IEEE websites place cookies on your device to give you the best user experience. By using our websites, you agree to the placement of these cookies. To learn more, read our Privacy Policy.
At issue: Quantum-entangled water bears?!
A tardigrade, also known as a water bear: It seems to survive the extreme conditions of supercooling and ultrahigh vacuums. Whether it can be quantum-mechanically entangled, though, is another question.
“I don’t like it, and I’m sorry I had anything to do with it,” the physicist Erwin Schrödinger supposedly said of the quantum theory.
He was so sorry that he worked to prove it nonsensical with the most famous thought problem in physics, one that involves putting a cat in a box that would fill with poison if a radioactive atom were to split apart spontaneously. According to the theory, that splitting can be said to have happened only if observed; otherwise, it must be deemed indeterminate. And because the cat’s fate is aligned with the atom’s, Schrödinger’s cat must also be considered neither dead nor alive.
Patent nonsense, concluded Schrödinger. But later researchers found ways to turn the thought problem into real experiments, and these have actually validated the predictions of quantum theory. One experiment used a resonator chilled nearly to absolute zero so that it became “entangled” across two quantum states, vibrating or not. Those two states were then shown to be superposed.
Actually entangling a living creature would be quite a feat for the physicists, perhaps more so for the biochemists. Complex chemical systems don’t normally stand still for inspection, but if you could freeze them quantum-cold you could probe their constituent parts. Some have suggested that biochemical processes, such as photosynthesis, must involve quantum effects; this method could be a way to prove it.
A tardigrade is a good candidate for freezing down to zero in a near-total vacuum. It’s about as tough as an animalcule gets.
To entangle a life-form you have to put it in an extreme vacuum and cool it nearly to absolute zero without killing it. Bacteria have been so entangled. Now a group of scientists say they’ve entangled a tardigrade, commonly called a water bear, a cute critter that’s just barely visible to the naked eye.
The 11 researchers published their work on 15 December in the online preprint server arXiv, which is not peer-reviewed. Among them are Rainer Dumke of the Center for Quantum Technologies, in Singapore, and Tomasz Paterek of the University of Gdansk, in Poland, who in 2019 were honored, so to speak, with an IgNobel Prize for their work on magnetized cockroaches (the results of which bear on methods by which animals navigate).
Let the record show that at least one winner of the IgNobel, Andre Geim, went on to win an actual Nobel. He got the IgNobel one for levitating a frog, the real Nobel for discovering graphene.
A tardigrade is a good candidate for freezing down to zero in a near-total vacuum. It’s about as tough as an animalcule gets. Insult the thing and it goes dormant by curling up into a ball, called a tun, in a process known as cryptobiosis. Though some have argued that at least some metabolism must still go on, a tun is perhaps best characterized as a life that’s been put on hold. In 2019, when a bunch of tardigrades were deposited on the moon during the very unintended crash-landing of an Israeli spacecraft, many people speculated that the critters would survive even there. Sadly, experiments involving the firing of nylon bullets later suggested that this didn’t happen.
Dumke and his colleagues came on their current interest in the course of studying superconducting qubits, electronic oscillators that many hope will produce a fundamentally new computer based on quantum effects. They wondered what would happen if they put a dormant tardigrade on top of one of their qubits, bringing the system to near absolute zero.
First, they learned, the tardigrade survived. That alone is a significant finding.
“At this very, very low temperature, almost nothing is moving, everything is in the ground state; it’s a piece of dust,” Dunke tells IEEE Spectrum. “Bring it back to conditions where it can survive, increasing the temperature gently, and the pressure, and it comes back. Some had suggested that in the cryptobiological state, some metabolism is going on. Not so.”
The presence of two superconducting qubits beside the tardigrade strengthens the case for the existence of entanglement—here it appears the creature is in superposition with one |0> qubit and one |1> qubit.
This discovery raises the question of what forces of natural selection might have shaped the tardigrade to be so tough? It seems way overengineered for its normal terrestrial habitats, including moss and lichen.
Second, Dumke and his colleagues argue, they achieved true quantum entanglement between the qubit and the tardigrade. Larger objects have been so entangled, but those objects were inanimate matter. This is a bigger claim—and one that’s harder to nail down.
“We start with a superconducting qubit at energy state 0, comparable to an atom in the ground state; there’s no oscillation—nothing is happening,” Dumke says. “We can use microwaves to supply exactly the right amount of energy for the right amount of time to raise this to level 1; this is like the second orbital in an atom. It is now oscillating.
“Or, and this is the important point, we can add exactly that much energy but supply it for just half the time to raise the system to a quantum state of ½, which is the superposition state. In this state, it is at the same time oscillating and not oscillating. You can do extensive testing to measure all three states.”
Then the workers tested the system under a number of different conditions to determine the quantum state, and they found that the system consisting of the qubit and the tardigrade together occupied a lower energy state than either one alone would have occupied. The researchers concluded that the two things had been entangled.
No need to wait for peer review; in a matter of days, the criticism began to come in.
One critic, Ben Brubaker, a physicist turned journalist, has argued on Twitter that the experiments do not demonstrate what the authors claim. He said there were three possibilities—that quantum entanglement had been achieved with the entire tardigrade, that it had been achieved with a part of it, and that it hadn’t been achieved at all. That last one would imply that any effects were caused by some classical (nonquantum) physical process.
The authors admit that they could not perform the perfect experiment, which would involve measuring the tardigrade and the qubit independently, using two probes. Their tardigrade comes packaged with the qubit, forming a hybrid structure, and so two probes are hard to manage.
A sketch of the experiment—including a photo of the revived tardigrade on the system’s qubit. arXiv
“So you have to construct a model that represents the qubit as a quantum-mechanical system, and if you do it classically you wouldn’t be able to account for all the features,” says Vlatko Vedral, another author, who is a professor of physics at the University of Oxford. “The feature we are talking about is the quantum energy state that the combined system is able to reach. In fact, much of chemistry is based on this kind of thing—the Van der Waals force.”
Kai Sheng Lee, of Singapore’s Nanyang Technological University, says that the criticism of the entanglement claim is at least partially answered in the second part of the arXiv paper, “when we introduce the second qubit.” The presence of two superconducting qubits beside the tardigrade strengthens the case for the existence of entanglement, because here it seems the creature is in superposition with one qubit that’s in the 0 state (sometimes abbreviated |0>) and also with the other qubit, which is in the 1 state (a.k.a. |1>).
“But the major weakness,” Vedral concedes, “is that there is no direct measurements on the tardigrade alone. This is what you need to do to satisfy even the most conspiratorial critic, the one who says we could explain this with classical arguments.”
Can direct measurements of each part in this entanglement triangle ever be made? That question makes Dumke, Vendral, and Lee pause. Finally Dumke takes a stab at it.
“You could try to find a particular resonance frequency inside the tardigrade, then use this frequency to find what leads to a stronger entanglement,” he says.
“Or maybe you could genetically engineer the tardigrade to resonate,” Vendral suggests.
Why the pregnant pause? Maybe they’re thinking about the question. Maybe they’re thinking about how much of their research plan to reveal. Or maybe the two states are superposed.
1 January 2022 Correction: A previous version of this story misspelled Ben Brubaker’s name. Apologies, Mr. Brubaker!
Philip E. Ross is a senior editor at IEEE Spectrum. His interests include transportation, energy storage, AI, and the economic aspects of technology. He has a master's degree in international affairs from Columbia University and another, in journalism, from the University of Michigan.
There is a great deal of research being conducted on quantum biological mechanisms that are repeatable and that have a proven function, such as photosynthesis and avian magnetic field navigation. IEEE Spectrum should publish an issue that discusses that work! Many chemists are aware of the discoveries being made in the field of quantum biology, but it seems that solid state scientists remain skeptical, and hold the view that any biological process is too "warm and wet" for a quantum mechanical effect to be present. That misconception has been disproven.
The part of quantum mechanics that Schrödinger objected to was where some physicists essentially claimed that a cat could be both alive and dead. Of course, Schrödinger was right. It is nonsense for a cat to be both alive and dead.
That picture looks phony as if it were made out of plastic. Is it really a tardigrade or is it a model of one?
Would love to know how they proved entanglement with some living thing.
There’s plenty of bandwidth available if we use reconfigurable intelligent surfaces
Ground level in a typical urban canyon, shielded by tall buildings, will be inaccessible to some 6G frequencies. Deft placement of reconfigurable intelligent surfaces [yellow] will enable the signals to pervade these areas.
For all the tumultuous revolution in wireless technology over the past several decades, there have been a couple of constants. One is the overcrowding of radio bands, and the other is the move to escape that congestion by exploiting higher and higher frequencies. And today, as engineers roll out 5G and plan for 6G wireless, they find themselves at a crossroads: After years of designing superefficient transmitters and receivers, and of compensating for the signal losses at the end points of a radio channel, they’re beginning to realize that they are approaching the practical limits of transmitter and receiver efficiency. From now on, to get high performance as we go to higher frequencies, we will need to engineer the wireless channel itself. But how can we possibly engineer and control a wireless environment, which is determined by a host of factors, many of them random and therefore unpredictable?
Perhaps the most promising solution, right now, is to use reconfigurable intelligent surfaces. These are planar structures typically ranging in size from about 100 square centimeters to about 5 square meters or more, depending on the frequency and other factors. These surfaces use advanced substances called metamaterials to reflect and refract electromagnetic waves. Thin two-dimensional metamaterials, known as metasurfaces, can be designed to sense the local electromagnetic environment and tune the wave’s key properties, such as its amplitude, phase, and polarization, as the wave is reflected or refracted by the surface. So as the waves fall on such a surface, it can alter the incident waves’ direction so as to strengthen the channel. In fact, these metasurfaces can be programmed to make these changes dynamically, reconfiguring the signal in real time in response to changes in the wireless channel. Think of reconfigurable intelligent surfaces as the next evolution of the repeater concept.
Reconfigurable intelligent surfaces could play a big role in the coming integration of wireless and satellite networks.
That’s important, because as we move to higher frequencies, the propagation characteristics become more “hostile” to the signal. The wireless channel varies constantly depending on surrounding objects. At 5G and 6G frequencies, the wavelength is vanishingly small compared to the size of buildings, vehicles, hills, trees, and rain. Lower-frequency waves diffract around or through such obstacles, but higher-frequency signals are absorbed, reflected, or scattered. Basically, at these frequencies, the line-of-sight signal is about all you can count on.
Such problems help explain why the topic of reconfigurable intelligent surfaces (RIS) is one of the hottest in wireless research. The hype is justified. A landslide of R&D activity and results has gathered momentum over the last several years, set in motion by the development of the first digitally controlled metamaterials almost 10 years ago.
This article was jointly produced by IEEE Spectrum and Proceedings of the IEEE with similar versions published in both publications.
RIS prototypes are showing great promise at scores of laboratories around the world. And yet one of the first major projects, the European-funded Visorsurf, began just five years ago and ran until 2020. The first public demonstrations of the technology occurred in late 2018, by NTT Docomo in Japan and Metawave, of Carlsbad, Calif.
Today, hundreds of researchers in Europe, Asia, and the United States are working on applying RIS to produce programmable and smart wireless environments. Vendors such as Huawei, Ericsson, NEC, Nokia, Samsung, and ZTE are working alone or in collaboration with universities. And major network operators, such as NTT Docomo, Orange, China Mobile, China Telecom, and BT are all carrying out substantial RIS trials or have plans to do so. This work has repeatedly demonstrated the ability of RIS to greatly strengthen signals in the most problematic bands of 5G and 6G.
To understand how RIS improves a signal, consider the electromagnetic environment. Traditional cellular networks consist of scattered base stations that are deployed on masts or towers, and on top of buildings and utility poles in urban areas. Objects in the path of a signal can block it, a problem that becomes especially bad at 5G’s higher frequencies, such as the millimeter-wave bands between 24.25 and 52.6 gigahertz. And it will only get worse if communication companies go ahead with plans to exploit subterahertz bands, between 90 and 300 GHz, in 6G networks. Here’s why. With 4G and similar lower-frequency bands, reflections from surfaces can actually strengthen the received signal, as reflected signals combine. However, as we move higher in frequencies, such multipath effects become much weaker or disappear entirely. The reason is that surfaces that appear smooth to a longer-wavelength signal are relatively rough to a shorter-wavelength signal. So rather than reflecting off such a surface, the signal simply scatters.
One solution is to use more powerful base stations or to install more of them throughout an area. But that strategy can double costs, or worse. Repeaters or relays can also improve coverage but here, too, the costs can be prohibitive. RIS, on the other hand, promises greatly improved coverage at just marginally higher cost
The key feature of RIS that makes it attractive in comparison with these alternatives is its nearly passive nature. The absence of amplifiers to boost the signal means that an RIS node can be powered with just a battery and a small solar panel.
RIS functions like a very sophisticated mirror, whose orientation and curvature can be adjusted in order to focus and redirect a signal in a specific direction. But rather than physically moving or reshaping the mirror, you electronically alter its surface so that it changes key properties of the incoming electromagnetic wave, such as the phase.
That’s what the metamaterials do. This emerging class of materials exhibits properties beyond (from the Greek meta) those of natural materials, such as anomalous reflection or refraction. The materials are fabricated using ordinary metals and electrical insulators, or dielectrics. As an electromagnetic wave impinges on a metamaterial, a predetermined gradient in the material alters the phase and other characteristics of the wave, making it possible to bend the wave front and redirect the beam as desired.
An RIS node is made up of hundreds or thousands of metamaterial elements called unit cells. Each cell consists of metallic and dielectric layers along with one or more switches or other tunable components. A typical structure includes an upper metallic patch with switches, a biasing layer, and a metallic ground layer separated by dielectric substrates. By controlling the biasing—the voltage between the metallic patch and the ground layer—you can switch each unit cell on or off and thus control how each cell alters the phase and other characteristics of an incident wave.
To control the direction of the larger wave reflecting off the entire RIS, you synchronize all the unit cells to create patterns of constructive and destructive interference in the larger reflected waves [ see illustration below]. This interference pattern reforms the incident beam and sends it in a particular direction determined by the pattern. This basic operating principle, by the way, is the same as that of a phased-array radar.
A reconfigurable intelligent surface comprises an array of unit cells. In each unit cell, a metamaterial alters the phase of an incoming radio wave, so that the resulting waves interfere with one another [above, top]. Precisely controlling the patterns of this constructive and destructive interference allows the reflected wave to be redirected [bottom], improving signal coverage.
An RIS has other useful features. Even without an amplifier, an RIS manages to provide substantial gain—about 30 to 40 decibels relative to isotropic (dBi)—depending on the size of the surface and the frequency. That’s because the gain of an antenna is proportional to the antenna’s aperture area. An RIS has the equivalent of many antenna elements covering a large aperture area, so it has higher gain than a conventional antenna does.
All the many unit cells in an RIS are controlled by a logic chip, such as a field-programmable gate array with a microcontroller, which also stores the many coding sequences needed to dynamically tune the RIS. The controller gives the appropriate instructions to the individual unit cells, setting their state. The most common coding scheme is simple binary coding, in which the controller toggles the switches of each unit cell on and off. The unit-cell switches are usually semiconductor devices, such as PIN diodes or field-effect transistors.
The important factors here are power consumption, speed, and flexibility, with the control circuit usually being one of the most power-hungry parts of an RIS. Reasonably efficient RIS implementations today have a total power consumption of around a few watts to a dozen watts during the switching state of reconfiguration, and much less in the idle state.
To deploy RIS nodes in a real-world network, researchers must first answer three questions: How many RIS nodes are needed? Where should they be placed? And how big should the surfaces be? As you might expect, there are complicated calculations and trade-offs.
Engineers can identify the best RIS positions by planning for them when the base station is designed. Or it can be done afterward by identifying, in the coverage map, the areas of poor signal strength. As for the size of the surfaces, that will depend on the frequencies (lower frequencies require larger surfaces) as well as the number of surfaces being deployed.
To optimize the network’s performance, researchers rely on simulations and measurements. At Huawei Sweden, where I work, we’ve had a lot of discussions about the best placement of RIS units in urban environments. We’re using a proprietary platform, called the Coffee Grinder Simulator, to simulate an RIS installation prior to its construction and deployment. We’re partnering with CNRS Research and CentraleSupélec, both in France, among others.
In a recent project, we used simulations to quantify the performance improvement gained when multiple RIS were deployed in a typical urban 5G network. As far as we know, this was the first large-scale, system-level attempt to gauge RIS performance in that setting. We optimized the RIS-augmented wireless coverage through the use of efficient deployment algorithms that we developed. Given the locations of the base stations and the users, the algorithms were designed to help us select the optimal three-dimensional locations and sizes of the RIS nodes from among thousands of possible positions on walls, roofs, corners, and so on. The output of the software is an RIS deployment map that maximizes the number of users able to receive a target signal.
An experimental reconfigurable intelligent surface with 2,304 unit cells was tested at Tsinghua University, in Beijing, last year.
Of course, the users of special interest are those at the edges of the cell-coverage area, who have the worst signal reception. Our results showed big improvements in coverage and data rates at the cell edges—and also for users with decent signal reception, especially in the millimeter band.
We also investigated how potential RIS hardware trade-offs affect performance. Simply put, every RIS design requires compromises—such as digitizing the responses of each unit cell into binary phases and amplitudes—in order to construct a less complex and cheaper RIS. But it’s important to know whether a design compromise will create additional beams to undesired directions or cause interference to other users. That’s why we studied the impact of network interference due to multiple base stations, reradiated waves by the RIS, and other factors.
Not surprisingly, our simulations confirmed that both larger RIS surfaces and larger numbers of them improved overall performance. But which is preferable? When we factored in the costs of the RIS nodes and the base stations, we found that in general a smaller number of larger RIS nodes, deployed further from a base station and its users to provide coverage to a larger area, was a particularly cost-effective solution.
The size and dimensions of the RIS depend on the operating frequency [see illustration below] . We found that a small number of rectangular RIS nodes, each around 4 meters wide for C-band frequencies (3.5 GHz) and around half a meter wide for millimeter-wave band (28 GHz), was a good compromise, and could boost performance significantly in both bands. This was a pleasant surprise: RIS improved signals not only in the millimeter-wave (5G high) band, where coverage problems can be especially acute, but also in the C band (5G mid).
To extend wireless coverage indoors, researchers in Asia are investigating a really intriguing possibility: covering room windows with transparent RIS nodes. Experiments at NTT Docomo and at Southeast and Nanjing universities, both in China, used smart films or smart glass. The films are fabricated from transparent conductive oxides (such as indium tin oxide), graphene, or silver nanowires and do not noticeably reduce light transmission. When the films are placed on windows, signals coming from outside can be refracted and boosted as they pass into a building, enhancing the coverage inside.
Planning and installing the RIS nodes is only part of the challenge. For an RIS node to work optimally, it needs to have a configuration, moment by moment, that is appropriate for the state of the communication channel in the instant the node is being used. The best configuration requires an accurate and instantaneous estimate of the channel. Technicians can come up with such an estimate by measuring the “channel impulse response” between the base station, the RIS, and the users. This response is measured using pilots, which are reference signals known beforehand by both the transmitter and the receiver. It’s a standard technique in wireless communications. Based on this estimation of the channel, it’s possible to calculate the phase shifts for each unit cell in the RIS.
The current approaches perform these calculations at the base station. However, that requires a huge number of pilots, because every unit cell needs its own phase configuration. There are various ideas for reducing this overhead, but so far none of them are really promising.
The total calculated configuration for all of the unit cells is fed to each RIS node through a wireless control link. So each RIS node needs a wireless receiver to periodically collect the instructions. This of course consumes power, and it also means that the RIS nodes are fully dependent on the base station, with unavoidable—and unaffordable—overhead and the need for continuous control. As a result, the whole system requires a flawless and complex orchestration of base stations and multiple RIS nodes via the wireless-control channels.
We need a better way. Recall that the “I” in RIS stands for intelligent. The word suggests real-time, dynamic control of the surface from within the node itself—the ability to learn, understand, and react to changes. We don’t have that now. Today’s RIS nodes cannot perceive, reason, or respond; they only execute remote orders from the base station. That’s why my colleagues and I at Huawei have started working on a project we call Autonomous RIS (AutoRIS). The goal is to enable the RIS nodes to autonomously control and configure the phase shifts of their unit cells. That will largely eliminate the base-station-based control and the massive signaling that either limit the data-rate gains from using RIS, or require synchronization and additional power consumption at the nodes. The success of AutoRIS might very well help determine whether RIS will ever be deployed commercially on a large scale.
Of course, it’s a rather daunting challenge to integrate into an RIS node the necessary receiving and processing capabilities while keeping the node lightweight and low power. In fact, it will require a huge research effort. For RIS to be commercially competitive, it will have to preserve its low-power nature.
With that in mind, we are now exploring the integration of an ultralow-power AI chip in an RIS, as well as the use of extremely efficient machine-learning models to provide the intelligence. These smart models will be able to produce the output RIS configuration based on the received data about the channel, while at the same time classifying users according to their contracted services and their network operator. Integrating AI into the RIS will also enable other functions, such as dynamically predicting upcoming RIS configurations and grouping users by location or other behavioral characteristics that affect the RIS operation.
Intelligent, autonomous RIS won’t be necessary for all situations. For some areas, a static RIS, with occasional reconfiguration—perhaps a couple of times per day or less—will be entirely adequate. In fact, there will undoubtedly be a range of deployments from static to fully intelligent and autonomous. Success will depend on not just efficiency and high performance but also ease of integration into an existing network.
6G promises to unleash staggering amounts of bandwidth—but only if we can surmount a potentially ruinous range problem.
The real test case for RIS will be 6G. The coming generation of wireless is expected to embrace autonomous networks and smart environments with real-time, flexible, software-defined, and adaptive control. Compared with 5G, 6G is expected to provide much higher data rates, greater coverage, lower latency, more intelligence, and sensing services of much higher accuracy. At the same time, a key driver for 6G is sustainability—we’ll need more energy-efficient solutions to achieve the “net zero” emission targets that many network operators are striving for. RIS fits all of those imperatives.
Start with massive MIMO, which stands for multiple-input multiple-output. This foundational 5G technique uses multiple antennas packed into an array at both the transmitting and receiving ends of wireless channels, to send and receive many signals at once and thus dramatically boost network capacity. However, the desire for higher data rates in 6G will demand even more massive MIMO, which will require many more radio-frequency chains to work and will be power-hungry and costly to operate. An energy-efficient and less costly alternative will be to place multiple low-power RIS nodes between massive MIMO base stations and users as we have described in this article.
The millimeter-wave and subterahertz 6G bands promise to unleash staggering amounts of bandwidth, but only if we can surmount a potentially ruinous range problem without resorting to costly solutions, such as ultradense deployments of base stations or active repeaters. My opinion is that only RIS will be able to make these frequency bands commercially viable at a reasonable cost.
The communications industry is already touting sensing—high-accuracy localization services as well as object detection and posture recognition—as an important possible feature for 6G. Sensing would also enhance performance. For example, highly accurate localization of users will help steer wireless beams efficiently. Sensing could also be offered as a new network service to vertical industries such as smart factories and autonomous driving, where detection of people or cars could be used for mapping an environment; the same capability could be used for surveillance in a home-security system. The large aperture of RIS nodes and their resulting high resolution mean that such applications will be not only possible but probably even cost effective.
And the sky is not the limit. RIS could enable the integration of satellites into 6G networks. Typically, a satellite uses a lot of power and has large antennas to compensate for the long-distance propagation losses and for the modest capabilities of mobile devices on Earth. RIS could play a big role in minimizing those limitations and perhaps even allowing direct communication from satellite to 6G users. Such a scheme could lead to more efficient satellite-integrated 6G networks.
As it transitions into new services and vast new frequency regimes, wireless communications will soon enter a period of great promise and sobering challenges. Many technologies will be needed to usher in this next exciting phase. None will be more essential than reconfigurable intelligent surfaces.
The author wishes to acknowledge the help of Ulrik Imberg in the writing of this article.