sweeps huge numbers of candidate combinations at once. (QC has noisy and limits still)

The realistic options today:

• Quantum annealing: purpose built hardware (the D-Wave family) that takes the QUBO and hunts for the lowest cost point. The most practical option you can use at this moment.
• QAOA: Gatebased NISQ machines, the classical and quantum computers pass values back and forth, refining the answer step by step. (The one from Part 4.)

The key: the answer comes out of a hybrid setup where the classical computer compensates for the quantum computer’s limits, side by side.

Phase 4. Execute and Feedback: turn the math answer into field orders

What the quantum computer returns is, in the end, a mathematical result in 0s and 1s. It has to be translated back into orders a human can act on.

• Translation: say the optimization comes back as ‘move 500 boxes of vaccine from warehouse A to hospital B on truck 3 via route C.’ That gets mapped onto ontology objects.
• Simulation: before executing for real, run it once inside Palantir and verify it’s safe.
• Auto execution: once verified, it hooks into GPS apps, ERP Transfer Orders, and so on, and executes immediately.

And when the whole loop completes, it feeds back into Phase 1. The Inventory decision ‘move stock to the neighboring store’ flows into the Logistics question ‘which truck, which route’, tying inventory flow and logistics flow into one CLOSED LOOP. From an Supply Chain Management point of view, it can’t be separated anyway.

At a glance

Palantir Foundry (AI + Ontology)

• Good at: data integration, situational understanding, demand forecasting, execution control
• Task: What’s happening? How much will we need tomorrow?
• Limit: computation time explodes as variables grow (combinatorial explosion)
• Role: hands the quantum computer a cleanly prepared QUBO

Quantum Computer

• Good at: computing optimal combinations under brutally complex constraints
• Task: Of millions of combinations, which is best?
• Limit: knows nothing about business meaning or field context
• Role: returns the optimal answer

One side understands reality; the other side does the math. Each fills in what the other lacks.

Expected ROI

• Lower transport costs: real time optimization of store to store stock swaps and cross docking (loading straight onto the next truck without warehousing) cuts transport spend. The optimal combinations and routes matter, of course.
• Fewer stockouts, protected revenue: when events or extreme weather hit, redistribute inventory across hospitals, pharmacies, warehouses, and stores nationwide in real time, so ‘needed but unavailable’ stops costing sales.
• Less waste: put shelf life itself into the math problem for fresh food and vaccines, so near expiry goods ship first along optimal routes, and less gets thrown away.

There would be plenty of other benefits too.

Phase 1, 2, and 4 already run in the real world. That’s what Palantir actually does. The real question is Phase 3: can quantum computers beat classical ones on genuinely industrial scale problems? That part is still developing. Still the quantum warring states era.

My own conclusion isn’t “quantum computers will revolutionize logistics.” It’s “let each side cover the other’s weaknesses and grow together.” That, I think, is the most realistic picture at this point in time.

Some say it will take 20 years. Some say soon.

Nobody knows the future, but right now there are real machines you can run, methodologies being validated, and the world’s brightest people pushing forward one step at a time.

A chance encounter got me studying quantum computing, and I feel a quiet pride in having written this series. It feels like I’ve actually accomplished something, and that feels good.

I haven’t been sleeping well lately, but tonight, for the first time in a while, I think I’ll sleep deeply.

References (there was so much I didn’t know that I leaned on a lot of material. I couldn’t understand every line, but I tried hard to grasp the context.)

• IonQ and Einride partnership: https://www.ionq.com/news/einride-and-ionq-partnership-uses-quantum-computing-to-optimize-the-logistics-of-electric-and-autonomous-freight
• Palantir Foundry Supply Chain offering: https://www.palantir.com/offerings/supply-chain
• Palantir consumer-goods ERP examples: https://www.palantir.com/docs/foundry/use-case-examples
• Palantir case studies: https://unit8.com/resources/palantir-foundry-case-studies-by-unit8/
• Quantum Computing in Logistics and Supply Chain: arXiv 2402.17520
• Plus many more QUBO papers