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Impact of ionizing radiation on superconducting qubit coherence – Nature.com

Abstract

Technologies that rely on quantum bits (qubits) require long coherence times and high-fidelity operations1. Superconducting qubits are one of the leading platforms for achieving these objectives2,3. However, the coherence of superconducting qubits is affected by the breaking of Cooper pairs of electrons4,5,6. The experimentally observed density of the broken Cooper pairs, referred to as quasiparticles, is orders of magnitude higher than the value predicted at equilibrium by the Bardeen–Cooper–Schrieffer theory of superconductivity7,8,9. Previous work10,11,12 has shown that infrared photons considerably increase the quasiparticle density, yet even in the best-isolated systems, it remains much higher10 than expected, suggesting that another generation mechanism exists13. Here we provide evidence that ionizing radiation from environmental radioactive materials and cosmic rays contributes to this observed difference. The effect of ionizing radiation leads to an elevated quasiparticle density, which we predict would ultimately limit the coherence times of superconducting qubits of the type measured here to milliseconds. We further demonstrate that radiation shielding reduces the flux of ionizing radiation and thereby increases the energy-relaxation time. Albeit a small effect for today’s qubits, reducing or mitigating the impact of ionizing radiation will be critical for realizing fault-tolerant superconducting quantum computers.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request and with the permission of the US Government sponsors who funded the work.

Code availability

The code used for the analyses is available from the corresponding author upon reasonable request and with the permission of the US Government sponsors who funded the work.

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Acknowledgements

We thank K. Serniak and R. Winik for discussions and comments on the manuscript; G. Calusine, K. Serniak and U. von Luepke for designing and pre-characterizing the qubit samples; G. Castelazo for assistance with operating the lead shield; M. S. Galanek, R. Samz and A. Greene for assistance in oversight of radiation source use; M. R. Ames and T. I. Bork at the MIT Reactor (MITR) for production of the 64Cu source; and M. A. Zalavadia for providing the NaI detector. This work was supported in part by the US Department of Energy Office of Nuclear Physics under an initiative in Quantum Information Science research (contract award no. DE-SC0019295, DUNS: 001425594); by the US Army Research Office (ARO) grant W911NF-14-1-0682; by the ARO Multidisciplinary Research Initiative W911NF-18-1-0218; by the National Science Foundation grant PHY-1720311; and by the Assistant Secretary of Defense for Research and Engineering via MIT Lincoln Laboratory under Air Force contract no. FA8721-05-C-0002. A.H.K. acknowledges support from the NSF Graduate Research Fellowship program. Pacific Northwest National Laboratory is operated by Battelle Memorial Institute under contract no. DE-AC05-76RL01830 for the US Department of Energy. We acknowledge IARPA and Lincoln Laboratory for providing the TWPA used in this experiment.

Author information

Author notes

  1. Akshunna S. Dogra

    Present address: Harvard University, Cambridge, MA, USA

Affiliations

  1. Massachusetts Institute of Technology, Cambridge, MA, USA

    Antti P. Vepsäläinen, Amir H. Karamlou, Akshunna S. Dogra, Francisca Vasconcelos, Simon Gustavsson, Joseph A. Formaggio & William D. Oliver

  2. Pacific Northwest National Laboratory, Richland, WA, USA

    John L. Orrell, Ben Loer & Brent A. VanDevender

  3. MIT Lincoln Laboratory, Lexington, MA, USA

    David K. Kim, Alexander J. Melville, Bethany M. Niedzielski, Jonilyn L. Yoder & William D. Oliver

Contributions

This research project was a collaboration between experts in quantum systems (A.P.V., A.H.K., F.V., S.G. and W.D.O.) and nuclear physics (J.A.F., J.L.O., B.A.V., B.L. and A.S.D). Simulations of background radiation and the impact of the radiation shielding were performed by A.S.D., B.L., and J.L.O. D.K.K., A.J.M., B.M.N. and J.L.Y. fabricated the qubit chips. The qubit experiments and data analysis were performed by A.P.V., A.H.K. and F.V. All authors contributed to writing and editing of the Article. A.P.V. should be contacted on matters concerning qubit operations, and J.L.O. should be contacted on matters concerning radiation exposure.

Corresponding authors

Correspondence to
Antti P. Vepsäläinen or John L. Orrell.

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Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Experimental set-up.

a, Simplified block diagram of the room-temperature electronics and dilution refrigerator configuration used for measuring the qubit frequency and coherence times. bd, Schematic of the lead shield used to block environmental radiation. The lead shield can be raised and lowered by a scissor lift. b, In the up position, the qubits were 17 cm below the edge of the lead shield. c, In the lowered position, the edge of the lead shield was 120 cm below the qubits. d, Picture of a partially raised lead shield (in between the configurations shown in b and c). The lead bricks are wrapped in protective plastic film. e, The parameters of the qubits used in the lead shield experiment.

Extended Data Fig. 2 Quasiparticle injection experiment.

a, The pulse sequence in the quasiparticle injection experiment. First, a strong microwave pulse is applied for the duration of dqp to the resonator, which excites quasiparticles. After time tqp, the energy-relaxation time of the qubit is measured. b, The energy-relaxation rate of the qubit Q1 during the quasiparticle injection experiment (blue dots). A solid green line shows a fit to the data using the full model that includes quasiparticle trapping and recombination. Orange dash-dotted line shows the model with only recombination; dotted line shows the same model without the internal quasiparticle relaxation rate Γother. Blue dash-dotted line shows the fit to the model that only includes trapping of quasiparticles. Dotted blue line shows the trapping model without Γother.

Extended Data Fig. 3 Energy-relaxation times in the shielding experiment.

a, Energy relaxation times T1 for qubits Q1–Q7 during the lead shield experiment while the shield is in up (blue) or down (orange) positions. b, The pulse sequence used to measure the energy-relaxation rate of all the qubits. First, a π-pulse is applied to all the qubits. After time t, a measurement pulse is used to determine the state of the qubits. The qubit excited-state population relaxes exponentially as a function of time. Blue circles show the measured qubit excited-state populations, and the orange line is an exponential fit using the model of equation (2). c, Stacked histogram of the combined energy-relaxation times for all of the qubits in the lead shield experiment. d, Plot of the noise power spectral density during the lead shield experiment for qubits Q3, Q4, Q6 and Q7. The red dashed line marks the rate of a single cycle of the lead shield. The green dashed line shows the estimated measurement period if all the data were gathered sequentially. Orange line is a fit to a power law, S = const/fα, with α ≈ 1.5.

Extended Data Fig. 4 Resonator single-tone spectroscopy.

ad, The transmission coefficient |S21| of resonator 1 as a function of readout power and readout frequency at different times throughout the experiment. When exposed to a high level of radiation, the resonator frequency becomes unstable in the dispersive regime that is used for reading out the qubit. The resonator becomes more stable as the radiation source decays. e, The change in the resonance frequency, Δωr, due to radiation throughout the experiment. We observe that the median Δωr follows an exponential decay with a half-life of t1/2 = 21.74 ± 2.8 h. f, Furthermore, the full-width at half-maximum (FWHM) of the resonator also exponentially decays with a half-life of t1/2 = 24.16 ± 0.78 h until it converges to the control value.

Extended Data Fig. 5 Qubit frequency shift.

a, The frequency of the qubit can be determined from a Fourier transform of a Ramsey measurement, shown at different times after installation of the 64Cu source. We plot the inferred qubit frequency by offsetting the measured Fourier transform spectra by the frequency of the control pulses. The orange dashed lines show the shift in the average qubit frequency during the experiment. b, The pulse sequence used in a Ramsey measurement. The first π/2-pulse prepares the qubit in a superposition state. The phase of the qubit state evolves during time t, after which a second π/2-pulse is applied before the measurement pulse. ce, Ramsey oscillations and fit T2 times are shown at 152 h, 212 h and 340 h after installation of the 64Cu source. The dashed lines in a show the times at which the measurements are performed.

Extended Data Fig. 6 Radiation transport simulations.

a, Isotopes measured to be present in the reference sample (A-Ref) and their activities inferred for sample A as of 24 May 2019 at 4:00 p.m. Eastern time zone. b, Results from simulations of environmental radiation sources in the laboratory environment. The background γ-ray flux is obtained by a fit to a measurement with a NaI scintillator (Fig. 3a), simulating and measuring both with and without the lead shield in the ‘up’ position. Cosmic rays were also measured and simulated for both shield-up and shield-down conditions; the shield did not have a measurable effect in the up position, as expected, and the effect is taken to be zero in the down position. c, The average shield effectiveness values η are weighted by each component’s contribution to total external power. Statistical uncertainties on the fraction of flux reaching the interior of the dilution refrigerator are all 0.0001; uncertainties on η-values for individual isotopes are all approximately 0.001. d, Power densities absorbed in silicon and aluminium. e, The figure shows the spectrum of the energy deposited in a NaI detector by cosmic-ray muon secondaries measured in the laboratory. The blue solid line shows the known cosmic-ray muon spectrum fit to the measured data. The spectrum corresponding to energies below the dashed red line is shown in Fig. 3a. Note that in the spectrum shown here, a different energy bin width is used to capture higher energy scales.

Extended Data Fig. 7 The effect of post-processing on the lead shield effect A/B test.

a, The upper row shows the P-value of the Wilcoxon signed rank test for three different test cases and for the different post-processing parameters. On the horizontal axis, the ({T}_{1}^{{rm{cut}}-{rm{off}}}) is varied. The vertical axis shows the effect of applying a cut-off to the difference in the energy-relaxation rates when the shield status is changed. The first column shows the actual data. The middle column shows a reference experiment, in which the energy-relaxation rates are compared without moving the shield. The last column shows the data when the energy-relaxation rate pairs are randomized. The lower row shows the median of the effect of the shield on the energy-relaxation rate δΓ1. b, The median of δΓ1 along the dashed lines in a. The shaded area shows 68% CIs for the median.

Extended Data Fig. 8 Asymmetry parameter distribution.

The distribution of the asymmetry parameter Ai of the energy-relaxation rates between the shield in up or down position.

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Vepsäläinen, A.P., Karamlou, A.H., Orrell, J.L. et al. Impact of ionizing radiation on superconducting qubit coherence.
Nature 584, 551–556 (2020). https://doi.org/10.1038/s41586-020-2619-8

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I learned the impact of prolonged exposure to stress from my foster child – The Washington Post

You know what stress is, right? You’re late for work, your car won’t start, gas costs more than you expected. We’ve all been there, and it’s not pleasant, that palm-sweating, heart-racing anxiety. Luckily, it’s not long-lasting — not toxic.

What is toxic stress? It’s prolonged adversity and/or abuse — not having enough to eat or being exposed to violence. It’s the kind of stress that puts you on edge and keeps you there, day after day after day.

If you’re familiar with the Centers for Disease Control and Prevention-Kaiser Permanente study from the 1990s, you know that factors such as divorce, domestic violence or having an incarcerated parent are called adverse childhood experiences (ACEs). Four or more ACEs can result in chronic health conditions such as heart disease or diabetes. In the long term, living with ACEs or other negative factors, such as poverty, can literally change your brain chemistry.

“Ms. Apple said that I have a traumatized brain,” my daughter, Cleo, said matter-of-factly after school one day. Her counselor had shown her a video and talked with her about why she reacts the way she does to certain triggers, such as someone blocking a door. Telling me this, Cleo sounded relieved, even empowered: It finally made sense to her.

As a director at a nonprofit group for preventing child abuse, I’ve known about ACEs for a long time. But it wasn’t until I brought a 12-year-old foster child into my life that I fully understood their impact. What does it look like for a young person to live with several ACEs and no supports? As custodial guardian to my daughter, now 16, I can only speak from my own observations.

For Cleo, it’s not being able to sleep without the light on. It’s eating even when she’s full. For a while, she was what the school called a “runner” — she left school whenever she was upset. For a while, this happened every day. She’d make it halfway across town before I caught up with her.

She was a cutter; she was suicidal. She had trouble forming appropriate friendships. She trashed her room several times; in one fight-or-flight moment, she climbed out of her window and tumbled one story to the snowy ground. She once jumped out of my car (which was, thankfully, not moving very fast). On several occasions, I had to restrain her by wrapping my arms around her shoulders or waist, using all my strength to keep her from leaving or hurting herself.

When she reacted in those ways, impulsively and without thought, I would tell her over and over, “Stop, calm down, I love you.” One day, when I raised my hand to motion toward something, she flinched. No, that’s not right — she ducked. And my heart broke for this young woman, who should know by now that I would never hit her.

I became a foster parent because I thought I had things to give — time and care and love — to kids who needed them. Initially, I was the person whom the county calls when a child is removed from a home and has nowhere else to go, or when a foster family needs a break. That’s what I signed up for: emergency respite.

Then I met Cleo.

She was all elbows and colt legs, a talented artist who had been in and out of the child-welfare system most of her life. That first weekend with her, I found out that we both like cute kitten videos and television shows about vampires. I saw a child who wanted to be happy, but who, after a lifetime of abuse and neglect, didn’t know how.

There was something about this kid that moved me. She tried so hard. Emotionally, she was much younger than 12, but she was also more resilient than most adults I knew. She came to stay with me every weekend after that, until a couple of months later, when her foster family decided that they’d had enough (she was “challenging,” she was “too much”). They sent her to residential care.

Believing that she belonged in a home, I fought with the county social service agencies to bring her home with me. Another year later, I supported a reunification with her biological mother — but, when that didn’t work out, I agreed to share custody. Cleo came to live with me and our dog, Zelda. A host of friends and family cheered us on.

Cleo and I worked hard, both with therapists and on our own, to build her coping skills. We sat together for hours over her schoolwork. I got her an individualized education plan that allowed for smaller classes and more breaks when she needed them. She had near-perfect attendance for the first time ever and glowed when she brought home a good grade. We set aside one hour each night to cuddle on the couch and watch those vampire shows. We had structure. We had routine.

We’d liked each other right away, but trusting each other took time. I’ll never forget the first time she asked, “Can I hug you?”

It’s been four years now. We persevere. We’re in this together, and we are resilient. I’ve taken pains to build a fortress of protective factors around my girl. Protective factors are those things that most of us take for granted — a friend to call when we need advice; someone to help when that car I mentioned won’t start. Some of us are born with built-in protective factors (a supportive family, enough money); others need to collect them (a family made up of friends, perhaps).

For Cleo, protective factors include school supports — not just teachers and staff who are kind, but trauma-informed teachers and staff who understand how ACEs can be reflected in behavior. Her protective factors are as simple as my giving her a night-light, and as complex as my helping to facilitate her relationships with the aunts she hadn’t seen in years. Her biggest protective factor? A dog who shows her unconditional love.

There are so many young people like my daughter everywhere. National data shows that more than 20 percent of children up to age 17 have experienced two or more ACEs. I’ve given a lot of thought to the ways that we, as a society, could help to ease and hopefully heal trauma in children. Here are my ideas:

First, we need to acknowledge that brain toxicity exists. Yes, a child can have post-traumatic stress disorder; PTSD is not reserved for combat veterans. Or maybe it is — maybe we need to start seeing these children as refugees from a war zone. We need to educate ourselves about ACEs and look at all people through a trauma-informed lens. We need to admit that ACEs are not limited to low-income neighborhoods, and that the domestic violence and substance abuse that take place in higher income homes are just as toxic.

We need to stop asking “What’s wrong with you?” and ask, instead, “What happened to you?”

Second, we need to stop treating children who’ve been affected by trauma as if their behavior doesn’t make sense. We need to approach them with understanding and compassion, and give them tools to help them cope. In the state of New York, every April 30 is ACEs Awareness Day, and the state mandates trauma training for domestic-violence shelter workers and child-care providers. My wish list includes trauma training for child-protective services workers, family court and law enforcement personnel, and for physicians. We also need to increase mental-health supports, so that there are therapists and crisis-response teams to refer to.

Finally, we must not see these children as damaged or doomed. They’re only lost causes if we make them so by giving up on them or telling them they’re worthless. Treating them as if their trauma is their fault, or as if their reactions make no sense, doesn’t help anyone. We need to shore up (or perhaps create) a safety net: The child-welfare, mental-health and education systems must work together to serve the whole child, or kids will fall through the cracks.

When I look at my daughter, I see the way her whole face lights up when Zelda licks it. I see how proud she feels when someone praises her for a job well done. I see the baby steps we make every day (not lashing out, not running away) and call them progress — and they are. One night, this teenager who rarely made eye contact as a child went around the room after a party and hugged every adult there.

When I told her that I was writing this piece, she said that she wanted adults to know this: “Records only tell part of the story. Nobody takes into account what the kids are dealing with. You can’t treat kids with trauma like kids without trauma. You have to treat us differently — but don’t make us feel different. You can’t tell us how to feel, or drug us up with medication. You have to listen.”

Cleo is remarkably resilient. How do I know this? Because she gets up every morning and tries again.

She hasn’t given up. So I won’t give up on her. And I hope others don’t, either.

Jenn O’Connor is director of policy and advocacy at Prevent Child Abuse NY and director of the NYS Home Visiting Coordination Initiative. This essay originally appeared on Pulse — Voices From the Heart of Medicine, which publishes personal accounts of illness and healing.

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