By Bhuvanesh Sundar, Senior Quantum Researcher

Plasmas are the most abundant phase of matter in the universe. They are found in diverse environments such as stars, the interstellar medium, atmospheric events like lightning, and everyday objects like televisions. Accurate simulations of plasma are crucial for the design of advanced fusion reactor power plants that harness the plasma at the core of the reactor. However, target fusion plasmas may have high energies or densities, leading to strong quantum effects that are intractable to simulate using today’s classical methods.

Quantum computers offer a path forward: they can simulate quantum physics in ways classical machines cannot. Previous theoretical proposals for simulating plasmas on quantum computers require quantum algorithms with too many operations to run reliably given the error rates of contemporary quantum hardware. Other approaches sidestep that obstacle by approximating the plasma with linearized equations, but those approximations cannot capture the full quantum behavior.

Researchers at Rigetti Computing, Lawrence Livermore National Laboratory, and the University of Colorado, Boulder have been exploring how to overcome both limitations. The team built a quantum “spin” model that captures the essential physics of waves in a plasma and ran it on Rigetti’s 84-qubit Ankaa-3 quantum computer to simulate electromagnetic waves scattering through a plasma. Although the current model covers linear wave scattering, the same approach can be extended to capture nonlinear effects.

The experiments ran on a nine-qubit subset on the Rigetti Ankaa-3 system, with the simulation broken into a sequence of time steps the chip can execute directly. Crucially, raw output from today’s quantum processors is noisy enough to mask the physics we wanted to see: Therefore, we developed novel variations of two complementary error mitigation techniques. The first randomizes how each two-qubit gate is compiled, which converts most types of hardware error into a single, well-behaved statistical form. The second uses linear regression on a set of easy-to-check reference circuits to learn how that noise distorts results, then corrects the result from the real experiment accordingly. Together, these techniques recovered clean signals for wave dispersion and scattering across the time scales of physical interest.

The simulation

We first confirmed that the surrogate spin model reproduces the energy spectrum of electromagnetic waves in a real plasma. A signature feature of that spectrum is an energy gap, meaning a range of energies in which waves simply cannot travel through the plasma. For instance, this is exactly why long-range radio works: radio waves bounce off Earth’s ionosphere because their energy falls inside the ionosphere’s gap.

By tuning the quantum circuit, we could dial the simulated plasma’s density up and down, which directly controls the size of the gap. In one limit we set the density to zero, recovering ordinary light waves traveling through empty space. To map out the energy spectrum, we prepared a superposition of wave packets and tracked how each one’s phase evolved over time. As expected, we saw no gap when the density was zero, and a clear gap once the density became nonzero.

Using the same controls, we could make the simulated plasma’s density vary across space: uniformly zero in some experiments, a sharp jump in others, and smoothly varying in a third. We then launched a single wave packet into each setup. In an empty region the packet propagated freely. When it met a region whose energy gap exceeded the wave’s energy, it bounced back, reproducing the expected reflection signatures shown below.

The impact

This work is a milestone for using quantum computers to study plasma physics, made possible by error mitigation techniques that turn noisy hardware measurements into useful scientific data. The present experiment focuses on linear wave propagation, but the framework naturally extends toward regimes where classical simulation becomes much more demanding. In particular, richer initial quantum states and nonlinear plasma interactions could push larger simulations beyond the practical reach of classical methods. Scaling this approach to nonlinear plasma physics on larger quantum processors is therefore a promising route toward demonstrating quantum advantage on a scientifically useful problem.

Learn More

• Read the technical paper describing Rigetti’s simulation of electromagnetic waves in plasmas
• Read the technical paper describing Rigetti’s previous experimental realizations of nonlinear optical processes that can occur in plasmas: Simulating nonlinear optical processes on a superconducting quantum device

This material is based upon work supported by the U.S. Department of Energy, Office of Science, under award number DE-SC0021661. This publication was prepared to include an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. The work by Lawrence Livermore National Laboratory was performed under the auspices of the US Department of Energy (DOE) under Contract DE-AC52–07NA27344. Two of the authors were supported by the DOE Office of Fusion Energy Sciences projects SCW1736 and SCW1680. One of the authors is supported in part by U.S. Department of Energy under Grant No. DE-SC0020393.

By Maxime Dupont, Lead Quantum Researcher

We often hear that quantum computing will reshape industries like logistics and pharmaceuticals. These fields rely on solving complex optimization problems — finding the single best solution among a vast sea of possibilities. While quantum algorithms like the Quantum Approximate Optimization Algorithm (QAOA) show great promise, they face a stubborn reality: today’s quantum computers are small and noisy.

This presents a dilemma. To realize the full potential of quantum algorithms, we must test them on large-scale problems where classical computers struggle. Real-world applications — like finding the optimal fit for a drug molecule on a protein — require managing hundreds to thousands of variables, yet current quantum processors can only represent a fraction of that. Without bridging this gap, we cannot fully benchmark the technology or unlock its practical advantage.

Our latest research introduces a new quantum algorithm: the self-consistent mean-field “SCMF” QAOA. It draws inspiration from the physics of magnets, where each atom aligns itself to the average magnetic field of its neighbors rather than tracking every single interaction individually. We apply this idea to optimization: instead of solving the whole puzzle at once, we break it into manageable pieces that influence each other through a shared environment. This environment acts like a messenger, summarizing the influence of the rest of the system so that each small piece can be solved accurately on its own (Figure 1). This approach enables us to tackle problems that would otherwise exceed the limits of today’s hardware or simulators. We demonstrate this capability by solving a molecular docking problem for drug discovery, using methodology established by Pfizer’s and others; see arXiv:2503.04239 and Sci. Adv. 6, eaax1950 (2020).

Self-consistent mean-field QAOA

The core idea behind our new approach is to break a large problem into bite-sized pieces without losing the big picture. In optimization, different parts of a problem are often deeply connected, so simply cutting a problem apart usually severs the ties that make the solution high-quality.

Our algorithm preserves these connections by introducing a shared environment. As we solve each subproblem using QAOA, we use the results to update this environment. This creates a feedback loop: the environment guides the subproblems, and the subproblems refine the environment. This cycle repeats until the system stabilizes, allowing us to combine the pieces into a high-quality global solution without the computational cost of solving everything at once.

We validated this approach using the Sherrington-Kirkpatrick model, a standard benchmark designed to stress-test optimization algorithms (Figure 2). By decomposing 256-variable problems into 16 smaller subproblems, we reduced the gate count by 99.6%. Despite this dramatic drop in resource requirements, we didn’t compromise on quality. At shallow circuit depths, our approach actually outperforms the standard QAOA on the full problem — a feat currently out of reach for simulation tools. This level of performance is much higher than that of solving the independent subproblems without any environment, confirming that our mean-field environment effectively captures the complex correlations between the separated pieces and allowing us to scale up without losing accuracy.

Application to drug design

We applied this framework to a key task in pharmaceutical research known as molecular docking. This process involves computationally predicting the preferred orientation of a drug molecule (ligand) when it binds to a protein target, like finding the angle at which a key enters a lock. Accurately modeling these interactions is essential for identifying potential new medicines, but the computational complexity grows rapidly with the size of the molecules involved.

We focused on a specific protein-ligand complex relevant to cancer research, which presented an optimization challenge involving 252 variables and a solution space of 10⁷⁶. Solving this problem directly with the depth-1 QAOA on a quantum processor would require 252 qubits and tens of thousands of two-qubit gates — a requirement that exceeds the fidelity and coherence times of current quantum hardware. By applying our self-consistent mean-field algorithm, we decomposed this intractable problem into 12 manageable subproblems, each containing 21 variables. This reduction allowed us to execute the algorithm on a simulator and on the Rigetti Ankaa–3 superconducting processor, reducing the resource requirement to 21 qubits and a few hundred gates.

The results were compelling (Figure 3). Our self-consistent mean-field approach outperforms the baseline of solving subproblems with independent QAOAs at identifying quality configurations for the drug-protein binding. This highlights the critical role of the environment: by capturing the removed correlations between the separated pieces, it allows us to reconstruct superior global solutions that are impossible to find by looking at the subproblems alone. Similar to the Sherrington-Kirkpatrick benchmarks, our simulations show that the self-consistent mean-field achieves performance very close to the standard QAOA on the full problem at a fraction of the required resources. While the performance on real quantum hardware was lower than in simulations — an expected result due to inherent hardware noise — the clear advantage over independent solvers remains.

Solving this 252-variable instance represents a step toward practical quantum advantage. While previous attempts to investigate molecular docking were restricted to a few tens of variables, our results demonstrate that we can now benchmark quantum heuristics on problems of realistic complexity. By mitigating hardware size constraints through decomposition, this approach offers a pathway to develop and test quantum algorithms on industrial-scale instances that were previously inaccessible.

What’s next?

The utility of the self-consistent mean-field framework extends well beyond molecular docking. Its ability to reduce dimensionality while preserving essential correlations makes it a versatile tool for broader applications. Furthermore, it holds promise for simulating complex materials and handling large quantum machine learning models by embedding them into decomposable sub-units. As we continue to improve the scale and fidelity of quantum processors, algorithmic innovations like this will be crucial in extracting the maximum value from every qubit, accelerating the timeline to solving some of the world’s most intractable problems.

Learn more

• Read the technical paper describing Rigetti’s self-consistent mean-field quantum approximate optimization algorithm.
• Read the technical papers describing Rigetti’s previous quantum optimization algorithm development: A quantum preconditioning technique for optimization problems, a quantum-enhanced greedy solver, a quantum relax-and-round solver, novel quantum circuit syntheses, a benchmark against state-of-the-art classical methods, a qubit-efficient optimization algorithm, a technique for handling many variables, an investigation of quantum entanglement in optimization circuits, and an investigation on the classical emulability hardness of quantum optimization circuits.
• Read our previous quantum optimization blog post showcasing our quantum preconditioning algorithm applied to a grid energy problem.

This work was partly supported by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Superconducting Quantum Materials and Systems Center (SQMS), under Contract №89243024CSC000002. Learn more about the SQMS Center!

Breaking the size barrier in quantum optimization was originally published in Rigetti on Medium, where people are continuing the conversation by highlighting and responding to this story.

How to get a QEC-ready, 99.9% fidelity two-qubit gate by pulsing only the tunable coupler

By Stefano Poletto, Director, Quantum Engineering

Over the years, Rigetti QPUs have supported two families of native two-qubit entangling gates: iSWAP-type gates and controlled-phase gates, most commonly the CZ gate (a controlled-phase gate where the conditional phase accumulated by the |11⟩ state is exactly π).

Both are widely used in superconducting quantum computing, and either choice enables universal quantum computation when combined with arbitrary single-qubit rotations. However, deciding which native two-qubit gate to use matters: it affects how efficiently quantum programs compile, how many two-qubit gates are needed for a given algorithm, and ultimately what performance is achievable on hardware.

Summary (TLDR)

Rigetti QPUs initially used iSWAP-type entangling gates for their strong expressivity in NISQ circuits. In our latest deployed QPU, Cepheus-1–36Q, we switched to CZ gates because they compile more naturally for parity-check circuits (a key ingredient for error correction) and can be implemented with a high-fidelity, hardware-efficient adiabatic pulse using only the tunable coupler. We have demonstrated fast (<30 ns), high fidelity (>99.9%) operation on prototype devices.

Why the native two-qubit gate matters

In principle, any two-qubit entangling gate plus single-qubit control can generate any unitary operation. In practice, the native entangling gate determines:

• how compact compiled circuits can be,
• how much calibration effort is required, and
• how robust the gate is to device variations and noise sources.

For these reasons, choosing between iSWAP and CZ is not only about reaching high two-qubit fidelity but it also affects usability, compilation overhead, and scalability.

iSWAP gates on early Ankaa-class processors

Rigetti’s fourth-generation Ankaa-class QPUs were initially introduced with iSWAP and √iSWAP as native entangling gates.

This choice was motivated in part by the expressivity of √iSWAP type interactions for NISQ-era applications. Like iSWAP, three √iSWAP gates with one-qubit rotations are able to generate the full space of two-qubit unitaries. However, unlike the iSWAP, √iSWAP gates with single-qubit rotations are also sufficient to synthesize a surprisingly large fraction of two-qubit gates. This property makes √iSWAP a highly economical entangling gate, not only can it often express target operations with less applications, but the gate is typically twice the speed of the iSWAP due to the smaller rotation angle.

In practice, real hardware implementations may introduce a small additional conditional phase during an iSWAP-like gate. While a nonzero conditional phase means the native gate is no longer exactly an ideal iSWAP (and the set of two-qubit unitaries reachable with a fixed gate count is reduced), that reduction is gradual: for small phases, the set of representable two-qubit unitaries remains large. This tolerance to small conditional-phase errors was one of the key motivations for choosing iSWAP and √iSWAP as native entangling gates on earlier Ankaa-class devices.

Why we switched to CZ on the latest Cepheus QPUs

In the latest version of our Cepheus processor, we moved from iSWAP-type entangling gates to a controlled-phase gate with fixed phase π: the CZ gate.

CZ is also an entangling two-qubit gate, and together with single-qubit operations it is fully universal. But compared to iSWAP, CZ is often the more natural native primitive for several important algorithmic patterns, especially those involving parity checks.

A concrete example is multi-qubit parity encoding into an auxiliary qubit, a building block used in many quantum error detection and correction schemes. Parity encoding is typically described using CNOT gates. Each CNOT can be implemented with:

• one CZ gate (plus single-qubit rotations), or
• two iSWAP gates (plus single-qubit rotations).

As a result, CZ is often the more direct and efficient choice for circuits dominated by parity-check structure.

Additionally, within the control system, phase-swap gates require information sharing across processors in order to transfer phase information. For programs with dynamic control flow, this sharing must occur at runtime. This exacts a quadratic cost in time and memory, effectively limiting the width of such programs to O(10) qubits on current systems. CZ gates do not require such information sharing and thus face no such scaling limitation.

This becomes even more important when looking beyond near-term NISQ demonstrations toward universal quantum computation enabled by quantum error correction. Surface-code-style protocols rely on repeated measurement of parity operators (for example, weight-four parity checks on qubits arranged in a 2D lattice), where CZ-based decompositions are standard. In that long-term vision, moving to CZ as a native entangling gate is a natural step toward fault-tolerant architectures.

Experimental advantages of CZ: resonant vs adiabatic implementations

Beyond algorithmic convenience, CZ gates offer important experimental benefits, especially in a tunable-coupler architecture like Cepheus.

One advantage is that CZ can be implemented in multiple ways, including resonant CZ, and adiabatic CZ. Both produce the same logical operation (a π conditional phase on |11⟩), but they differ in how that phase is generated physically.

1) Resonant CZ (state oscillation in the two-excitation manifold)

A resonant CZ can be implemented by bringing the |11⟩ state close to resonance with a non-computational two-excitation state such as |20⟩ or |02⟩. In this picture, the system evolves coherently between the states:

|11⟩ ↔ |20⟩ (or |02⟩)

After a full oscillation, the population returns to |11⟩, but the state acquires an additional phase of π, producing the desired CZ operation.

In a tunable-coupler architecture, this is typically implemented by flux biasing the coupler (to increase interaction strength) and also flux-tuning one of the qubits to reach the required resonance condition. Because this approach moves at least one qubit away from its flux sweet spot during the gate, it places tighter constraints on frequency trajectories and detuning management. Detuning a qubit away from its sweet spot can also reduce its coherence time, which is an additional constraint when engineering the system Hamiltonian and targeting high-fidelity operation alongside stable single-qubit performance and simultaneous gates.

2) Adiabatic CZ (phase accumulation with qubits left unperturbed)

The second approach, and the one we emphasize in our latest Cepheus QPU, is adiabatic CZ using only tunable-coupler modulation, while leaving both qubits at their sweet spot during the gate.

This has become one of the most widely used gates on our Cepheus processors because of its combination of high fidelity and simple implementation.

The adiabatic CZ relies on dynamical phase accumulation arising from level repulsion and hybridization in the two-excitation manifold. By adiabatically tuning the coupler frequency, the energy of the |11⟩ state shifts relative to its idle value (where qubit–qubit interaction is minimized). The time integral of this energy shift produces a controllable conditional phase: a CZ corresponds to engineering the pulse so that the total conditional phase is exactly π.

This implementation is attractive not only because it performs well, but because it is simple and scalable. The CZ interaction is generated by modulating a single flux pulse on the tunable coupler, while keeping both qubits at (or very near) their operating points. In other words, we do not need to detune the qubits away from their sweet spots to create entanglement.

Keeping the qubits fixed during the entangling operation has two important consequences. First, it reduces sensitivity to flux noise and helps preserve single-qubit coherence during the gate. Second, it opens the door to using fixed-frequency qubits, since the gate does not rely on frequency-tuning the qubits into resonance. Fixed-frequency qubits can offer practical advantages such as fewer control lines, simpler wiring, and in many cases higher coherence, all of which translate directly into better performance and a clearer path to larger QPU sizes.

There are a few key messages worth highlighting:

• One control knob: the gate is driven by a single coupler-flux waveform.
• Qubits stay at their sweet spots: no qubit frequency excursion is required during the entangling operation.
• Detuning-tolerant: the interaction does not require a finely tuned qubit–qubit resonance condition.
• Fast phase accumulation: strong coupler-mediated interactions can enable large conditional phase rates (potentially hundreds of MHz), allowing fast entangling gates in principle.
• Easier calibration and stability: fewer parameters typically mean easier optimization and better long-term repeatability.

Finally, because the adiabatic CZ does not depend on a narrowly tuned resonance condition, it is naturally more tolerant of fabrication-driven frequency variation. While there are still preferred detuning regimes to minimize leakage and support high-fidelity simultaneous operations, the same general calibration procedure can be applied across a wide range of qubit frequencies, making the approach robust across devices.

High performance obtained on R&D devices

In parallel with deploying adiabatic CZ gates on production systems, we have continued to refine the architecture on dedicated R&D devices, focusing on increasing both fidelity and speed. These prototypes leverage the favorable energy structure of symmetric tunable-coupler designs and carefully engineered flux waveforms to ensure maximal adiabatic evolution while maximizing conditional phase accumulation.

In our latest proprietary implementation, we have fixed-frequency transmon qubits connected by a symmetric floating tunable coupler. In the “straddling” regime, where the qubit–qubit detuning is smaller than the qubit anharmonicity, it is possible to engineer an effective ZZ = 0 interaction at idle, minimizing unwanted static coupling. At the same time, the energy distribution in the two-excitation manifold enables strong dynamical phase accumulation during the gate, together with improved robustness against non-adiabatic transitions.

On a two-qubit prototyping platform, we recently demonstrated a 24 ns two-qubit gate (with 2 ns buffers on each side) with a fidelity above 99.9%, measured using interleaved randomized benchmarking. The gate is activated by flux pulsing only the tunable coupler as described above. To our knowledge, this result places our implementation among the fastest reported adiabatic CZ gates for transmon-based superconducting qubits, based on publicly available literature.

Additional technical details will be presented in a forthcoming research publication.

Conclusion

Rigetti’s proprietary adiabatic CZ gates are a scalable, performant, and forward-looking choice for entangling operations. They lay the foundation for efficient quantum error correction, and, by virtue of only requiring pulses on tunable couplers, preserve qubit performance better than traditional resonant approaches.

These gates are already in use on current and future Cepheus systems. Even more excitingly, improvements on prototype devices show a clear path to world-class speed and fidelity.

Rigetti’s New Proprietary Adiabatic CZ Entangling Gate for High-Fidelity QPUs and Quantum Error… was originally published in Rigetti on Medium, where people are continuing the conversation by highlighting and responding to this story.

This summer, we welcomed our largest intern cohort yet — 18 students eager to explore the emerging world of quantum technologies and ready to contribute to something meaningful. During their time at Rigetti, that spark of curiosity transformed into conviction: conviction in their skills, in their career paths, and in the power of collaboration to move technology — and humanity — forward.

At Rigetti, we’re building the world’s most powerful computers to help solve humanity’s most important and pressing problems. But we’re also building something else: a community of thinkers and doers who will shape the next generation of quantum computing.

Growing the Quantum Community

For many of our interns, their time at Rigetti offered a first real glimpse into the world of full-stack quantum computing — where hardware, software, and applications all intersect. Interns experienced a wide range of opportunities — from R&D and software development to hands-on hardware work and cross-functional collaboration — developing practical skills that bridge theory and application. These experiences helped transform curiosity into conviction, as interns discovered where their interests create impact and how their unique strengths shape the future of quantum computing.

At Rigetti, we’re a small but mighty team — publicly traded, yet operating with the agility and energy of a startup. That means interns have unique access to people and projects across the company, seeing firsthand how each team’s work contributes to advancing quantum computing.

For Bora Basyildiz (California Institute of Technology), creativity and independence stood out: “I’ve enjoyed the level of freedom I’ve had here. Rigetti gives me the support I need, but I’m able to express my own creativity — and I really appreciate that.”

For Matthew Rich (Wesleyan University), the summer bridged theory and application: “With a background in computer science and physics but no prior quantum experience, it was inspiring to be surrounded by physicists while applying my software development skills.”

And as Davis Rens (UC Berkeley) reflected, “Mentoring conversations shaped my next steps. I leave with sharper judgment, better technical habits, and a stronger sense of how to contribute at scale.” These opportunities to connect, collaborate, and explore are how curiosity begins to take shape as conviction.

Workforce Development in Action

At Rigetti, interns don’t just observe — they contribute. Each project challenges them to turn classroom learning into solutions that make a real impact on our roadmap.

“What surprised me most was how fast everything moves here — it’s a mix of early startup and Bay Area spirit,” said San Dinh (Northwestern University).

That fast-paced, hands-on environment gave students a rare perspective on how innovation happens. Matthew Rich shared, “I loved collaborating with different teams. The culture was hardworking yet welcoming, and I had a fantastic summer with the other interns.”

For Aarav Bedi (UC Berkeley), teamwork and creativity defined the experience: “This experience not only strengthened my technical skills but also gave me a deeper appreciation for teamwork in cutting-edge R&D.”

He added, “The creative environment encouraged me to expand on my interests and work on a variety of projects… I feel like I am a better engineer coming out of it.”

Mentorship That Builds Futures

Every intern project begins with a Rigetti mentor who proposes a real-world challenge tied to our roadmap. Mentorship here isn’t just about teaching — it’s about growing together. Mentors gain leadership and project management experience, while interns learn to navigate complex problems with both guidance and independence.

Prarthana Sanghani reflected on the experience with honesty: “Frankly, mentorship is tricky! It was a good experience to have a short time to help develop someone’s skills. I learned that in some situations it’s best to have specific criteria — and in others, flexibility is key.”

For Angela Chen, mentorship was an opportunity to refine her leadership skills: “I gained more experience in project management under time constraints — learning when to de-prioritize tasks, how to select which to elevate, and how to tailor the project to my intern’s strengths so we’d have a meaningful deliverable by the end.”

Prasad Sarangapani found that guiding an intern sharpened his approach to teamwork: “Guiding an intern required me to think carefully about how to break down abstract problems into well-defined steps, which will influence how I lead and support teammates in the future.”

And James O’Donoghue captured the balance between teaching and autonomy: “I’m starting to understand some common areas where juniors get stuck and how best to nudge them in the right direction without outright giving them the solution.”

Together, these reflections capture the essence of mentorship at Rigetti: an exchange of ideas and insights that strengthens both sides. This mutual investment — of time, knowledge, and trust — helps interns and mentors alike grow in ways that extend far beyond a single project.

Advancing the Quantum Roadmap

Rigetti’s internship program isn’t theoretical — it’s hands on and high impact. Interns contribute directly to the systems, tools, and technologies that power our full-stack quantum computing solutions.

“Within two weeks I got to work on the actual live compiler that’s used to run Rigetti hardware,” said Jessica Jeng (Northwestern University).

Sreyashi Mondal (University of Michigan) developed tools to improve engineering efficiency: “I built an application to run routines to save time for quantum engineers. I created the framework, prototyped, and tested it on live fridges.”

Others worked on the hardware itself. Aarav Bedi noted, “I had the chance to work on CAD modeling, vacuum routing, and cryostat design to support quantum hardware development.”

The sense of ownership was universal. Davis Rens shared, “The team trusted me with real problems, offered clear reviews, and gave me room to own the work.” And Kenneth Phan summed it up simply: “Helping users directly is fun — it makes you feel part of the team.”

Connecting Across the Stack

Though many projects were hybrid or remote, interns still found meaningful ways to build community and connect their work across disciplines. The program offered regular opportunities for connection — every other week, interns gathered to share updates on their projects, exchange ideas, and explore how their individual contributions fit into the larger quantum stack. These sessions encouraged curiosity and cross-disciplinary learning, helping interns “connect the dots” across Rigetti’s hardware, software, and applications.

For Kaustubh Simha (UC Irvine), who worked with the Control Systems team, collaboration extended beyond his own projects. “Interning at Rigetti was a great experience, as I had the opportunity to gain hands-on experience in quantum computing through several projects, one of which involved measuring qubits in one of Rigetti’s fridges. It was a great environment where I felt comfortable interacting with everyone — even people from different teams.”

Anurag Ramesh (Purdue University) echoed that sense of connection, noting that “while remote collaboration was smooth, the highlight was visiting Rigetti’s offices. I got to see how hardware, software, and applications integrate to create real business impact.”

That theme — bridging distance and disciplines through collaboration — resonated across the cohort. As Davis Rens reflected, “I learned how world-class engineers think, how ideas become experiments, and how steady collaboration turns into progress.”

To our 2025 interns: thank you. Your curiosity inspired us, your work advanced our mission, and your conviction reminds us what’s possible when learning meets purpose.

Interested in joining our 2026 internship cohort? Upload your resume here and we’ll be in touch once our internship positions are available!

Curiosity & Collaboration Create Impact: Meet Rigetti’s 2025 Interns was originally published in Rigetti on Medium, where people are continuing the conversation by highlighting and responding to this story.

Enabling hands-on access to quantum computers is imperative for fostering the quantum computing workforce, quantum computing education, and increasing adoption of quantum computing technologies. Since introducing the Novera™ QPU, our 9-qubit QPU compatible with existing cryogenics and controls systems, and the Novera QPU Partner program, we’ve had opportunities to test and develop integrated quantum computing solutions with our partners for researchers across academia and government.

We recently had the pleasure of hosting QuantrolOx, the developer of Quantum EDGE software for automated bring-up, characterization, and tuning of quantum devices, and Qblox, a leading provider of scalable and modular quantum control stacks, to demonstrate two-qubit bring-up and characterization on a Novera. Keep reading for a closer look at how the integration test worked!

Setting Up the QPU

For the integration, we connected a Qblox Cluster control system to a Novera QPU. QuantrolOx operated the Cluster with Quantum EDGE to automate the chip bring-up.

Our goal was to characterize 6 qubits across 2 different readout lines on the 9-qubit chip. Once the Qblox Cluster was connected to the Novera, Quantum EDGE was launched on a control PC to send instructions to the Cluster. The hardware setup was quite seamless, with the modular Cluster configurable to the number of readout lines, flux lines, and drive lines required to operate qubits, as well as tunable couplers. The software provided a neat, easy-to-understand user interface (UI), and the Novera device topology and design parameters were pre-loaded into Quantum EDGE, greatly streamlining device characterization.

Measuring the QPU

We began our integration test focusing on 3 qubits on the rightmost readout line, which contained qubits 2, 5, and 8 (QB2, QB5, QB8) and tunable couplers 13 and 18 (C13, C18). The device topology is depicted below:

Quantum EDGE allows the building of automation workflows that can be customized based on device components and required measurements. The workflows typically include initial bring-up, single-qubit tuning, and qubit pair tuning. The workflows we used are shown as they appear in the Quantum EDGE UI:

During the bring-up part of the workflow, the software performed a broadband resonator spectroscopy and found the readout resonators’ frequencies, their operating points via resonator power spectroscopy, followed by pairing qubits with resonators. Finally, we performed qubit power spectroscopy to identify qubit frequencies and anharmonicities. The initial bring-up was remarkably smooth and fast, lasting just below 3 minutes for 3 qubits on one readout line.

For each of the qubits we proceeded to measure the qubit’s individual characteristics using the single qubit tuning workflow. To perform T₁ and T₂ echo coherence times measurements, Quantum EDGE first measures the Rabi pulse amplitude, retunes the qubit frequency via Ramsey measurement, and performs several pulse corrections before optimizing the readout fidelity.

The coherence times measured using the Qblox Cluster and Quantum EDGE were comparable to the values measured on a Novera using Rigetti proprietary control electronics and software, underscoring the efficacy of the QPU’s modularity and ability to maintain performance in various system configurations, as well as highlighting the performance of our partners’ technology. Example plots showing T₁ and T₂ echo measurement data for qubit 8 are below:

The final measurement of the single qubit tuning workflow is single-qubit randomized benchmarking, the results of which are shown in the example for qubit 8:

Once the qubits on the readout line were characterized, we proceeded to the qubit pair tuning part of the workflow, focusing initially on qubits 5 and 8. This involved operation of tunable coupler 18, namely finding the control pulse parameters to achieve zero coupling and maximum coupling. Once the coupler operating points were determined, we ran a CZ gate between qubits 5 and 8. The qubit spectroscopy and coupler flux pulse parameters for this interaction are shown below:

While randomized benchmarking will be performed in a follow-up experiment, the sharpness and regularity of the interaction plots indicates that this is likely a high-fidelity gate. It should be noted that the design of the qubits and couplers on the chip was optimized for iSWAP gates; the ease of implementing a CZ gate with outstanding results is a reflection of the flexibility of the QPU, the quality of the control electronics, and the precision of the measurement software.

Quantum EDGE provides a clear visual of the QPU topology populated with the measurement results, which is quite useful for tracking device metrics and getting an at-a-glance overview of qubit performance:

The high-quality signals and fidelities achieved were a testament to the integrated system’s hardware and software reliability. QuantrolOx EDGE and Qblox Cluster worked flawlessly together, simplifying our workflow significantly. Adding to the ease of use, Quantum EDGE initial configuration and measurements were performed completely remotely by Anton Vladyka, QuantrolOx Application Software Engineer. In addition to the measurement results, the speed of going from an uncharacterized chip to measuring two-qubit gates was quite impressive — everything was done in less than a day!

The Impact of an Integrated System

“The QuantrolOx software brought up the qubits in a matter of minutes. It was easy to tweak and tailor it to the Novera chip as needed. After that — things ran very smoothly and for the next readout line no tweaking was necessary,” says Ella Lachman, Senior Quantum Materials R&D Engineer at Rigetti. “I think the integration between the Qblox hardware and the QuantrolOx software made a whole that’s larger than the sum of its parts, making bring-up all the way to two-qubit gates fast and seemingly effortless (of course a lot of effort went into making it like this!).”

“Rigetti’s Novera, a high quality and well packaged QPU has been a delight to integrate with QuantrolOx’s Quantum EDGE software. Using Quantum EDGE’s easy installation, intuitive UI and record breaking speed, the combined package is the perfect platform for accelerating quantum research across academia and government,” says Vishal Chatrath, CEO of QuantrolOx

“As a leading manufacturer of scalable control stacks Qblox is on a mission to deliver industrial-scale quantum computing power. Our customers are often limited by the quality and size of quantum chips available on the market or by what they can develop internally. We deem the launch of the Novera chips therefore as a game changer as we can now with Rigetti offer an out-of-the-box and seamlessly integrated package of chip and control.” says Niels Bultink CEO of Qblox

“Working with the Rigetti team and the Novera QPU was an outstanding experience — the system simply worked from the start, showcasing the exceptional quality of the chip. This reinforces how seamlessly Quantum EDGE and the Qblox cluster integrate with Rigetti’s Novera QPU. We initially planned for a week-long measurement campaign, but due to the Novera QPU’s high quality, we were able to complete everything — from bring-up to single-qubit operations and CPHASE two-qubit gates — in less than a day,” says Jelena Trbovic, VP of Business Development at QuantrolOx.

From Our Lab to Yours

Are you ready to start your quantum journey? We’re excited to be working with our Novera QPU Partner Program members on bringing value to the quantum computing community with TreQ’s Compass SG25B integrated quantum computing system. Learn more about our turnkey and open architecture solution here.

Automated and Streamlined Novera QPU Bring-Up with Qblox and QuantrolOx was originally published in Rigetti on Medium, where people are continuing the conversation by highlighting and responding to this story.

By Rebecca Malamud, Senior Marketing & Communications Manager, and Yuvraj Mohan, Lead Quantum Technology Program Manager

Hands-on access to quantum computers is a key part of quantum workforce development, educating the next generation of quantum researchers, and advancing quantum technology R&D. As more national labs and academic institutions establish on-premises quantum computing capabilities through testbeds and data centers, we aim to offer flexible, high-performance quantum computing solutions with our 9-qubit Novera QPU combined with our partners’ compatible technology. The Novera QPU Partner Program provides excellent opportunities to integrate our QPU with our partners’ technology to demonstrate its capabilities and performance.

We recently hosted Zurich Instruments — a founding member of the Novera QPU Partner Program — at our quantum foundry, Fab-1, to integrate their Quantum Computing Control System with a Novera for qubit bring-up and readout. Keep reading for a deeper dive into how the two technologies performed together, and the benefits of a flexible, modular system architecture.

Measurement Results

A typical measurement procedure for superconducting qubits starts with finding the frequencies of the readout resonators and qubits — guided by the design specifications of the chip — as well as the optimal readout power. Identifying these device parameters and operating points sets the stage for qubit characterization, which includes coherence measurements, readout fidelities, and running one- and two-qubit gates.

Once the initial system bring-up of resonator discovery and qubit spectroscopy was complete, we proceeded to measure qubit coherence, namely T₁ and T₂ echo. The values of T₁ = 45.9 µs and T₂ echo = 25.5 µs are comparable to what we measure on Novera devices using in-house Rigetti-built control electronics and software. Example plots of these measurement results on one of the qubits under test (qubit 3) are shown below:

Following qubit coherence measurements, we proceeded to measuring readout fidelity and single-qubit gates. In preparing these measurement scripts, the capabilities of Zurich Instruments’ control system were quite evident — we were able to customize the qubit control pulses and data collection parameters to fine-tune the QPU operation. This level of “under-the-hood” access and flexibility is a valuable tool for power users — such as quantum engineers — and for researchers experimenting with microwave pulse shaping and qubit control schemes.

The readout assignment matrix for qubit 3 is shown below — the measured fidelity value of 97.96% is on par with typical readout fidelities measured internally. Also shown below is a plot of randomized benchmarking results for single-qubit gates also run on qubit 3, with a T₁-limited gate fidelity of 99.51% comparable to the values measured on Novera devices using Rigetti proprietary control electronics and software. These results highlight not only the flexibility of the Novera QPU and ability to maintain performance in various system configurations, but also the precision of Zurich Instruments’ control electronics and software in identifying optimal qubit operating points and readout parameters.

After characterizing several single qubits on the 9-qubit device, we began two-qubit gate bring-up between qubits 3 and 6, using the tunable coupler between them to modulate the gate. An aspect of the control software that stood out during preparation of these measurements was how intuitive it was to write custom gate operations, including various types of two-qubit gates. A preview of the two-qubit measurement results are shown below — stay tuned for randomized benchmarking and gate fidelity results!

The Engineers’ Perspectives

“What I love most about working with the Novera QPU is that it ‘just works’. Every experimentalist knows the anxiety that comes with cooling down a sample — the fear that you’ll need to spend multiple days thermal-cycling your system because some component isn’t working as expected. With the Novera QPU, all of that stress disappears. I knew that I would be able to walk into the lab on day one, hook up the electronics and start measuring. Zurich Instruments specializes in world-class, plug-and-play control electronics, and I’d say the Novera QPU is the equivalent in the measurement-platform realm, says Linsey Rodenbach, Zurich Instruments Applications Scientist. “With demonstrations like this, we can see the tangible benefits of the Novera QPU Partner Program. It is transformative for the quantum ecosystem because it provides every researcher with a level of autonomy typically reserved for measurement specialists in large industry settings. You can focus solely on your research, with the confidence that everything — from the sample to the control electronics and software — will function flawlessly.”

“In less than two days, we went from an uncharacterized cold chip to measuring single-qubit gate fidelity and starting two-qubit gate bring-up — which is no small feat. This demonstration underscores the value of the ecosystem created by the Novera QPU Partner Program. Between the flexibility afforded by the Novera’s modular design and our collaborative relationship with the Zurich Instruments team, we have a great example of how the Novera can be operated with our partners’ control systems,” says Yuvraj Mohan, Rigetti’s Lead Quantum Technology Program Manager.

“The Zurich Instruments control electronics and the software controlling it have many advantages. First is the broad and deep control over experimental parameters without being cumbersome or complicated. The code is very nicely structured and can be mastered quickly. Additionally, you’re able to define experiments as ‘tasks’ and then combine multiple tasks into a more complex experiment with ease. If you are doing any sort of complex measurement on qubits you know how valuable that is. Linsey also showed me the simulator option, which allows you to plan the pulse shapes and timing without the need for a cold system. Zurich Instruments worked hard to ensure that the pulses you see on your screen are exactly what you’ll be sending when working with the cold system. That’s an important tool to be able to build and debug experiments,” says Ella Lachman, Rigetti Senior Quantum Materials R&D Engineer

What’s Next for Rigetti and Zurich Instruments?

We plan on continuing our work with Zurich Instruments to further demonstrate the performance and capabilities of an integrated Novera-Zurich Instruments system, and measuring two-qubit gate randomized benchmarking on a chip with more precisely tuned qubits and tunable couplers using Rigetti’s ABAA process. ABAA improves the frequency targeting precision and addressability of the qubits, and their interactions with readout resonators and other qubits, creating an optimal environment for qubit bring-up.

We look forward to more demonstrations of the Novera QPU Partner Program in action!

Are you ready to get your hands-on quantum? Learn more about Rigetti’s 9-qubit Novera QPU and how it can enable your quantum computing R&D today: https://www.rigetti.com/novera.

Flexible, Fast and Precise Qubit Bring-up on a Novera™ QPU Leveraging Zurich Instruments’ Control… was originally published in Rigetti on Medium, where people are continuing the conversation by highlighting and responding to this story.

Last year, the quantum-tech community started a new tradition at the APS March Meeting. Resonance was more than just a networking event — it was an intimate community experience. Picture this: an arcade-filled venue, a buzzing crowd of 500+ attendees, and an atmosphere electric with excitement. The event proved that when the brightest minds in quantum tech meet informally, the resulting connections are extraordinary, and it fosters energized networking in an industry that thrives on collaboration.

With the APS Global Physics Summit 2025 just around the corner, the stage is set for Resonance II. If 2024 was the launch, 2025 is where the real momentum builds. This year, we’re amplifying everything — more co-hosts, more engagement, and even stronger resonance.

What does resonance mean?

Resonance, in physics, describes the transfer of energy, where particles vibrate together and strengthen one another. That’s exactly what our event embodies. Resonance, organized by 18 co-hosts including Rigetti, brings the quantum community together during the APS March Meeting in an informal setting. A social gathering of leading experts in quantum tech, where ideas resonate, collaborations form, and industry advancements take shape.

The success of Resonance 2024

Reflecting on Resonance in 2024, we were thrilled by the response from the quantum tech community. Resonance was attended by more than 500 guests, co-hosted by 16 companies, at a leading arcade game bar in downtown Minneapolis. The venue provided the community with arcade games and an after-hours DJ session by a well-known experimental quantum physicist. Check out the photos and videos from last year’s event here: https://www.flickr.com/photos/qresonance/

What to expect at Resonance II

With the tremendous success of our first gathering, we knew we had to return with even more resonance. This year, 18 co-hosts are coming together to make Resonance II an unmissable event.

The 18 Resonance II co-hosts are bringing the community back for another unforgettable evening. Consider this scenario: an evening where the beats of a DJ match the pulse of conversations, arcade games spark spontaneous debates on qubits, and every clink of glass signals new partnerships being formed. It’s about being in tune.

Get ready to resonate

Are you already looking forward to Resonance II? So are we! We can’t wait to host you on March 19, 2025, at a stunning 1920s theater-turned-event venue! This exclusive, invitation-only event will span two floors and include plenty of food and drinks. Be sure to watch your email for an invite from us or our co-hosts. In another case, express your interest in attending by contacting us at [link to contact form]. Get ready for a resonating evening!

Resonance II isn’t just about attendance — it’s about being part of the great quantum community. Whether you’re returning or joining for the first time, expect an evening of connection and unforgettable moments — an evening of quantum resonance. We look forward to seeing you there!

Who co-hosts Resonance?

About Bluefors

Bluefors is the world-leading manufacturer of cutting-edge cryogenic measurement systems, cryocoolers, and other cryogenic product lines for quantum technology and fundamental physics research.

https://bluefors.com/

About Delft Circuits

Delft Circuits is a Dutch hardware company, which designs and manufactures the cryogenic microwave cabling Cri/oFlex® for various applications, including quantum computation. Cri/oFlex® technology allows the scalability of i/o solutions by creating conducting circuits on flexible substrates.

https://delft-circuits.com/

About Diraq

Diraq is a global leader in the development of quantum processors based on cutting-edge silicon ‘quantum dot’ technology. Diraq’s innovative technology harnesses existing silicon manufacturing processes used by semiconductor foundries (CMOS), offering a faster, more cost-effective path to market.

https://diraq.com/

About Kiutra

Kiutra is a pioneering cryogenics company. We want to turn cooling from a bottleneck into a key enabler for quantum science and technology. We do this by providing simplified and fast cooling solutions as well as services at ultra-low temperatures.

https://kiutra.com/

About Maybell Quantum

Maybell is building the infrastructure for a quantum future. We engineer reliable, scalable, and user-friendly quantum hardware.

https://www.maybellquantum.com/

About OrangeQS

OrangeQS delivers test solutions for better quantum chips, with the turnkey OrangeQS MAX system for utility-scale quantum chip testing and the OrangeQS FLEX with building blocks for a qubit R&D setup.

https://orangeqs.com/

About Oxford Instruments

Oxford Instruments’ Proteox dilution refrigerators provide scalable quantum computing platforms from research to scale-up to commercialization, complemented by etch, deposition, and analytical tools.

https://www.oxinst.com/

About Q-CTRL

Q-CTRL is a category-defining business accelerating the pathway to useful quantum computing and quantum sensing for defense and industry. The company’s quantum infrastructure software delivers unmatched commercial success for its global customers and partners.

https://q-ctrl.com/

About QDNL

Quantum Delta NL serves as the driving force behind the Dutch quantum ecosystem, connecting leading companies, research institutions, and societal organizations in quantum technology. As the implementing body of the Dutch National Agenda for Quantum Technology, it plays a pivotal role in shaping the country’s quantum future.

https://quantumdelta.nl/

About Qblox

Qblox provides scalable and modular qubit control stacks, supporting academic and industrial labs worldwide. Qblox Cluster control stack integrates key technologies for qubit control and readout and supports a wide variety of qubit types.

https://www.qblox.com/

About Qilimanjaro

Qilimanjaro, based in Barcelona, develops analog quantum computation with superconducting flux qubits to deliver a faster quantum advantage. We offer QASIC development, access via cloud to our QaaS, and consultancy.

https://www.qilimanjaro.tech/

About Qruise

Qruise develops machine learning software to accelerate the development of novel devices, from quantum computers to MRI, silicon photonics, and beyond. By creating highly accurate digital twins, our software delivers deep and rapid insights that facilitate enhanced device performance.

https://www.qruise.com/

About QuantWare

QuantWare is the world’s leading provider of superconducting QPU’s. With our VIO technology, we empower quantum players worldwide to build and rapidly scale best-in-class quantum computers-bringing useful quantum computing closer to reality.

https://www.quantware.com/

About Quantum Motion

Quantum Motion is developing scalable fault-tolerant quantum computing architectures that are compatible with CMOS processes. Our outstanding interdisciplinary team of over 100 specialists is leading the next revolution in silicon.

https://quantummotion.tech/

About QuantrolOx

QuantrolOx is leading the development of Quantum EDGE, a software solution that automates the tune-up of qubits and quantum processors. Through automation, we aim to drive significant advancements in the field of quantum computing.

https://quantrolox.com/

About Rigetti

Rigetti delivers superconducting quantum computing systems over the cloud and on-premises. Rigetti designs and manufactures its chips in-house at Fab-1, the industry’s first dedicated and integrated quantum device manufacturing facility.

https://www.rigetti.com/

About Riverlane

Riverlane is building Deltaflow — the Quantum Error Correction Stack to comprehensively correct the millions of data errors that prevent today’s quantum computers from achieving useful scale.

https://www.riverlane.com/

About SCALINQ

SCALINQ develops cryogenic electronics to push the performance and scalability of superconducting & spin-based qubits. They offer plug-and-research packaging, filtering, TWPAs, and wiring solutions.

https://www.scalinq.com/

By Maxime Dupont, Senior Quantum Researcher

Optimization is a part of daily life for both individuals and businesses. For example, a company may seek to optimize its delivery routes to minimize fuel costs and time. In finance, an investor might aim to optimize their portfolio to maximize returns while reducing risk. Engineers may need to monitor and optimize the energy delivery capabilities of an electrical grid to homes and industries.

Solving optimization problems requires substantial computational power. Quantum computers leverage quantum mechanics to perform operations fundamentally differently than classical computers, driving researchers to investigate whether quantum algorithms may improve and accelerate optimization processes. However, demonstrating that current quantum computers, with their limited number of qubits and imperfect fidelities, may provide any advantage over classical computers remains a challenge. Rigetti is at the forefront of this effort through consistent progress in our hardware and software. We are also developing novel quantum algorithms that are more performant and efficient, require fewer qubits, and can tolerate imperfect fidelities to shorten the path to quantum advantage in optimization.

Today, we introduce quantum preconditioning, our latest quantum optimization algorithm, which has the potential to deliver an advantage in the near term.

Optimization via Quantum Preconditioning

Although best-in-class classical optimization solvers are computationally demanding, they remain tough competitors for quantum solutions to deliver utility. At Rigetti, we’ve been exploring using quantum computers to enhance the performance of existing classical solving methods, thus improving upon an already high-performing baseline. Quantum preconditioning fits such a framework. We show it can boost the performance of state-of-the-art classical solvers such as simulated annealing and the Burer-Monteiro solver.

The idea of preconditioning in a classical setting isn’t new. For instance, it is instrumental in the performance of classical linear system solvers used everyday in science and engineering. Preconditioning seeks to transform a problem into a more suitable form that simplifies its solution or manipulation, enabling a faster convergence. Here, we introduce a quantum approach to precondition complex optimization problems before they are fed to traditional optimization solvers (see Figure 1). The output of quantum preconditioning can simply be substituted for the original input, allowing for a seamless integration with many existing optimization pipelines.

Quantum preconditioning employs parametric quantum circuits, like the well-known quantum approximate optimization algorithm (QAOA). While the QAOA is a popular algorithm for tackling optimization problems, its requirements to possibly challenge classical optimizers are out of reach for today’s quantum computers. Quantum preconditioning overcomes some QAOA limitations for competing on the same level as best-in-class classical solvers on current hardware.

The main step of quantum preconditioning is to compute a quantum correlation matrix from the output of the quantum circuit. This quantum correlation matrix is then used as the input to standard classical optimizers. The level of preconditioning is controlled by the depth 𝑝 of the circuit, with the preconditioned problem becoming easier as 𝑝 increases. Although higher 𝑝 values are theoretically better, they necessitate larger quantum circuits that may be unfeasible with current quantum computers.

In our work, we demonstrate that best-in-class classical solvers can converge significantly faster when given quantum preconditioned input for various problems, even for small 𝑝 values. We test the algorithm on standard mathematical benchmarks such as Sherrington-Kirkpatrick spin glasses and random 3-regular graph maximum-cut problems. On the latter class of problems with 4,096 variables, we show that simulated annealing and Burer-Monteiro can converge up to at least 9x and 432x faster to a solution with 99.9% accuracy when working with the quantum preconditioned problem in place of the original one (see Figure 2).

Enhancing Power Grid Optimization

Having demonstrated the potential for advantage on mathematical problems, we investigate the benefit of quantum preconditioning on a power grid energy problem representative of what is encountered by engineers in the real world, using a public dataset representing South Carolina’s energy grid. The problem seeks to compute the maximum power exchange section, a metric that informs on the health and the power delivery capability of the energy network.

The quantum preconditioning step is performed on Rigetti’s latest 84-qubit Ankaa–3 quantum computer. The dataset involves optimizing over 410 variables. We use the classical Burer-Monteiro solver, which is 10x more efficient than simulated annealing on this problem, and therefore, the method of choice. We find that working with the preconditioned problem provides an advantage over the original one (see Figure 3). For instance, through quantum preconditioning, Burer-Monteiro returns a solution with 99.95% accuracy in 4x less iterations. Although Burer-Monteiro working with the original problem is able to reach a much higher solution accuracy given enough iterations, optimization solvers operate on a run-time versus accuracy basis, and real-world use cases may constrain the available run-time.

Here, despite a low preconditioning level (𝑝=1) and imperfect fidelities, quantum preconditioning can provide a relative advantage against the classical baseline and delivers a high solution accuracy. This highlights the potential for quantum preconditioning to achieve quantum utility for solving practical optimization problems.

What’s Next?

It is important to note that the preconditioning step itself takes time and that an advantage should be assessed accounting for this. For a fixed solution accuracy, is the full pipeline of the quantum preconditioning algorithm getting there faster than the classical solver alone (see Figure 4)? Our results highlight the faster convergence of the classical optimizers only after the quantum computer has preconditioned the problem. As such, they define preconditioning time budgets for quantum preconditioning to deliver an advantage.

Superconducting quantum platforms, such as the Rigetti Ankaa-3 system, are particularly well-positioned, thanks to their fast clock rate enabling quantum circuit executions in the microseconds range. Moving forward, tracking, improving, and optimizing the preconditioning time will be key factors for establishing quantum preconditioning as a candidate for quantum advantage in optimization.

Learn More

• Read the technical paper describing Rigetti’s latest quantum preconditioning algorithm.
• Read the press release introducing Rigetti’s 84-qubit Ankaa–3 quantum computer.
• Read the technical papers describing Rigetti’s previous quantum optimization algorithm development: A quantum-enhanced greedy solver, a quantum relax-and-round solver, novel quantum circuit syntheses, a benchmark against state-of-the-art classical methods, a qubit-efficient optimization algorithm, a technique for handling many variables, an investigation of quantum entanglement in optimization circuits, and an investigation on the classical emulability hardness of quantum optimization circuits.

This work was supported by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Superconducting Quantum Materials and Systems Center (SQMS) under Contract No. DEAC02–07CH11359. Learn more about the SQMS Center!

New Quantum Algorithm Boosts Classical Optimizers was originally published in Rigetti on Medium, where people are continuing the conversation by highlighting and responding to this story.

Looking back on the summer, we’re still beaming from the energy and talent that interns brought to Rigetti as part of our 2024 Summer Internship Program. We received nearly 400 applications and ultimately selected 9 students to work on projects across our technology stack.

Rigetti is on a mission to build the world’s most powerful computers to help solve humanity’s most important and pressing problems, and we can’t do that without looking ahead to the talent of the future. From working on quantum algorithms for optimization to cryogenic hardware development, our interns gained hands-on experience with our quantum computing systems and collaborated with teams across multiple scientific and engineering disciplines. As this year’s program closes, we asked students to share highlights of their time with us and a glimpse of life as a Rigetti intern!

“Seeing firsthand the entire development process from chip design to fabrication to testing really put into perspective the monumental challenge Rigetti faces head-on: to advance humanity forward toward the next era of computing.” (Shuhul Mujoo, California Institute of Technology)

“…highlights from the summer have been getting to know my 2024 intern cohort, touring the Fab at the Fremont office, and deepening my understanding of quantum computing under the guidance of my mentor! As someone with no prior background in quantum computing, this internship exposed me to the field and piqued my interest.” (Shivani Varma, University of California, Davis)

The internship program had an even larger impact as it gave employee mentors the opportunity to grow their skills as they carefully crafted projects for our interns.

“Rigetti invests a great deal of thought and effort into designing research projects for its interns, ensuring that each one aligns with our skills and the project timeline. My mentor structured my project in a way that took me from having no prior knowledge to making meaningful contributions to the research — something that can be challenging to achieve in just a few months.” (Tina Oberoi, University of Chicago)

The praise from the interns speaks volumes about the dedication and passion of the team at Rigetti. We truly appreciate how our mentors went above and beyond in their work, and demonstrated a high level of engagement as they guided this year’s intern cohort through the complex space of quantum computing.

“I was able to extensively learn about the intricacies of chip design and simulation and get hands-on experience with Rigetti’s chip infrastructure. I feel so grateful to have collaborated so closely with some of the most intelligent, humble, and passionate scientists in pursuit of Rigetti’s mission!” (Tanmai Pathak, Amherst College)

We wish all of our summer interns a wonderful rest of the school year, and thank them for joining us on our mission.

Interested in interning at Rigetti? Stay tuned for our next internship program, and email talent@rigetti if you have any questions about careers at Rigetti. View our latest job openings at rigetti.com/careers.

Tomorrow’s Quantum Experts: A Glimpse into Rigetti’s 2024 Summer Internship Program was originally published in Rigetti on Medium, where people are continuing the conversation by highlighting and responding to this story.

Rigetti is laser-focused on achieving narrow quantum advantage, the point at which a quantum computer is able to solve a practical, operationally relevant problem significantly better, faster, or cheaper than classic alternatives.

We believe that the finance sector has problems that could benefit from the unique properties of quantum computing. This is why we are delighted to announce the signing of a collaboration agreement with Moody’s Analytics — one of the world’s leading financial intelligence and analytics companies — to develop quantum machine learning methods for the financial sector.

In April 2023, we shared the initial results of our novel approach to forecasting recessions using cutting edge machine learning techniques, developed in collaboration with Moody’s and Imperial College London. Our methods combine classical signature kernels with quantum-enhanced data transformations. Performing noiseless quantum simulation, we showed that signature kernels can leverage quantum-enhanced data and forecast recessions more accurately than industry standard baseline models.

In the first half of 2024, building on our initial findings, we plan to use higher dimensional datasets from variegated and interdependent macroeconomic areas, leveraging a higher qubit count. This work, differently from the simulations of the initial collaboration, will be executed on Rigetti’s 84-qubit Ankaa™ system using error mitigation methods.

“Moody’s is investing in quantum computing to better understand how it can provide an advantage over classical solutions. By partnering with the algorithm and application teams at Rigetti, and leveraging their QPUs, we get access to the expertise needed to make meaningful progress in our quantum computing strategy,” says Sergio Gago, MD of AI and Quantum Computing at Moody’s Analytics.

“We need to work hand in hand with industry experts who will be relying on hardware vendors like Rigetti to provide quantum computers capable of tackling their complex computational problems. Partnering with Moody’s to use quantum machine learning techniques to improve time series prediction gives us valuable insight into how we can continue to improve our quantum computing systems for real-world applications,” says Dr. Subodh Kulkarni, Rigetti CEO.

“The potential for quantum computing to have a transformative impact on the financial sector has led to increasingly more financial institutions building in-house teams dedicated to exploring quantum computing. Achieving quantum advantage will require collaborating with end users who truly understand how to apply quantum computing to their business. This is why our work with Moody’s is so valuable — partnering with Moody’s quantum experts allows us to work on valuable problems and in turn feed improvements back into our hardware and software stack to enhance our capabilities,” says Marco Paini, VP of Finance Solutions at Rigetti.

About Rigetti

Rigetti is a pioneer in full-stack quantum computing. The Company has operated quantum computers over the cloud since 2017 and serves global enterprise, government, and research clients through its Rigetti Quantum Cloud Services platform. The Company’s proprietary quantum-classical infrastructure provides high performance integration with public and private clouds for practical quantum computing. Rigetti has developed the industry’s first multi-chip quantum processor for scalable quantum computing systems. The Company designs and manufactures its chips in-house at Fab-1, the industry’s first dedicated and integrated quantum device manufacturing facility. Learn more at rigetti.com.

About Moody’s Analytics

Moody’s Analytics provides financial intelligence and analytical tools to help business leaders make better, faster decisions. Our deep risk expertise, expansive information resources, and innovative application of technology help our clients confidently navigate an evolving marketplace. We are known for our industry-leading and award-winning solutions, made up of research, data, software, and professional services, assembled to deliver a seamless customer experience. We create confidence in thousands of organizations worldwide, with our commitment to excellence, open mindset approach, and focus on meeting customer needs. For more information about Moody’s Analytics, visit our website or connect with us on Twitter and LinkedIn.

Quantum-Enhanced Machine Learning with Moody’s Analytics was originally published in Rigetti on Medium, where people are continuing the conversation by highlighting and responding to this story.