Quantum Simulation on a Desk

 

Copyright: Sanjay Basu

Experimentation with my DGX Spark continues

A field report from a weekend of DGX Spark experimentation, written on the road to SC25

I will admit something upfront. Every time I sit down in front of the DGX Spark, I feel a little like I am getting away with something. Not in a criminal sense. More in the sense that this compact workstation is quietly doing the type of quantum simulation work that used to require a noisy rack somewhere in a cold data center. It feels a bit unfair, like owning a personal synchrotron that fits neatly next to a coffee cup.

My DGX experimentation continues, and this week I have been focused on quantum simulation libraries from NVIDIA. I spent the weekend running circuits across CUDA Quantum and the cuQuantum Appliance before heading to St. Louis for SC25. Nothing sharpens the mind like a few late-night qubit experiments followed by an early-morning conference flight.

This long-form edition is a reflection on that work. A guided tour of what it feels like to simulate 15 qubits, then 20, then 33, then slide gracefully into tensor network territory, all from a machine that sits on a desk. It is also a look at why this matters right now, and why the line between AI infrastructure and quantum simulation is beginning to blur in ways no one would have predicted five years ago.

So let me take you through the journey. Consider this a travel diary for a strange but thrilling weekend in the land of quantum circuits, GPU memory, and whatever magic is baked into the Grace Hopper architecture.

A quiet revolution under the desk

For most of its short history, quantum simulation lived in a rarefied world. You either wrote toy examples and ran them on a laptop, or you booked time on a large cluster with a reservation system that acted like a deeply suspicious bouncer. Anything interesting required real resources. Anything heavy came with a warning message, a wait time, and a budget.

Now we have DGX Spark. It is not huge. It is not noisy. It does not need its own air-conditioned room. Yet it comes with unified memory that tops out at 128 GB, the Grace Hopper architecture that ties CPU and GPU together in a coherent memory pool, and enough bandwidth to fling quantum states around like someone dragging files between spreadsheets.

This combination matters because quantum simulation is not a clever trick. It is raw linear algebra, and linear algebra is limited by memory. Memory defines your ceiling. Compute helps, but memory decides what fits and what falls on the floor.

The table below summarizes what state-vector simulation requires on any system, including mine.

State Vector Memory Requirements

Table: 1

This table says something simple. If you have 128 GB of unified memory, 33 qubits is your Everest. You can climb it, but you are not going beyond it without a different technique. It is an elegant reminder that quantum simulation is exponential growth turned into hardware pressure.

That is the background for everything I did this weekend.

A 15-qubit GHZ state that behaves exactly as it should

When testing a new quantum setup, start small. I began with a classic GHZ state across 15 qubits. The memory footprint is tiny. Around half a megabyte. A polite request rather than a challenge.

The CUDA Quantum kernel is short and direct. Allocate 15 qubits. Apply a Hadamard to the first one. Apply controlled X gates across the chain. Measure. Run. Inspect.

The output clusters around the two expected bitstrings:

• 000000000000000
• 111111111111111

It is an old friend. A textbook state. But it also marks the moment when the simulation environment reveals itself. The DGX Spark runs this instantly. I did not feel it ramp up. I did not hear fans. It behaved like I told it to add two numbers.

This is exactly how technology sneaks up on you. One day, GHZ circuits belong on HPC clusters. The next day, they run on your desk while you sip tea. A revolution dressed like a convenience.

This is also where CUDA Quantum shows its personality. The syntax feels designed for people who enjoy both Python and physics. It does not overwhelm with ceremony. It also does not hide the mechanics. And the backend toggle to “nvidia” tells the system to route operations to cuQuantum’s state-vector engine with no additional configuration.

Once the warmup is done, you move to something that actually tests the architecture.

The 15-qubit Quantum Fourier Transform

The QFT is one of those circuits that reveals how quickly quantum structure gets intricate. With controlled rotations, changing angles, nested loops, and swap networks, it feels like you are building a miniature machine.

The simulation, however, remains easy for the DGX Spark. The circuit loads, runs, and returns measurements before boredom can set in. The point is not complexity. The point is that realistic, structured circuits behave predictably.

It sets the stage for the real test.

The edge of classical simulatability

33 qubits

This is the moment I had been waiting for, because it attempts to allocate a 33-qubit state vector in unified memory. The math is merciless:

2^33 amplitudes

Each amplitude: 16 bytes

Total memory: around 128 GB

That is the entire unified memory pool on the DGX Spark. It is not a suggestion. It is a demand. The system either accepts it or falls over.

I wrote a simple 33-qubit kernel. Allocate qubits. Create superposition on the first one. Apply 32 controlled X gates. Measure.

Then I watched the memory readout. It climbed. It kept climbing. It took what it needed. And then it stopped. No errors. No warnings. No thrashing. The system behaved like someone who had practiced this many times.

This is where the architecture shows its value. Unified memory eliminates the messy dance between CPU and GPU, and the Grace Hopper design treats the entire memory pool as one coherent space. That is what makes 33 qubits possible on a machine this small.

The experience teaches an important lesson. If you want to do serious quantum simulation without renting time on a supercomputer, you need memory bandwidth, coherent memory, and a system that does not treat CPU and GPU as strangers. The DGX Spark checks all those boxes.

There is something satisfying about seeing a 33-qubit state vector run on such a compact machine. It is the kind of milestone every researcher remembers. Your first cluster job. Your first distributed training run. Your first large quantum simulation on local hardware. These mark turning points.

But of course, once you hit the 33-qubit wall, you begin looking for a way around it.

Beyond the wall

I am using tensor networks

If state-vector simulation is a brute force approach, tensor networks are more like calligraphy. They rely on structure, not scale. They exploit limited entanglement, locality, and patterns inside circuits that do not explode into full exponential form.

The Matrix Product State method in CUDA Quantum allows you to simulate circuits with 40 or more qubits as long as the entanglement remains manageable. This is ideal for scenarios where qubits interact mostly with neighbors, as in one-dimensional chains or lattice models.

Many real world quantum algorithms fall into this category. Variational quantum algorithms in chemistry. Feature map encodings. Some QAOA circuits. Systems from condensed matter theory. All can be handled by MPS if you shape the circuit the right way.

Here is the general rule:

• If the circuit entangles everything with everything, forget it.
• If the circuit entangles nearest neighbors, tensor networks shine.

The DGX Spark handles these simulations with plenty of room to spare. It is not pushing against memory limits. It moves gracefully through higher qubit counts.

This hybrid strategy, state vector up to 33 and tensor networks beyond that, defines a realistic workflow for quantum experimentation on a workstation.

Choosing the right backend

CUDA Quantum gives multiple backends, each tuned for a different purpose. Your choice influences memory use, accuracy, and speed.

Backend Comparison Table

Table: 2

This table hides an important truth. There is no single correct way to simulate quantum circuits. The landscape is a toolbox rather than a single hammer. A good quantum researcher learns which style of simulation belongs to which problem.

State vector for raw accuracy.

MPS for scale.

Density matrix for the real world.

The DGX Spark is flexible enough to run all of them without friction.

The practical side

Memory awareness

When circuits get large, memory becomes the silent villain. Exponential growth is not poetic. It is brutal. To prevent accidental system overload, I built a small helper function that calculates memory requirements before running a circuit. It reads like a pre-flight checklist.

Inputs: number of qubits.

Outputs: required memory, available memory, and a feasibility status.

This avoids unpleasant surprises. It also builds intuition. After a few runs, you develop a natural sense for what fits and what should be handed to a tensor network backend instead.

That is how quantum simulation should feel. Not guesswork. Not mysticism. Just engineering.

Batch processing and parameter sweeps

Many quantum algorithms require running the same circuit with different parameters. Variational algorithms, for example, rely on sweeping angles and recording the energy for each configuration.

The DGX Spark is excellent at this type of workload. Because it combines GPU acceleration with large coherent memory, it runs batch experiments without crossing into memory exhaustion.

Parameterized circuits with 20 or more sweeps become routine. Each run completes quickly. The workflow feels familiar to anyone who has trained neural networks or run hyperparameter tuning.

Quantum research begins to resemble ML research more than physics. This is not an accident. The two fields are converging.

Quantum chemistry

This is where simulation proves its worth

The most compelling early quantum applications live in chemistry. Molecular Hamiltonians, VQE routines, ground state estimations. These tasks do not require hundreds of qubits. They require accuracy, speed, and iteration.

A DGX Spark can run these workloads comfortably. CUDA Quantum integrates with spin operators, enabling Hamiltonians for small molecules. The variational loop produces ground state estimations quickly.

These experiments teach you something important. The short-term future of quantum computing is hybrid. Not one or the other. A careful duet between classical compute and quantum structure.

When you simulate these algorithms locally, you understand the rhythm of that duet.

Quantum machine learning

This is where physics meets feature maps!

One of the more exciting directions in QML is angle embedding. You take classical data and encode it into quantum states by mapping values to rotation angles. The idea is to treat quantum circuits as feature transformers that reshape the data landscape.

Simulating these circuits requires at least dozens of qubits if you want real dimensionality. That is where the DGX Spark shines. You can encode 20 features, entangle them with controlled operations, sample the outputs, and repeat the process during training without struggling for resources.

QML becomes practical. Not theoretical.

And that is the key. Quantum simulation is no longer a “look how cool this is” gimmick. It is a tool for designing new computational layers that enrich classical models.

Optimization and QAOA

Optimization problems sit at the heart of industry. Scheduling, logistics, finance, graph theory. QAOA tries to solve these using a combination of quantum operators and classical search.

Simulating QAOA circuits is often the first step before running them on real quantum hardware. A DGX Spark handles these small to medium sized circuits easily. Three node or six node MaxCut circuits run smoothly through multiple layers.

This is not only useful for research. It is practical for organizations exploring quantum readiness. You can build pilot studies on local hardware without purchasing cloud quantum credits.

Troubleshooting lessons from the weekend

I hit a few bumps along the way, which is expected.

Memory issues appear instantly when you cross the 33-qubit limit. The solution is simple. Move to tensor networks or reduce qubits. Performance issues sometimes show up when drivers lag behind the cuQuantum version. Updating CUDA resolves this.

Installation issues arise when Python dependencies collide. A clean virtual environment solves almost everything. Quantum simulation is not fragile. It just rewards cleanliness.

The troubleshooting section of my ResearchGate guide covers all of these in more detail, but the short version is that DGX Spark is predictable. When something fails, it fails for a good reason, not a mysterious one.

https://www.researchgate.net/publication/397658119_Quantum_Circuit_Simulation_on_NVIDIA_DGX_Spark

https://doi.org/10.13140/RG.2.2.30028.07042

Why this matters right now

Quantum hardware is still early. The big breakthroughs are coming, but they have not arrived. What has arrived, however, is simulation power that changes the pace of research.

You no longer need a reserved cluster to do serious quantum work. You no longer need to book cloud credits. A workstation can take you surprisingly far.

This changes who can experiment.

This changes how fast people can iterate.

This changes what students can learn.

This changes how startups can operate.

There is an important parallel here with the early days of deep learning. At first, training large models required cluster access. Then GPUs arrived, and the field exploded with creativity. Researchers could try new ideas daily rather than monthly.

Quantum simulation is entering that same phase. The friction is dropping. The hardware is accessible. The software is maturing. And the form factor fits into everyday research life.

This is why I keep returning to the DGX Spark. It is not only a machine. It is a permission slip.

A final reflection before SC25

I packed my bags after the weekend experiments, shut down my notebook, and headed to the airport for SC25 in St. Louis. On the flight, I kept thinking about how quantum simulation no longer feels exotic. It feels like a normal part of my workflow. A small but meaningful shift.

There is something liberating about knowing that I can test 33-qubit circuits without thinking twice. There is something satisfying about pushing tensor network simulations beyond 40 qubits without drama. And there is something energizing about seeing the line between AI infrastructure and quantum algorithms begin to dissolve.

We are entering a period where researchers will bounce between neural networks, quantum circuits, CUDA kernels, and tensor networks in a single notebook. That is the shape of things to come.

The DGX Spark makes that world feel closer.

And that is why I spent my weekend running quantum experiments before catching a flight to SC25. Because the future of computing is not waiting politely in the corner. It is already here, tapping us on the shoulder.

Copyright: Sanjay Basu