EM Phone Locator (working title)

Inspiration

Everyone has torn apart a couch looking for a phone. Tools like Apple's Find My solve part of the problem, but they depend on the lost device reporting its own location through GPS, Wi-Fi, or Bluetooth beacons. If the phone can't do that, the trail goes cold. We noticed that a powered-on phone constantly emits electromagnetic energy whether or not it is trying to be found, and asked a simple question: what if we located the phone by sensing it instead of waiting for it to report in?

DuQuantum gave us the perfect excuse to push that idea further. Localization is at heart an inference problem: turning noisy field measurements into a position estimate. We wanted to see where a quantum algorithm could fit into that pipeline. [TODO: personal story, e.g. the moment someone on the team lost a phone, or why the [TODO: sponsor] challenge spoke to you]

What it does

[TODO: Project name] is a phone locator in the spirit of Find My, built around physical sensing:

• Senses electromagnetic emissions. A sensor picks up the electromagnetic field coming from the phone. [TODO: what kind of signal (RF band, magnetic field, etc.) and what the sensor measures (strength, direction, frequency)]
• Finds every device on your account. Instead of hunting for one phone, it discovers the other devices linked to the same account, so you can see and locate all of them in one place. [TODO: how linking works (account login, pairing, shared ID) and what the user sees]
• Uses a quantum algorithm to locate. Sensor readings go to a quantum algorithm that [TODO: what the algorithm does, e.g. estimates position, matches signal fingerprints, optimizes the search]. Its output is [TODO: a location, distance estimate, heat map, ranked list…].

[TODO: one or two sentences describing the demo from the user's point of view: open the app, pick a device, see X]

How we built it

1. Sensing pipeline

[TODO: sensor hardware (model/part), how it connects (microcontroller, laptop, phone), sampling rate, and how raw readings are filtered and cleaned before use. Include a photo of the setup if possible.]

2. Account-linked device discovery

[TODO: how the app identifies devices on the same account: backend/database, authentication, device registration flow, and how a sensed signal gets tied to a specific device.]

3. Quantum algorithm

[TODO: name the algorithm (e.g. QAOA, VQE, Grover-style search, a quantum ML model) and the SDK/platform (e.g. Qiskit, Pasqal Pulser, Perceval, Classiq, BlueQubit). Explain how the localization problem is encoded (qubits, cost function/Hamiltonian, circuit depth), whether it ran on a simulator or real hardware, and the number of qubits/shots used.]

4. Classical baseline comparison

To be honest about where quantum helps, we compared the quantum approach to a classical baseline on the same data. [TODO: classical method (e.g. least-squares trilateration, brute-force search, a classical optimizer), the metric (localization error, runtime, success rate), and the actual results. Add a plot or table. Report real numbers only.]

Challenges we ran into

• [TODO: sensing challenge, e.g. noise from other electronics, separating the target phone's signal from the background]
• [TODO: quantum challenge, e.g. encoding the problem in few enough qubits, simulator limits, noise, convergence]
• [TODO: integration challenge, e.g. connecting hardware, backend, and quantum code within 24 hours]

Accomplishments that we're proud of

• Built an end-to-end pipeline, from physical sensor to quantum algorithm to a located device, in 24 hours. [TODO: confirm it ran end to end]
• [TODO: a concrete result, e.g. "located the phone within ___ in our tests." Use the real measured number only.]
• [TODO: something about the quantum-vs-classical comparison or a technical first for the team]

What we learned

• [TODO: what you learned about electromagnetic sensing and how messy real-world signals are]
• [TODO: what you learned about the quantum SDK/algorithm and how to map a real problem onto qubits]
• [TODO: a teamwork or scoping lesson from building under a 24-hour deadline]

What's next for [TODO: Project name]

Our prototype uses [TODO: current sensor]. The bigger opportunity is in quantum sensors, which can detect much weaker fields than classical ones. These are directions we'd like to explore. They have not been tested with our project:

• Rydberg-atom RF sensors for direction finding. NIST researchers used Rydberg atoms in a vapor cell to find the angle-of-arrival of an RF source (NIST, 2021). A later design achieved 1.7° angular resolution over 0–60° at 4.2–5.7 GHz (Applied Physics Letters, 2025). In principle, a sensor like this could point toward a phone's radio emissions directly.
• NV-center diamond magnetometers. These quantum sensors work at room temperature (Rev. Mod. Phys. review), and portable versions have reached roughly 400 pT/√Hz in field deployments (arXiv:2509.19179). That could make a handheld, high-sensitivity version of our sensor possible.
• Field-proven quantum magnetic positioning. Quantum magnetometers have already delivered GPS-free navigation in real flight and ground trials (Q-CTRL, arXiv:2504.08167). That is evidence that quantum sensing for localization can work outside the lab, though at a very different scale than finding a phone.
• [TODO: software next steps, e.g. running on real quantum hardware, scaling to more devices, a mobile app]

How we used Cursor and Grok Bot

(Required for the MLH "Build Your Hackathon A-Team with SpaceXSI" raffle. Opt in when you submit.)

• Cursor: [TODO: how Cursor was used as the core AI coding agent, e.g. which parts of the code it helped write or debug]
• Grok Bot: [TODO: which automation Grok Bot handled in the workflow, e.g. research, drafting docs, task automation]
• What we wrote ourselves: [TODO: clearly separate team-written work from AI-generated work]

Built With

[TODO: confirm/edit] python · [TODO: quantum SDK, e.g. qiskit / pulser / perceval / classiq] · [TODO: sensor hardware] · [TODO: microcontroller, if any] · [TODO: backend/database] · [TODO: frontend framework] · [TODO: other libraries, e.g. numpy, scipy, matplotlib] · cursor · grok-bot

Try it out: [TODO: GitHub repo link] · [TODO: demo video link]

Built With

• python