This is a joint project by me and Ellen. It was inspired by a QxQ summer camp we both attended in 2023. Initially, it was aimed to develop a portable Quantum Computing simulator for educational purposes using the Jetson Orin Nano for its GPU acceleration, portability, and low power consumption (15-60w). We then discovered the Jetson Orin Nano’s efficiency in simulating more qubits compared to traditional GPUs (220-450w) like RTX3070, leading us to create a cost-effective, low-power quantum simulation device for researchers and faculty.

Memory Capacity Requirement for Qubits

In our research into the interplay between GPU capabilities and quantum simulators, we learned of a crucial relationship: a GPU’s internal memory size determines the maximum number of qubits it can simulate in a quantum computing scenario. This is governed by the equation for an N-qubit circuit:

Memory required (bytes) = 8 x 2^N

Both the Jetson Orin Nano and the RTX3070, with their 8GB of GPU memory, can theoretically simulate up to 29 qubits. What’s remarkable is the vast difference in power consumption between the two: the Jetson Orin Nano uses a mere 7-15 watts compared to the RTX3070’s 220 watts, yet both can simulate an equal number of qubits.

Jetson Orin’s Low Power High Qubit Capacity

Our findings, based on Nvidia’s documentation, are summarized in the table below:

This comparison highlights the Jetson Orin series’ capability to simulate a large number of qubits while consuming significantly less power than other high-end gaming or professional GPUs. It’s worth noting that the Orin devices are standalone computers, whereas other GPUs require installation in desktops or workstations, potentially increasing the overall power consumption and cost.

Comparison Test: Jetson Orin Nano vs RTX 3070

We are able to conduct a test to compare power consumption / qubit count for Jetson Orin Nano vs RTX 3070. We chose to install Qibo quantum simulator and ran the Grover models from its examples for both platform. Although both Jetson Orin Nano and RTX 3070 are equipped with 8G GPU memory and would theoretically be able to simulate up to 29 qubits, the Grover models can each only simulate 25 qubits. When we attempted to simulate 26 or more, program reports “Out of memory” error, indicating GPU memory is not sufficient.

We recorded temperature, power, memory usage, duration, and calculate total energy consumption for the run.

	Jetson Orin Nano	RTX 3070
Temperature (°C)	55	60
Power (Watts)	6.1	109
Memory (MB)	4173	4257
Duration (Seconds)	740	94
Energy Cost (Watt-Seconds)	4514	10246
Energy Cost per Qubit (Grover Algorithm with 25 qubits)	181	410

Based on the above test result we have concluded:

• Energy Efficiency: The Jetson Orin Nano appears to be more energy-efficient in running the Grover Quantum Computing algorithm for 25 qubits. This is evident by the lower energy cost (watt-seconds) of 4514 for the Jetson Orin Nano, compared to 10246 for the RTX 3070.
• Power Consumption: The RTX 3070 has a much higher power consumption rate at 109 watts versus the 6.1 watts of the Jetson Orin Nano. This is almost 18 times higher, which significantly contributes to the increased energy cost.
• Performance Time: However, it’s essential to note that the RTX 3070 completes the task much faster, taking only 94 seconds compared to 740 seconds for the Jetson Orin Nano. This suggests that while the RTX 3070 is less energy-efficient, it performs the computation much more quickly.
• Temperature: The RTX 3070 operates at a higher temperature (60°C compared to 55°C for the Jetson Orin Nano) which is consistent with its higher power usage.
• Memory Usage: Memory usage is almost the same for both devices, with the RTX 3070 using slightly more memory (4257 MB) than the Jetson Orin Nano (4173 MB).
• Energy Cost per Qubit: When it comes to the energy cost per qubit, the Jetson Orin Nano is more efficient at 181 watt-seconds per qubit, as opposed to the 410 watt-seconds per qubit of the RTX 3070.

From this comparison, we can infer that if energy consumption and cost are a concern, the Jetson Orin Nano is the more efficient option. However, if time performance is critical and power availability isn’t a limitation, the RTX 3070 may be the preferable choice despite its higher energy consumption. This could be relevant in scenarios where speed is more crucial than energy efficiency, such as time-sensitive computations or environments where energy cost is less of a factor.

Jetson AGX Orin Provides A Well Balance between Energy Cost and Computation Performance

While energy efficiency is very promising for Jetson Orin Nano, its lousy computation performance is of a concern. But here is the catch. Jetson Orin series has an extreme high performance variant, Jetson AGX Orin (see below), which provides a staggering 64G GPU memory with only 60w power consumption. We cannot afford one to examine its energy cost per qubit along with its computation performance, but it would be safe bet that AGX Orin likely would performance 10 times better than Orin Nano considering its much strong specifications.

Quantum On Edge: Is This A Real Thing?

One thing we didn’t mention much about the Jetson Orin is its tiny form factor compared to the traditional GPU card. In fact, the Jetson Orin itself is a computer whereas a traditional GPU card must be inserted into a bulky computer, resulting in even more energy efficiency with Jetson Orin series devices. Additionally, Jetson Orin is particularly aimed for portable IoT or Edge computing applications. While there is a qubit limitation (up to 33) for the Jetson Orin compared to the real quantum computer (hundreds and still evolving), there might be a place for small qubit count IoT applications, or shall we call it Quantum On Edge?