Understanding Quantum Sensing Networks
An overview of quantum sensing networks, explaining how quantum technologies enable ultra-precise measurements and introduce new security capabilities for building secure, next-generation sensor networks.
Quantum Sensing Networks
An important frontier in quantum technologies is quantum sensing: using quantum phenomena to build better sensors and detectors. The term “better” here is flexible, and can mean any number of things: higher resolution, faster sample rate, better accuracy, lower energy requirements, cheaper construction, easier deployment, and so on.
However, sensors are of little value without anyone reading their results. In particular, remote sensors, as well as networks of many disparate sensors, are better facilitated with a network to share these results. As quantum technologies furnish better detectors, they also increase our motivation to defend ourselves against quantum adversaries. This is a natural application of Tectonic’s post-quantum blockchain. To facilitate this, we are working on integrating quantum sensors into our programmability layer, with our sights set on delivering the world’s first quantum IoT.
Quantum sensors leverage quantum phenomena to improve the accuracy of a variety of measurements. Typically, they do not replace existing sensors. Rather, they enable us to measure additional phenomena and regimes. In this post, we will describe two such technologies, one relating to magnetic fields and the other to electrical fields.
Quantum Magnetic Sensing
Quantum magnetic sensors provide important improvements over classical sensors. They can operate on much finer spatial resolutions, allowing measurements of objects as small as single molecules. More impressively, they work at room temperature. For comparison, SQUID sensors can provide comparable sensitivity, but their spatial resolution is at least three orders of magnitude higher, and, more crucially, they must be cryogenically cooled to below -190 °C to operate. Finally, quantum sensors can measure various properties of magnetic fields, allowing us to see their “texture” rather than just their intensity.
That is not to say that quantum magnetic sensors make classical sensors obsolete. Classical sensors are much better at measuring a weak magnetic field dispersed over a large volume (at least on the order of centimeters). But when it comes to localized phenomena, quantum sensors are unparalleled.
A common technique for quantum magnetic sensing is based on imperfections in diamonds. A perfect diamond is made of a lattice of carbon atoms. It is possible that some carbon atoms are replaced with other atoms or are missing entirely. In some cases, there are small regions that look like a perfect diamond except for two adjacent atoms: one is replaced with a nitrogen atom, while the other is missing. We call such constructions nitrogen-vacancy (NV) centers. NV centers were originally sought in natural diamonds, but today we can engineer them in laboratories.
The physics is a bit hairy, but the big picture is that the lattice structure holds all the atoms at fixed distances. The vacancy creates a cavity for the additional free (valence) nitrogen electron to behave in complicated ways that are highly sensitive to magnetic fields. The diamond lattice holds everything at fixed, molecular-scale distances, sufficiently amplifying a phenomenon called the Zeeman effect to allow practical measurement, even at room temperature.
Applied NV-based magnetometry has been around for more than 15 years, and the applications are varied. They are used for nanoscale imaging, detecting bombs, non-GPS positioning, and much more.
Quantum Electric Sensing
While NV-centers can also be used for sensing electrical fields, there is another approach that seems more suitable. This approach is based on Rydberg Atoms.
Most classical methods for measuring electrical fields ultimately boil down to antennae. These bulky constructions require precise calibration and must be very large to cover low-energy (long-wavelength) frequencies. On the other hand, to detect high frequencies, the antenna must be super-cooled.
Rydberg atoms are created by exciting one of the electrons to a high-energy level, where it is not quite escaping the atom but is very close to it. The high energy and weak electron binding to the atom make the Rydberg atom extremely sensitive to electric field variations. Subtle quantum phenomena imply that the outlier atom remains in a high-energy state much longer than expected. Long enough to make a measurement. Owing to these properties, a cloud of Rydberg atoms is an excellent detector. This sensitivity allows measuring a wide range of frequencies at room temperature without requiring a specific scale or calibration.
The disadvantage is that, unlike antennae, Rydberg detectors are not passive. They require constantly creating Rydberg atoms. Standard, bulky antennae are still considered the correct choice for long-range, fixed-frequency communication.
Rydberg sensing is relatively new compared to NV magnetometry. However, there is a host of exciting upcoming applications. From precise mapping of electric fields to radar astronomy, military surveillance, and more.
Quantum Security for Quantum Sensors
Quantum sensors work by creating and measuring quantum phenomena. However, it is not necessary to read the measurement. This provides another non-classical ability: making a coherent measurement that remains in superposition.
For simplicity, let’s consider a binary detector whose output is either 0 or 1. We can represent it as a hugely complicated quantum state containing anything that affected the result of the measurement, including the sensor itself and its environment. (We never have to actually access once the measurement is recorded, so we are not too bothered by how complex it is.) The measurement process can be described as making a computation on $\ket{\psi}_\text{lab}$ and printing out either $0$ or $1$. But what if instead of printing it out, we store the result to an additional qubit? The resulting state is
where is the state of the lab if the measurement result is b, and is the probability of that result (for those who know: ).
Instead of collapsing into a single result, we get a pure superposition of the possible outcomes. This is what we describe as “measure the lab coherently into the result register”. We can always “complete” the measurement by measuring the result qubit, but we don’t have to. But what sort of benefit do we get from “concentrating” the result into one qubit?
This is the core question in cryptographic quantum metrology, a field that researches how to extend quantum security properties (such as the temper-prevention of quantum key distribution (QKD)) to the setting above.
Like in QKD, it is impossible to obtain any new form of unconditional security without any shared quantum resource. If the communication is entirely classical, and the participants are not entangled with each other, then whatever they do can be classically simulated. If we allow them to share entangled states (prepared in advance, independent of any future measurements) or to transmit quantum states to each other, then, as in QKD, powerful capabilities emerge.
Recent works show how quantum metrology can afford temper-resistance, allows delegating metrological tasks to untrusted parties without leaking information, and detecting faulty/malicious sensors.
The Bottom Line
The term “quantum sensing” originally referred to the use of quantum phenomena to improve measurement accuracy. At some point, people realized that the quantum nature of the sensors could be leveraged for security, and cryptographic quantum metrology was born.
Quantum metrology has intriguing security properties that might be useful for the recovery of aggregated data from many sources in a manner that is secure against tampering, without having to secure every site individually.
On the other hand, we should remember that quantum metrology crucially relies on entangled states that are shared among many sites, which requires quantum information that is currently beyond the edge of what is possible.
