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Meeting 2026 07 13
amirshehataornl edited this page Jul 18, 2026
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- Date: Monday, July 13, 2026
- Time: (Slot #2) 7 pm - 8 pm US Eastern Daylight Time (View Meeting Time in Your Timezone)
- Location: Virtual Meeting.
- Meeting Link: openqse.org/captcha
- Next: Monday, July 20, 2026 (Slot #1) @ 12 pm - 1 pm US Eastern Daylight Time (View Meeting Time in Your Timezone)
- Speaker: Anastasiia Butko (LBL) overview on recent work related to control electronics at LBL
| Person | Institution |
|---|---|
| Amir Shehata | ORNL |
| Michael Ferguson | HPE |
| Bran Radovanovic | ORNL |
| Thomas Naughton | ORNL |
| Andy Stone | HPE |
| Alain Roy | IonQ |
| Eun Kyung Lee | IBM |
| Claudio Siqueira de Carvalho | IBM |
| Jeff Heckey | AWS |
| Smriti Bajaj | Dell Technologies |
| Brandon Neth | HPE |
| Nick Wright | NVIDIA |
| Namit Anand | HPE |
| Miwako Tsuji | RIKEN |
| Alex Chernoguzov | Quantinuum |
| Yasuko Eckert | AMD |
| Jonathan Skone | NERSC |
| Ryan Landfield | ORNL |
| Neal Erickson | Quantinuum |
| Christian Ortiz | Quantum Brilliance |
| Kevin Kissell | Alice & Bob |
| Anastasiia Butko | LBNL |
| Josh Moles | IonQ |
| Abdur Rahman Hatim | IISc |
| Martin Schulz | TUM |
- NOTICE: Notes generated using AI; please verify accuracy.
- Thomas Naughton started the recording and confirmed that Anastasiia Butko would be presenting on control electronics work at Berkeley Lab.
- Anastasiia introduced herself as part of the Berkeley Lab computer architecture group, with a background in classical architectures, microelectronics, HPC, and computer science.
- She framed the talk around the quantum hardware stack, especially control challenges for superconducting qubits.
- Anastasiia emphasized that a quantum computer is more than a quantum processor:
- The quantum chip is only one part of a larger system.
- The classical control stack, cabling, dilution refrigerator, readout, and feedback path are all part of the machine.
- The Berkeley Lab quantum testbed was used as an example of this full-system view:
- A small superconducting chip with roughly a dozen qubits still required substantial room-temperature control hardware and wiring into the refrigerator.
- Commercial control hardware was available from vendors, but Berkeley Lab also developed in-house FPGA-based control stacks.
- She described in-house work around open-source control components, including Quasar and Cubic.
- A major practical motivation was access and flexibility:
- Vendor hardware could provide comparable quality.
- In-house control gave researchers detailed access to each layer of the control hardware and software.
- Superconducting qubits were presented as one of the more mature quantum technologies, but with strict control constraints.
- The key example was mid-circuit measurement:
- A circuit may need to measure a qubit partway through execution.
- The control system then branches conditionally based on the measured state.
- These workflows are latency-sensitive because:
- Superconducting-qubit coherence times are limited.
- Single-qubit gate pulses may be on the order of 10 to 20 nanoseconds.
- Two-qubit gates and readout operations are longer.
- Feedback, signal travel, and control processing time all consume part of the available circuit window.
- Anastasiia contrasted two control approaches:
- A simpler offline model where software compiles an algorithm down to a fixed pulse sequence and the room-temperature controller plays it back.
- A more dynamic model where a quantum control processor sits closer to the hardware and can generate pulses and make decisions during execution.
- The dynamic model can reduce latency and support more flexible circuits, but it requires specialized hardware and is harder to implement and modify.
- Anastasiia described earlier work on a quantum instruction set architecture for control processors.
- The ISA work examined classical architecture questions such as:
- Instruction encoding width.
- Whether instructions should be vectorized.
- How many bits are needed to address qubits.
- How those choices limit simultaneous control and scalability.
- The design used a lightweight RISC-style approach rather than a complex instruction set.
- A RISC-V-based implementation was attractive because:
- The ecosystem already includes compilers, software stacks, and hardware implementations.
- Existing tooling can be extended rather than rebuilt from scratch.
- Berkeley Lab implemented a processor on a VC707 FPGA connected to the testbed and demonstrated fast feedback on real qubits.
- The same control stack was also used for measurements such as T1 characterization, showing that the work was not only simulation-based.
- The second major topic was the quantum state discriminator, which classifies readout data into qubit states.
- Anastasiia described the readout input as IQ-plane data or blob-like readout distributions:
- A simple threshold line can work when the readout blobs are well separated.
- Overlapping or closely spaced blobs require more sophisticated discrimination.
- Neural-network-based discriminators have been explored for improved accuracy and performance.
- Cubic ML, developed by Berkeley Lab colleagues, was used as a baseline:
- It implemented a neural network on FPGA programmable logic.
- It could support fast state discrimination, but scaling is limited by FPGA resource use and synchronization issues.
- Anastasiia explored using the Xilinx VCK190 AI-engine architecture:
- The AI engine provides an array of specialized vector-processing tiles next to the programmable logic.
- She mapped a three-layer neural network to the AI engine.
- The AI-engine version had about 81 nanoseconds of kernel inference time compared with about 54 nanoseconds for Cubic ML.
- Although slower for the small network, it stayed under 100 nanoseconds and may scale better for more qubits or larger neural networks.
- Resource and power measurements were included because power becomes a major constraint when control electronics move into low-temperature environments.
- The final part of the talk asked whether parts of the classical control hardware can be moved from room temperature into the refrigerator.
- The motivation is to:
- Reduce feedback latency.
- Reduce wiring complexity.
- Improve scalability beyond near-term experiments.
- Anastasiia identified several challenges:
- Electronics near qubits can introduce noise.
- Standard FPGAs and CMOS chips cannot simply be placed in millikelvin environments.
- Cryo-CMOS is easier to adopt because it remains CMOS-like, but may have unacceptable noise and power characteristics.
- Non-CMOS superconducting technologies require architectural redesign, not only technology substitution.
- She discussed superconducting classical logic technologies such as RSFQ and AQFP:
- These technologies can operate at very high clock rates.
- Existing computing work in this area predates current quantum-control applications.
- Mapping conventional designs directly to these technologies can be impractical.
- A neural-network state discriminator was used as an example:
- Directly mapping the network to superconducting logic required about 344,000 Josephson junctions, which Anastasiia described as impractical.
- Redesigning the representation and operations with a biologically inspired unary data representation reduced the design to roughly 3,000 Josephson junctions.
- The redesigned implementation was described as very fast and low power.
- Anastasiia said a Nature paper on this low-temperature discriminator work had been accepted and was moving through publication processing.
- Amir Shehata asked whether low-temperature control still needs to send data back to room-temperature compute and receive commands from outside the refrigerator.
- Anastasiia clarified that, for the low-temperature state discriminator work, the readout classification does not need to leave the low-temperature environment.
- She noted that other pieces of the control stack are still needed, especially memory-related pieces, which remain challenging for superconducting implementations.
- Amir asked whether only state discrimination had moved to low temperature, not pulse generation.
- Anastasiia said the team also had experimental work with a simplified Quasar-like core through the same tool flow, suggesting that additional low-temperature control pieces may be feasible.
- She cautioned that pulse-table handling and on-the-fly pulse generation remain research problems rather than solved engineering components.
- Jeff Heckey asked about the relationship between Berkeley Lab's control work and a Fermilab control-board project.
- Anastasiia distinguished the Fermilab work from Berkeley Lab's Cubic work:
- They are separate projects.
- Both are open-source efforts.
- They developed in parallel for somewhat different control contexts.
- She noted that there had been interest in adding a RISC-V processor into that broader ecosystem, though she was not sure of the current status.
- Jeff also asked about programming the AI-engine tiles and managing fan-out across multiple control systems.
- Anastasiia said this remains difficult to unify and is still largely handled by working at separate levels and stitching components together.
- A practical blocker is board capability:
- The VCK190 board has useful AI-engine capabilities.
- The GCU board used in the current setup has a different DAC/ADC configuration.
- Using two boards would create significant board-to-board data-movement complexity.
- Anastasiia said they were waiting for a future board that combines the relevant capabilities and would enable cleaner experiments.
- Josh Moles asked whether there was a preprint or publication timeline for the accepted paper Anastasiia mentioned.
- Anastasiia said there might be an arXiv version and invited Josh to email her so she could follow up with the paper.
- Nick Wright dropped a link to a related AI-engine paper during the discussion.
- Thomas asked Anastasiia to share her slides with him or Amir, and she agreed.
- Quantum-control hardware is a central part of the quantum-computing system, not a peripheral implementation detail.
- Superconducting-qubit control is strongly constrained by latency, especially for mid-circuit measurement and conditional branching.
- RISC-V-based control processors and FPGA-based state-discrimination hardware offer practical near-term paths for more flexible control.
- AI-engine accelerators may provide better scaling for neural-network-based state discrimination even when small single-qubit examples do not show the lowest latency.
- Moving control closer to the qubits is attractive, but low-temperature and superconducting implementations require new architectures, memory strategies, and careful noise and power management.
- Integration tooling and board-level system design remain major practical barriers for experimental control-stack work.
| Action | Owner | Notes |
|---|---|---|
| Follow up with Anastasiia about the accepted low-temperature state-discriminator paper or preprint. | Josh Moles | Josh offered to email her; Anastasiia said she could share or point him to the paper. |
| Share slides from the presentation. | Anastasiia Butko | Thomas asked Anastasiia to send slides to him or Amir, and she agreed. |
- Thomas thanked Anastasiia for the talk and apologized for the earlier room or meeting-access issue.
- Anastasiia said she was happy to share the materials and thanked the group for having her.
- Thomas closed by thanking everyone and wishing attendees a good rest of the week.