QuantrolOx
Machine learning-based software for automated tuning, stabilization, and optimization of qubits across quantum hardware.
Website: https://quantrolox.com/
Cover Block
Publicly reported
| Field | Value |
|---|---|
| Name | QuantrolOx |
| Tagline | Machine learning-based software for automated tuning, stabilization, and optimization of qubits across quantum hardware. [QuantrolOx, retrieved 2026] |
| Headquarters | Espoo, Finland [Tech.eu, October 2026] |
| Founded | 2021 [Tech.eu, October 2026] |
| Stage | Series A [Tech.eu, October 2026] |
| Business Model | SaaS |
| Industry | Deeptech |
| Technology | Quantum Computing |
| Geography | Global / Remote-First |
| Growth Profile | Venture Scale |
| Founding Team | Academic Spinout [Tech.eu, October 2026] |
| Funding Label | Series A |
| Total Disclosed | ~$17,800,000 [TechCrunch, February 2022] [The Quantum Insider, March 2023] [Tech.eu, October 2026] |
Links
Publicly reported
- Website: https://quantrolox.com/
- LinkedIn: https://www.linkedin.com/company/quantrolox
Summary and Signal
PUBLIC QuantrolOx builds machine learning-based software that automates qubit tuning, stabilization, and optimization, a narrow but consequential control-layer problem in quantum computing that has become more urgent as hardware teams try to scale beyond specialist-led manual workflows [Tech.eu, February 2022] [QuantrolOx, retrieved 2026] [Tech.eu, October 2026]. Founded in 2021 as an Oxford spinout and now headquartered in Espoo, Finland, the company is positioning itself as cross-architecture infrastructure rather than a single-stack hardware bet, which matters in a market where tool fragmentation can limit adoption [Tech.eu, October 2026] [Crunchbase, retrieved 2026].
The product case rests on Quantum EDGE, which the company says can automate tuning and optimization for superconducting quantum systems, while external coverage describes the broader platform as applicable across multiple hardware architectures [QuantrolOx, retrieved 2026] [Tech.eu, October 2026]. The most concrete public performance signal is a January 2025 report that QuantrolOx achieved fully automated QPU characterization and two-qubit gate tune-up in under 25 minutes with Qblox and QuantWare QPUs, although broader claims about replacing weeklong workflows still rely mainly on founder commentary and should be read with that limitation in mind [The Quantum Insider, January 2025] [podcast.yboger.com, February 2026].
The founding bench is stronger on technical credibility than most software-first infrastructure startups at this stage. CEO Vishal Chatrath is described as a former Nokia executive and has prior startup leadership experience at Prowler.io and Secondmind, while co-founders Andrew Briggs and Natalia Ares bring Oxford quantum research credentials, and Dominic Lennon has led product work tied to the platform's early development [Tech.eu, October 2026] [TechCrunch, September 2017] [Business Insider, November 2020] [The Oxford Trust, retrieved 2026] [LinkedIn, retrieved 2026].
On capital formation, the public record supports a seed round in February 2022, another seed round in March 2023, and a Series A announced in October 2026, with disclosed backers including Nielsen Ventures, Hoxton Ventures, Voima Ventures, Remus Capital, IW Capital, the European Innovation Council Fund, and Transpose Capital [Tech.eu, February 2022] [The Quantum Insider, March 2023] [Tech.eu, October 2026].
Over the next 12 to 18 months, the points to watch are straightforward: whether customer wins continue beyond the eight reported signings discussed by the CEO for 2025, whether cross-architecture deployment turns into repeatable production usage, and whether headcount growth remains disciplined relative to commercial progress [The Quantum Insider, March 2026] [Revelio Labs, retrieved 2026]. If those indicators firm up, QuantrolOx could occupy a useful control-software layer in quantum infrastructure, but the present public case still rests more on technical promise and team quality than on independently verified scale [Tech.eu, October 2026] [The Quantum Insider, March 2026].
One source, partially checked -- This section relies on named publisher coverage, Crunchbase, LinkedIn, and company materials, with several product and traction claims still only partially corroborated.
Taxonomy Snapshot
| Axis | Value |
|---|---|
| Stage | Series A |
| Business Model | SaaS |
| Industry / Vertical | Deeptech |
| Technology Type | Quantum Computing |
| Geography | Global / Remote-First |
| Growth Profile | Venture Scale |
| Founding Team | Academic Spinout |
| Funding | Total disclosed approximately $17,800,000 |
Company Overview
PUBLIC
QuantrolOx traces back to Oxford, but the clearest public picture is of a company that has since anchored itself in Finland while keeping the original academic spinout identity intact. Crunchbase lists QuantrolOx as founded in 2021 and based in Espoo, Finland, while the company website describes it as building machine learning based software for quantum control with the goal of maximizing quantum computer uptime [Crunchbase] [QuantrolOx].
The public chronology is still fairly compact. Crunchbase records the company’s founding in 2021, and the company’s website now presents a broader product surface that includes Quantum EDGE, Quantum Edge Academy, and VIDYAQAR, which suggests an expansion from a narrow control-software pitch toward a wider platform and training posture over time [Crunchbase] [QuantrolOx]. The available website materials do not identify a legal entity name on the captured homepage, so the public record here rests mainly on company-owned web materials and the Crunchbase profile rather than filings [QuantrolOx] [Crunchbase].
No independent source found -- Confirmed by Crunchbase and the company website, but key details in this section rely on company-owned materials and the captured homepage does not verify the legal entity name.
The Product and the Stack
Public record plus analysis
QuantrolOx is selling automation software into one of quantum hardware’s least scalable bottlenecks: the manual tuning and stabilization of qubits. Public descriptions are consistent on the core claim. The company says it builds machine learning based software for quantum control with the goal of maximizing quantum computer uptime [QuantrolOx, retrieved 2026], while Tech.eu describes the product more specifically as software that automates the tuning, stabilization, and optimization of physical qubits [Tech.eu, February 2022]. By October 2026, external coverage also characterized the platform as working across multiple quantum hardware architectures rather than one processor type [Tech.eu, October 2026].
The named product surface is broader than a single software module, but the evidence is uneven across components. On its website, QuantrolOx presents Quantum EDGE and VIDYAQAR among its products [QuantrolOx, retrieved 2026]. The company describes Quantum EDGE as software that automates tuning and optimization for superconducting quantum computers and says users can create automations with a few lines of Python that scale to larger hardware systems [QuantrolOx, retrieved 2026]. A more independently sourced description comes from Quantum Computing Report, which characterizes VIDYAQAR as an open-architecture quantum system intended for hardware experimentation, component benchmarking, and workforce development [Quantum Computing Report, retrieved 2026]. That matters because it suggests QuantrolOx is not only offering control software, but also packaging that software into a fuller development environment for labs and hardware teams, although the company’s own site remains the main source for the exact scope of that bundle [QuantrolOx, retrieved 2026].
The strongest public proof point is speed in a verified hardware context, not broad commercial scale. The Quantum Insider reported in January 2025 that QuantrolOx achieved fully automated QPU characterization and two-qubit gate tune-up in under 25 minutes using Qblox and QuantWare QPUs [The Quantum Insider, January 2025]. A later interview attributed to the CEO said one qubit characterization workflow had been reduced to roughly 25 minutes from processes that could take a week, but that figure is still closer to management testimony than independent benchmarking [podcast.yboger.com, February 2026]. Taken together, the product appears technically credible as an automation layer for calibration-heavy quantum workflows, with the remaining diligence question being how portable that performance is across architectures, customer environments, and production-scale qubit counts [Tech.eu, October 2026].
One source, partially checked -- Core product scope is corroborated by QuantrolOx and Tech.eu; some named product details remain company-sourced, while the under-25-minute automation result has independent trade-press coverage.
The Market They Are Entering
Publicly reported This market matters now because quantum hardware is moving from lab-grade experimentation toward repeatable operation, and that shift raises the value of software that can reduce manual calibration work and keep fragile systems running with fewer scarce specialists [Yahoo Finance] [Tech.eu, October 2026].
The available public record does not support a clean TAM, SAM, and SOM build for QuantrolOx itself. No named third-party market report in the sourced materials gives a quantified market size for quantum-control software, qubit-automation software, or calibration software as a standalone category. What the sources do establish is narrower and still useful: QuantrolOx is selling into a bottleneck in quantum hardware development, namely the need to tune, stabilize, and optimize physical qubits across different hardware architectures, at a time when the company argues specialist talent remains scarce [Tech.eu, February 2022] [Yahoo Finance]. That points to a market defined less by end-user quantum applications today and more by the installed and growing base of quantum hardware developers, research labs, and system builders that need faster characterization and control workflows [Tech.eu, October 2026] [The Quantum Insider, March 2026].
The demand signal in the public sources is operational rather than financial. In January 2025, The Quantum Insider reported that QuantrolOx achieved fully automated QPU characterization and two-qubit gate tune-up in under 25 minutes with Qblox and QuantWare QPUs, which is relevant because it ties the product to a concrete time-to-calibration outcome rather than a general promise about AI in quantum computing [The Quantum Insider, January 2025]. Separately, CEO Vishal Chatrath said in a 2026 interview that the company signed two customers in the first nine months of 2025 and six more in the fourth quarter, a modest but notable data point if treated carefully because the source did not name those customers or disclose contract values [The Quantum Insider, March 2026]. The broader tailwind is straightforward: as qubit counts and hardware complexity rise, manual tuning becomes harder to scale, so software that compresses experiment and characterization cycles should become easier to justify inside hardware programs [Tech.eu, February 2022] [Yahoo Finance].
Adjacent markets matter here because QuantrolOx is not competing only inside a pure software category. The product record spans quantum-control software, workflow automation, and, through VIDYAQAR, a broader open-architecture quantum system for hardware experimentation and workforce development, according to company and sector coverage [QuantrolOx, retrieved 2026] [Quantum Computing Report]. That creates exposure to substitute approaches as well, including in-house calibration tooling built by quantum hardware vendors, control-stack providers, and integrated quantum computing companies that may prefer vertically owned software over third-party orchestration [Tech.eu, October 2026]. In practice, the nearer-term budget line may sit inside R&D tooling, lab automation, and quantum control infrastructure rather than a mature enterprise software spend category.
Macro and policy forces are supportive, but the support is still indirect. The company is an Oxford spinout with investors that include the European Innovation Council Fund, and the Oxford Trust profile cited in the source set says it also received a grant from the National Quantum Computing Centre, which suggests public-sector interest in enabling quantum infrastructure and commercialization rather than only backing end applications [Tech.eu, October 2026] [The Oxford Trust]. The main constraint is timing: quantum computing remains an emerging infrastructure market, so demand can expand without becoming broadly budgeted or standardized in the near term. That leaves vendors like QuantrolOx dependent on the pace of hardware progress, research funding, and the willingness of labs and manufacturers to externalize a function many still treat as core internal know-how [The Quantum Insider, March 2026] [Tech.eu, February 2022].
| Market lens | Public evidence | What it suggests |
|---|---|---|
| Core problem | Shortage of specialists who can manually tune quantum computers [Yahoo Finance] | Labor scarcity can support automation spend before the end market is fully mature |
| Buyer environment | Hardware developers and researchers need characterization, calibration, and operation workflows across architectures [Tech.eu, October 2026] [Tech.eu, February 2022] | Early demand is likely concentrated in quantum infrastructure rather than broad enterprise IT |
| Proof of urgency | Automated QPU characterization and 2-qubit gate tune-up reported in under 25 minutes with named QPUs [The Quantum Insider, January 2025] | Time savings are the most credible wedge visible in public sources |
| Commercial readiness | Eight customer signings reported across 2025, without names or contract values [The Quantum Insider, March 2026] | There is some evidence of market pull, but revenue depth cannot be inferred |
The table shows a market that is easier to describe through bottlenecks than through conventional sizing. For now, the strongest public case is that QuantrolOx sits inside the picks-and-shovels layer of quantum computing, where operational pain may arrive earlier than large-scale revenue pools.
One source, partially checked -- This section relies on named public sources, but key market-shape claims are supported by a limited number of sector articles and company-adjacent reporting rather than independent third-party market studies.
The Competitive Field
Positioning
MIXED QuantrolOx is positioned less as a full-stack quantum computer vendor and more as a control-software layer that sits near the calibration bottleneck, which puts it in a different lane from hardware-first public companies such as Rigetti and IonQ, even when all three touch quantum system performance [Tech.eu, February 2022] [Tech.eu, October 2026] [Crunchbase, retrieved 2026].
| Company | Positioning | Stage / Funding | Notable Differentiator | Source |
|---|---|---|---|---|
| QuantrolOx | Machine learning-based software for automated tuning, stabilization, and optimization of qubits across quantum hardware | Series A, about $17.8M disclosed | Cross-architecture control software focused on reducing specialist manual tuning work | [Tech.eu, February 2022] [Tech.eu, October 2026] [Crunchbase, retrieved 2026] |
| Rigetti | Quantum computing company building superconducting quantum hardware and related systems | Public company profile listed on Crunchbase | Hardware ownership in superconducting qubits, which can embed tooling at the stack level | [Crunchbase, retrieved 2026] |
| IonQ | Quantum computing company commercializing trapped-ion quantum systems | Public company profile listed on Crunchbase | Distinct trapped-ion architecture and direct control over its own hardware roadmap | [Crunchbase, retrieved 2026] |
The competitive map breaks into three layers. First are hardware incumbents and scaled challengers, represented here by Rigetti and IonQ, that control the processor roadmap and can decide whether calibration, characterization, and tune-up software is a core internal capability or a purchased layer [Crunchbase, retrieved 2026]. Second are specialist control and infrastructure vendors, where QuantrolOx appears to play, selling automation into a narrow but consequential problem set around qubit tuning and uptime rather than selling compute access itself [Tech.eu, February 2022] [QuantrolOx, retrieved 2026]. Third are adjacent substitutes, namely internal research teams at labs, quantum startups, and hardware manufacturers that still rely on scarce experts and custom scripts instead of buying external automation, which is the pain point QuantrolOx itself has identified [Yahoo Finance] [Tech.eu, February 2022].
The edge today is fairly clear, but it is not unassailable. QuantrolOx's public case rests on two linked claims: it automates tuning and control, and it is designed to work across multiple quantum-hardware architectures rather than being tied to one processor family [Tech.eu, February 2022] [Tech.eu, October 2026]. If that cross-architecture claim holds in production settings, the company could matter to buyers that do not want to rebuild calibration workflows every time a hardware stack changes. The talent base also looks relevant here. The founding bench combines Oxford quantum researchers with a repeat venture-backed operator in Vishal Chatrath, which is a credible mix for selling specialist software into a research-heavy market [Tech.eu, October 2026] [The Oxford Trust] [TechCrunch, September 2017] [Business Insider, November 2020]. That said, durability depends on whether the company is accumulating proprietary workflow data and sticky integrations, neither of which is well documented in the public record reviewed here. Without those, the advantage is perishable and could narrow if hardware vendors internalize similar automation.
The main exposure is structural. Rigetti and IonQ each own their hardware stack, which gives them a distribution channel QuantrolOx does not own and a tighter feedback loop between device behavior and control tooling [Crunchbase, retrieved 2026]. A hardware vendor can choose to bundle calibration software, prioritize features for its installed base, and treat automation as part of system performance rather than as a separately purchased product. QuantrolOx is also exposed to the possibility that some categories of buyers, especially the most advanced hardware developers, prefer to keep control software in-house because it sits close to core intellectual property. Public evidence of customer traction exists, with management stating it signed two customers in the first nine months of 2025 and six in the fourth quarter of 2025, but those customers are unnamed, which makes it difficult to judge whether the company has already broken through that trust barrier at top-tier accounts [The Quantum Insider, March 2026].
The most plausible 18-month scenario is a sorting by where control software becomes strategic rather than merely helpful. Winner if X: QuantrolOx, if cross-platform automation proves meaningfully faster than internal workflows across more than one hardware architecture and converts that proof into repeatable commercial deployments in the US and Europe [Tech.eu, October 2026] [The Quantum Insider, March 2026]. Loser if Y: QuantrolOx, if hardware vendors such as Rigetti or IonQ keep bringing calibration and tune-up software closer to the hardware stack and buyers decide the performance risk of using an external layer outweighs the productivity gain [Crunchbase, retrieved 2026]. In that scenario, independent software remains relevant mainly in research, training, and open-system environments such as the company's VIDYAQAR positioning, rather than becoming a standard control layer across commercial quantum hardware [Quantum Computing Report, retrieved 2026] [QuantrolOx, retrieved 2026].
One source, partially checked -- Core company positioning is corroborated by Tech.eu and Crunchbase, but competitor detail is limited in the provided public source set and several material differentiation claims remain company-described or only partially corroborated.
Opportunity
PUBLIC
The prize here is not another point tool for quantum labs, but a credible shot at becoming the control software layer that hardware teams rely on as qubit counts rise faster than specialist staffing can keep up [Tech.eu, October 2026] [Yahoo Finance].
The headline opportunity is straightforward: if QuantrolOx becomes the default automation layer for qubit tuning, stabilization, and optimization across more than one hardware architecture, it could sit in a high-value position between expensive quantum hardware and the scarce experts needed to operate it [Tech.eu, February 2022] [Tech.eu, October 2026]. That outcome is reachable, not merely aspirational, because the public record already shows three ingredients that matter. First, the company is selling into an operational bottleneck, namely specialist-intensive calibration and characterization workflows that do not scale cleanly with hardware complexity [Yahoo Finance] [Tech.eu, February 2022]. Second, it has at least one public performance claim tied to named hardware counterparties, with fully automated QPU characterization and two-qubit gate tune-up reported in under 25 minutes with Qblox and QuantWare QPUs [The Quantum Insider, January 2025]. Third, investors have continued to fund the company from seed through a €12 million Series A, with cross-border backers including IW Capital, the European Innovation Council Fund, and Transpose Capital, which suggests the commercialization story has passed more than one diligence gate [Tech.eu, October 2026] [Tech.eu, February 2022] [The Quantum Insider, March 2023].
A reasonable way to think about the upside is through a few distinct paths rather than one monolithic outcome.
| Scenario | What happens | Catalyst | Why it's plausible |
|---|---|---|---|
| Cross-architecture control standard | QuantrolOx becomes the preferred software layer for automating calibration and tune-up across several quantum hardware types, making it costly for customers to rebuild those workflows internally | The platform's cross-architecture positioning converts into repeatable deployments beyond a single hardware stack [Tech.eu, October 2026] | The company has publicly described the platform as working across multiple quantum-hardware architectures, which is the key condition for standard-layer economics rather than bespoke tooling revenue [Tech.eu, October 2026] |
| Embedded workflow inside quantum OEM stacks | Hardware vendors bundle QuantrolOx software into their own system bring-up and operating workflows, letting the company scale through partner distribution rather than direct sales alone | Demonstrated automation on Qblox and QuantWare QPUs helps turn technical validation into OEM-style integration discussions [The Quantum Insider, January 2025] | Public evidence already points to operation with named ecosystem hardware, and the buyer pain is operational enough that vendors have reason to shorten deployment and calibration cycles [The Quantum Insider, January 2025] [Yahoo Finance] |
| Training and platform wedge into institutional labs | The broader VIDYAQAR and Quantum EDGE Academy surfaces become an entry point for universities, national labs, and workforce programs, creating earlier customer relationships that can graduate into production software sales | Adoption of open-architecture systems for experimentation and workforce development widens top-of-funnel access [Quantum Computing Report] [QuantrolOx] | Public descriptions of VIDYAQAR as an open-architecture quantum system and complete platform suggest management is not only selling narrow software seats, but also trying to shape how users learn and experiment on advanced hardware [Quantum Computing Report] [QuantrolOx] |
The compounding logic is stronger than it first appears. If QuantrolOx can automate more calibration steps on more hardware environments, every successful deployment should create three reinforcing benefits: more operational data on tuning workflows, deeper embedding into customer processes, and a clearer implementation playbook for the next account [Tech.eu, October 2026] [Tech.eu, February 2022]. In practical terms, this looks less like a consumer network effect and more like infrastructure compounding. The company says users can build automations with a few lines of Python and scale them to larger hardware environments, which, if borne out in practice, would lower switching appetite once workflows are written and trusted [QuantrolOx]. Public traction is still early, but management's statement that the company signed two customers in the first nine months of 2025 and six in the fourth quarter points to some evidence that the sales motion may improve as references and product maturity accumulate [The Quantum Insider, March 2026].
The size of the win depends on which role in the stack QuantrolOx ultimately owns. A useful public marker is IonQ, a listed quantum computing company with a far larger scope than QuantrolOx, but one that shows how capital markets can value core infrastructure in this category when investors believe the control layer and system software matter [IonQ]. A more grounded framing is this: if QuantrolOx were to become a category-standard control software provider used across hardware developers, OEMs, and research institutions, the business could support platform-style software economics rather than project revenue. On that path, a multi-hundred-million-euro outcome is plausible, and a unicorn-scale valuation is conceivable if the company proves cross-architecture adoption and durable embedment into customer operations (scenario, not a forecast) [Tech.eu, October 2026] [The Quantum Insider, January 2025]. The gap between those two endpoints will turn on whether the company remains a specialist workflow vendor or becomes part of the operating system for quantum hardware development.
One source, partially checked -- Section relies on a mix of independent publisher reporting and company descriptions; key upside premises are supported publicly, but customer breadth and commercial depth remain only partially corroborated.
Sources
Publicly reported
[QuantrolOx, retrieved 2026] Homepage | QuantrolOx | https://quantrolox.com/
[Tech.eu, October 2026] Anglo-Finnish Oxford University quantum spinout QuantrolOx raises €12M | https://tech.eu/2026/10/22/anglo-finnish-oxford-university-quantum-spinout-raises-eur12m/
[TechCrunch, February 2022] QuantrolOx uses machine learning to control qubits | https://techcrunch.com/2022/02/23/quantrolox-uses-machine-learning-to-control-qubits/?tpcc=tcplustwitter
[The Quantum Insider, March 2023] Oxford Spinout QuantrolOx Raises New Round to Bring Qubit Tuning Software to Market | https://thequantuminsider.com/2023/03/07/oxford-spinout-quantrolox-raises-new-round-to-bring-qubit-tuning-software-to-market/
[Crunchbase, retrieved 2026] QuantrolOx - Crunchbase Company Profile & Funding | https://www.crunchbase.com/organization/quantrol-ox
[Tech.eu, February 2022] Oxford spinoff slays off £1.4 million to accelerate development of scalable quantum computing | https://tech.eu/2022/02/24/oxford-spinoff-slays-off-ps14-million-to-accelerate-development-of-scalable-quantum-computing/
[The Quantum Insider, January 2025] QuantrolOx achieves fully automated QPU characterization and 2-qubit gate tune-up in under 25 minutes with Qblox and QuantWare QPUs | https://thequantuminsider.com/2025/01/16/quantrolox-achieves-fully-automated-qpu-characterization-and-2-qubit-gate-tune-up-in-under-25-minutes-with-qblox-and-quantware-qpus/
[podcast.yboger.com, February 2026] Vishal Chatrath, CEO and Co-founder, QuantrolOx | https://podcast.yboger.com/2026/02/02/vishal-chatrath-ceo-and-co-founder-quantrolox/
[TechCrunch, September 2017] Prowler.io nabs $13M for its new approach to decision making in AI | TechCrunch | https://techcrunch.com/2017/09/04/prowler-io-nabs-13m-for-its-new-approach-to-decision-making-in-ai/
[Business Insider, November 2020] Secondmind raises $16M to put AI in the driver's seat for autonomous vehicle design | https://www.businessinsider.com/secondmind-raises-16m-to-design-self-driving-cars-with-ai-2020-11
[The Oxford Trust, retrieved 2026] Five Minutes With Professor Andrew Briggs, co-founder of QuantrolOx | https://theoxfordtrust.co.uk/5_minutes_with/five-minutes-with-professor-andrew-briggs-co-founder-of-quantrolox/
[LinkedIn, retrieved 2026] QuantrolOx | LinkedIn | https://www.linkedin.com/company/quantrolox
[Revelio Labs, retrieved 2026] QuantrolOx headcount data | https://www.reveliolabs.com/
[Quantum Computing Report, retrieved 2026] QuantrolOx launches VIDYAQAR open-architecture quantum system | https://quantumcomputingreport.com/
[Yahoo Finance, retrieved 2026] Oxford quantum computing spinout QuantrolOx raises €12m | https://uk.finance.yahoo.com/news/oxford-quantum-computing-spinout-quantrolox-092700649.html
[The Quantum Insider, March 2026] Podcast with Vishal Chatrath, CEO and co-founder, QuantrolOx | https://thequantuminsider.com/2026/03/14/vishal-chatrath-ceo-and-co-founder-quantrolox-2/
Articles about QuantrolOx
- QuantrolOx Cuts Qubit Tuning from Weeks to 25 Minutes — The Finnish startup's machine learning software aims to replace specialist-intensive calibration, a bottleneck for scaling quantum processors.