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    A new way for quantum computing systems to keep their cool

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    Heat causes errors in the qubits that are the building blocks of a quantum computer, so quantum systems are typically kept inside refrigerators that keep the temperature just above absolute zero (-459 degrees Fahrenheit).

    But quantum computers need to communicate with electronics outside the refrigerator, in a room-temperature environment. The metal cables that connect these electronics bring heat into the refrigerator, which has to work even harder and draw extra power to keep the system cold. Plus, more qubits require more cables, so the size of a quantum system is limited by how much heat the fridge can remove.

    To overcome this challenge, an interdisciplinary team of MIT researchers has developed a wireless communication system that enables a quantum computer to send and receive data to and from electronics outside the refrigerator using high-speed terahertz waves.

    A transceiver chip placed inside the fridge can receive and transmit data. Terahertz waves generated outside the refrigerator are beamed in through a glass window. Data encoded onto these waves can be received by the chip. That chip also acts as a mirror, delivering data from the qubits on the terahertz waves it reflects to their source.

    This reflection process also bounces back much of the power sent into the fridge, so the process generates only a minimal amount of heat. The contactless communication system consumes up to 10 times less power than systems with metal cables.

    “By having this reflection mode, you really save the power consumption inside the fridge and leave all those dirty jobs on the outside. While this is still just a preliminary prototype and we have some room to improve, even at this point, we have shown low power consumption inside the fridge that is already better than metallic cables. I believe this could be a way to build largescale quantum systems,” says senior author Ruonan Han, an associate professor in the Department of Electrical Engineering and Computer Sciences (EECS) who leads the Terahertz Integrated Electronics Group.

    Han and his team, with expertise in terahertz waves and electronic devices, joined forces with associate professor Dirk Englund and the Quantum Photonics Laboratory team, who provided quantum engineering expertise and joined in conducting the cryogenic experiments.

    Joining Han and Englund on the paper are first author and EECS graduate student Jinchen Wang; Mohamed Ibrahim PhD ’21; Isaac Harris, a graduate student in the Quantum Photonics Laboratory; Nathan M. Monroe PhD ’22; Wasiq Khan PhD ’22; and Xiang Yi, a former postdoc who is now a professor at the South China University of Technology. The paper will be presented at the International Solid-States Circuits Conference.

    Tiny mirrors

    The researchers’ square transceiver chip, measuring about 2 millimeters on each side, is placed on a quantum computer inside the refrigerator, which is called a cryostat because it maintains cryogenic temperatures. These super-cold temperatures don’t damage the chip; in fact, they enable it to run more efficiently than it would at room temperature.

    The chip sends and receives data from a terahertz wave source outside the cryostat using a passive communication process known as backscatter, which involves reflections. An array of antennas on top of the chip, each of which is only about 200 micrometers in size, act as tiny mirrors. These mirrors can be “turned on” to reflect waves or “turned off.”

    The terahertz wave generation source encodes data onto the waves it sends into the cryostat, and the antennas in their “off” state can receive those waves and the data they carry.

    When the tiny mirrors are turned on, they can be set so they either reflect a wave in its current form or invert its phase before bouncing it back. If the reflected wave has the same phase, that represents a 0, but if the phase is inverted, that represents a 1. Electronics outside the cryostat can interpret those binary signals to decode the data.

    “This backscatter technology is not new. For instance, RFIDs are based on backscatter communication. We borrow that idea and bring it into this very unique scenario, and I think this leads to a good combination of all these technologies,” Han says.

    Terahertz advantages

    The data are transmitted using high-speed terahertz waves, which are located on the electromagnetic spectrum between radio waves and infrared light.

    Because terahertz waves are much smaller than radio waves, the chip and its antennas can be smaller, too, which would make the device easier to manufacture at scale. Terahertz waves also have higher frequencies than radio waves, so they can transmit data much faster and move larger amounts of information.

    But because terahertz waves have lower frequencies than the light waves used in photonic systems, the terahertz waves carry less quantum noise, which leads to less interference with quantum processors.

    Importantly, the transceiver chip and terahertz link can be fully constructed with standard fabrication processes on a CMOS chip, so they can be integrated into many current systems and techniques.

    “CMOS compatibility is important. For example, one terahertz link could deliver a large amount of data and feed it to another cryo-CMOS controller, which can split the signal to control multiple qubits simultaneously, so we can reduce the quantity of RF cables dramatically. This is very promising.” Wang says.

    The researchers were able to transmit data at 4 gigabits per second with their prototype, but Han says the sky is nearly the limit when it comes to boosting that speed. The downlink of the contactless system posed about 10 times less heat load than a system with metallic cables, and the temperature of the cryostat fluctuated up to a few millidegrees during experiments.

    Now that the researchers have demonstrated this wireless technology, they want to improve the system’s speed and efficiency using special terahertz fibers, which are only a few hundred micrometers wide. Han’s group has shown that these plastic wires can transmit data at a rate of 100 gigabits per second and have much better thermal insulation than fatter, metal cables.

    The researchers also want to refine the design of their transceiver to improve scalability and continue boosting its energy efficiency. Generating terahertz waves requires a lot of power, but Han’s group is studying more efficient methods that utilize low-cost chips. Incorporating this technology into the system could make the device more cost-effective.

    The transceiver chip was fabricated through the Intel University Shuttle Program. More

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      in Data Management & Statistics

      In-home wireless device tracks disease progression in Parkinson’s patients

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      Parkinson’s disease is the fastest-growing neurological disease, now affecting more than 10 million people worldwide, yet clinicians still face huge challenges in tracking its severity and progression.

      Clinicians typically evaluate patients by testing their motor skills and cognitive functions during clinic visits. These semisubjective measurements are often skewed by outside factors — perhaps a patient is tired after a long drive to the hospital. More than 40 percent of individuals with Parkinson’s are never treated by a neurologist or Parkinson’s specialist, often because they live too far from an urban center or have difficulty traveling.

      In an effort to address these problems, researchers from MIT and elsewhere demonstrated an in-home device that can monitor a patient’s movement and gait speed, which can be used to evaluate Parkinson’s severity, the progression of the disease, and the patient’s response to medication.

      The device, which is about the size of a Wi-Fi router, gathers data passively using radio signals that reflect off the patient’s body as they move around their home. The patient does not need to wear a gadget or change their behavior. (A recent study, for example, showed that this type of device could be used to detect Parkinson’s from a person’s breathing patterns while sleeping.)

      The researchers used these devices to conduct a one-year at-home study with 50 participants. They showed that, by using machine-learning algorithms to analyze the troves of data they passively gathered (more than 200,000 gait speed measurements), a clinician could track Parkinson’s progression and medication response more effectively than they would with periodic, in-clinic evaluations.

      “By being able to have a device in the home that can monitor a patient and tell the doctor remotely about the progression of the disease, and the patient’s medication response so they can attend to the patient even if the patient can’t come to the clinic — now they have real, reliable information — that actually goes a long way toward improving equity and access,” says senior author Dina Katabi, the Thuan and Nicole Pham Professor in the Department of Electrical Engineering and Computer Science (EECS), and a principle investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the MIT Jameel Clinic.

      The co-lead authors are EECS graduate students Yingcheng Liu and Guo Zhang. The research is published today in Science Translational Medicine.

      A human radar

      This work utilizes a wireless device previously developed in the Katabi lab that analyzes radio signals that bounce off people’s bodies. It transmits signals that use a tiny fraction of the power of a Wi-Fi router — these super-low-power signals don’t interfere with other wireless devices in the home. While radio signals pass through walls and other solid objects, they are reflected off humans due to the water in our bodies.  

      This creates a “human radar” that can track the movement of a person in a room. Radio waves always travel at the same speed, so the length of time it takes the signals to reflect back to the device indicates how the person is moving.

      The device incorporates a machine-learning classifier that can pick out the precise radio signals reflected off the patient even when there are other people moving around the room. Advanced algorithms use these movement data to compute gait speed — how fast the person is walking.

      Because the device operates in the background and runs all day, every day, it can collect a massive amount of data. The researchers wanted to see if they could apply machine learning to these datasets to gain insights about the disease over time.

      They gathered 50 participants, 34 of whom had Parkinson’s, and conducted a one-year study of in-home gait measurements Through the study, the researchers collected more than 200,000 individual measurements that they averaged to smooth out variability due to the conditions irrelevant to the disease. (For example, a patient may hurry up to answer an alarm or walk slower when talking on the phone.)

      They used statistical methods to analyze the data and found that in-home gait speed can be used to effectively track Parkinson’s progression and severity. For instance, they showed that gait speed declined almost twice as fast for individuals with Parkinson’s, compared to those without. 

      “Monitoring the patient continuously as they move around the room enabled us to get really good measurements of their gait speed. And with so much data, we were able to perform aggregation that allowed us to see very small differences,” Zhang says.

      Better, faster results

      Drilling down on these variabilities offered some key insights. For instance, the researchers showed that daily fluctuations in a patient’s walking speed correspond with how they are responding to their medication — walking speed may improve after a dose and then begin to decline after a few hours, as the medication impact wears off.

      “This enables us to objectively measure how your mobility responds to your medication. Previously, this was very cumbersome to do because this medication effect could only be measured by having the patient keep a journal,” Liu says.

      A clinician could use these data to adjust medication dosage more effectively and accurately. This is especially important since drugs used to treat disease symptoms can cause serious side effects if the patient receives too much.

      The researchers were able to demonstrate statistically significant results regarding Parkinson’s progression after studying 50 people for just one year. By contrast, an often-cited study by the Michael J. Fox Foundation involved more than 500 individuals and monitored them for more than five years, Katabi says.

      “For a pharmaceutical company or a biotech company trying to develop medicines for this disease, this could greatly reduce the burden and cost and speed up the development of new therapies,” she adds.

      Katabi credits much of the study’s success to the dedicated team of scientists and clinicians who worked together to tackle the many difficulties that arose along the way. For one, they began the study before the Covid-19 pandemic, so team members initially visited people’s homes to set up the devices. When that was no longer possible, they developed a user-friendly phone app to remotely help participants as they deployed the device at home.

      Through the course of the study, they learned to automate processes and reduce effort, especially for the participants and clinical team.

      This knowledge will prove useful as they look to deploy devices in at-home studies of other neurological disorders, such as Alzheimer’s, ALS, and Huntington’s. They also want to explore how these methods could be used, in conjunction with other work from the Katabi lab showing that Parkinson’s can be diagnosed by monitoring breathing, to collect a holistic set of markers that could diagnose the disease early and then be used to track and treat it.

      “This radio-wave sensor can enable more care (and research) to migrate from hospitals to the home where it is most desired and needed,” says Ray Dorsey, a professor of neurology at the University of Rochester Medical Center, co-author of Ending Parkinson’s, and a co-author of this research paper. “Its potential is just beginning to be seen. We are moving toward a day where we can diagnose and predict disease at home. In the future, we may even be able to predict and ideally prevent events like falls and heart attacks.”

      This work is supported, in part, by the National Institutes of Health and the Michael J. Fox Foundation. More